| PyTorch Cheat Sheet |
| ****************************** |
|
|
| Imports |
| ========= |
|
|
| General |
| ------- |
|
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| .. code-block:: python |
|
|
| import torch # root package |
| from torch.utils.data import Dataset, Dataloader # dataset representation and loading |
|
|
| Neural Network API |
| ------------------ |
|
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| .. code-block:: python |
|
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| import torch.autograd as autograd # computation graph |
| from torch import Tensor # tensor node in the computation graph |
| import torch.nn as nn # neural networks |
| import torch.nn.functional as F # layers, activations and more |
| import torch.optim as optim # optimizers e.g. gradient descent, ADAM, etc. |
| from torch.jit import script, trace # hybrid frontend decorator and tracing jit |
|
|
| See `autograd <https://pytorch.org/docs/stable/autograd.html>`__, |
| `nn <https://pytorch.org/docs/stable/nn.html>`__, |
| `functional <https://pytorch.org/docs/stable/nn.html#torch-nn-functional>`__ |
| and `optim <https://pytorch.org/docs/stable/optim.html>`__ |
|
|
| Torchscript and JIT |
| --------------- |
|
|
| .. code-block:: python |
|
|
| torch.jit.trace() # takes your module or function and an example |
| # data input, and traces the computational steps |
| # that the data encounters as it progresses through the model |
|
|
| @script # decorator used to indicate data-dependent |
| # control flow within the code being traced |
|
|
| See `Torchscript <https://pytorch.org/docs/stable/jit.html>`__ |
|
|
| ONNX |
| ---- |
|
|
| .. code-block:: python |
|
|
| torch.onnx.export(model, dummy data, xxxx.proto) # exports an ONNX formatted |
| # model using a trained model, dummy |
| # data and the desired file name |
|
|
| model = onnx.load("alexnet.proto") # load an ONNX model |
| onnx.checker.check_model(model) # check that the model |
| # IR is well formed |
| |
| onnx.helper.printable_graph(model.graph) # print a human readable |
| # representation of the graph |
|
|
| See `onnx <https://pytorch.org/docs/stable/onnx.html>`__ |
|
|
| Vision |
| ------ |
|
|
| .. code-block:: python |
|
|
| from torchvision import datasets, models, transforms # vision datasets, |
| # architectures & |
| # transforms |
|
|
| import torchvision.transforms as transforms # composable transforms |
|
|
| See |
| `torchvision <https://pytorch.org/docs/stable/torchvision/index.html>`__ |
|
|
| Distributed Training |
| -------------------- |
|
|
| .. code-block:: python |
|
|
| import torch.distributed as dist # distributed communication |
| from multiprocessing import Process # memory sharing processes |
|
|
| See `distributed <https://pytorch.org/docs/stable/distributed.html>`__ |
| and |
| `multiprocessing <https://pytorch.org/docs/stable/multiprocessing.html>`__ |
|
|
| Tensors |
| ========= |
|
|
| Creation |
| -------- |
|
|
| .. code-block:: python |
|
|
| torch.randn(*size) # tensor with independent N(0,1) entries |
| torch.[ones|zeros](*size) # tensor with all 1's [or 0's] |
| torch.Tensor(L) # create tensor from [nested] list or ndarray L |
| x.clone() # clone of x |
| with torch.no_grad(): # code wrap that stops autograd from tracking tensor history |
| requires_grad=True # arg, when set to True, tracks computation |
| # history for future derivative calculations |
|
|
| See `tensor <https://pytorch.org/docs/stable/tensors.html>`__ |
|
|
| Dimensionality |
| -------------- |
|
|
| .. code-block:: python |
|
|
| x.size() # return tuple-like object of dimensions |
| torch.cat(tensor_seq, dim=0) # concatenates tensors along dim |
| x.view(a,b,...) # reshapes x into size (a,b,...) |
| x.view(-1,a) # reshapes x into size (b,a) for some b |
| x.transpose(a,b) # swaps dimensions a and b |
| x.permute(*dims) # permutes dimensions |
| x.unsqueeze(dim) # tensor with added axis |
| x.unsqueeze(dim=2) # (a,b,c) tensor -> (a,b,1,c) tensor |
|
|
| See `tensor <https://pytorch.org/docs/stable/tensors.html>`__ |
|
|
| Algebra |
| ------- |
|
|
|
|
| .. code-block:: python |
|
|
| A.mm(B) # matrix multiplication |
| A.mv(x) # matrix-vector multiplication |
| x.t() # matrix transpose |
|
|
| See `math |
| operations <https://pytorch.org/docs/stable/torch.html?highlight=mm#math-operations>`__ |
|
|
| GPU Usage |
| --------- |
|
|
| .. code-block:: python |
|
|
| torch.cuda.is_available # check for cuda |
| x.cuda() # move x's data from |
| # CPU to GPU and return new object |
|
|
| x.cpu() # move x's data from GPU to CPU |
| # and return new object |
|
|
| if not args.disable_cuda and torch.cuda.is_available(): # device agnostic code |
| args.device = torch.device('cuda') # and modularity |
| else: # |
| args.device = torch.device('cpu') # |
|
|
| net.to(device) # recursively convert their |
| # parameters and buffers to |
| # device specific tensors |
|
|
| mytensor.to(device) # copy your tensors to a device |
| # (gpu, cpu) |
|
|
| See `cuda <https://pytorch.org/docs/stable/cuda.html>`__ |
|
|
| Deep Learning |
| ============= |
|
|
| .. code-block:: python |
|
|
| nn.Linear(m,n) # fully connected layer from |
| # m to n units |
|
|
| nn.ConvXd(m,n,s) # X dimensional conv layer from |
| # m to n channels where X⍷{1,2,3} |
| # and the kernel size is s |
|
|
| nn.MaxPoolXd(s) # X dimension pooling layer |
| # (notation as above) |
|
|
| nn.BatchNorm # batch norm layer |
| nn.RNN/LSTM/GRU # recurrent layers |
| nn.Dropout(p=0.5, inplace=False) # dropout layer for any dimensional input |
| nn.Dropout2d(p=0.5, inplace=False) # 2-dimensional channel-wise dropout |
| nn.Embedding(num_embeddings, embedding_dim) # (tensor-wise) mapping from |
| # indices to embedding vectors |
|
|
| See `nn <https://pytorch.org/docs/stable/nn.html>`__ |
|
|
| Loss Functions |
| -------------- |
|
|
| .. code-block:: python |
|
|
| nn.X # where X is BCELoss, CrossEntropyLoss, |
| # L1Loss, MSELoss, NLLLoss, SoftMarginLoss, |
| # MultiLabelSoftMarginLoss, CosineEmbeddingLoss, |
| # KLDivLoss, MarginRankingLoss, HingeEmbeddingLoss |
| # or CosineEmbeddingLoss |
| |
| See `loss |
| functions <https://pytorch.org/docs/stable/nn.html#loss-functions>`__ |
|
|
| Activation Functions |
| -------------------- |
|
|
| .. code-block:: python |
|
|
| nn.X # where X is ReLU, ReLU6, ELU, SELU, PReLU, LeakyReLU, |
| # Threshold, HardTanh, Sigmoid, Tanh, |
| # LogSigmoid, Softplus, SoftShrink, |
| # Softsign, TanhShrink, Softmin, Softmax, |
| # Softmax2d or LogSoftmax |
|
|
| See `activation |
| functions <https://pytorch.org/docs/stable/nn.html#non-linear-activations-weighted-sum-nonlinearity>`__ |
|
|
| Optimizers |
| ---------- |
|
|
| .. code-block:: python |
|
|
| opt = optim.x(model.parameters(), ...) # create optimizer |
| opt.step() # update weights |
| optim.X # where X is SGD, Adadelta, Adagrad, Adam, |
| # SparseAdam, Adamax, ASGD, |
| # LBFGS, RMSProp or Rprop |
|
|
| See `optimizers <https://pytorch.org/docs/stable/optim.html>`__ |
|
|
| Learning rate scheduling |
| ------------------------ |
|
|
| .. code-block:: python |
|
|
| scheduler = optim.X(optimizer,...) # create lr scheduler |
| scheduler.step() # update lr at start of epoch |
| optim.lr_scheduler.X # where X is LambdaLR, StepLR, MultiStepLR, |
| # ExponentialLR or ReduceLROnPLateau |
|
|
| See `learning rate |
| scheduler <https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate>`__ |
|
|
| Data Utilities |
| ============== |
|
|
| Datasets |
| -------- |
|
|
| .. code-block:: python |
|
|
| Dataset # abstract class representing dataset |
| TensorDataset # labelled dataset in the form of tensors |
| Concat Dataset # concatenation of Datasets |
|
|
| See |
| `datasets <https://pytorch.org/docs/stable/data.html?highlight=dataset#torch.utils.data.Dataset>`__ |
|
|
| Dataloaders and DataSamplers |
| ---------------------------- |
|
|
| .. code-block:: python |
|
|
| DataLoader(dataset, batch_size=1, ...) # loads data batches agnostic |
| # of structure of individual data points |
|
|
| sampler.Sampler(dataset,...) # abstract class dealing with |
| # ways to sample from dataset |
|
|
| sampler.XSampler where ... # Sequential, Random, Subset, |
| # WeightedRandom or Distributed |
|
|
| See |
| `dataloader <https://pytorch.org/docs/stable/data.html?highlight=dataloader#torch.utils.data.DataLoader>`__ |
|
|
| Also see |
| -------- |
|
|
| - `Deep Learning with PyTorch: A 60 Minute |
| Blitz <https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html>`__ |
| *(pytorch.org)* |
| - `PyTorch Forums <https://discuss.pytorch.org/>`__ |
| *(discuss.pytorch.org)* |
| - `PyTorch for Numpy |
| users <https://github.com/wkentaro/pytorch-for-numpy-users>`__ |
| *(github.com/wkentaro/pytorch-for-numpy-users)* |
|
|
