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README.md

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+---
+library_name: transformers
+tags: []
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]

config.json

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+{
+  "architectures": [
+    "Resnet3DScrollprizeModel"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_resnet3d.Resnet3DScrollprizeConfig",
+    "AutoModel": "modeling_resnet3d.Resnet3DScrollprizeModel"
+  },
+  "model_depth": 50,
+  "model_type": "resnetscrollprize",
+  "n_classes": 1139,
+  "num_layers": 18,
+  "torch_dtype": "float32",
+  "transformers_version": "4.46.3",
+  "window_size": 256
+}

configuration_resnet3d.py

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+from transformers import PretrainedConfig
+
+class Resnet3DScrollprizeConfig(PretrainedConfig):
+    model_type = "resnetscrollprize"
+
+    def __init__(
+        self,
+        window_size=64,
+        model_depth=50,
+        n_classes=1039,
+        num_layers=18,
+        **kwargs,
+    ):
+        self.window_size=window_size
+        self.model_depth=model_depth
+        self.n_classes=n_classes
+        self.num_layers=num_layers
+        super().__init__(**kwargs)

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:052670ea5013e1fa67b009e28d4d9d7e63e006ff4c705a43cd90f72f4f5f7b76
+size 342867728

modeling_resnet3d.py

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+from transformers import PreTrainedModel
+import torch 
+import torch.nn as nn
+from .resnetall import generate_model
+from .configuration_resnet3d import Resnet3DScrollprizeConfig
+import torch.nn.functional as F
+class Decoder(nn.Module):
+    def __init__(self, encoder_dims, upscale):
+        super().__init__()
+        self.convs = nn.ModuleList([
+            nn.Sequential(
+                nn.Conv2d(encoder_dims[i]+encoder_dims[i-1], encoder_dims[i-1], 3, 1, 1, bias=False),
+                nn.BatchNorm2d(encoder_dims[i-1]),
+                nn.ReLU(inplace=True)
+            ) for i in range(1, len(encoder_dims))])
+
+        self.logit = nn.Conv2d(encoder_dims[0], 1, 1, 1, 0)
+        self.up = nn.Upsample(scale_factor=upscale, mode="bilinear")
+
+    def forward(self, feature_maps):
+        for i in range(len(feature_maps)-1, 0, -1):
+            f_up = F.interpolate(feature_maps[i], scale_factor=2, mode="bilinear")
+            f = torch.cat([feature_maps[i-1], f_up], dim=1)
+            f_down = self.convs[i-1](f)
+            feature_maps[i-1] = f_down
+
+        x = self.logit(feature_maps[0])
+        mask = self.up(x)
+        return mask
+    
+class Resnet3DScrollprizeModel(PreTrainedModel):
+    config_class = Resnet3DScrollprizeConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.backbone= generate_model(model_depth=config.model_depth, n_input_channels=1,forward_features=True,n_classes=config.n_classes)
+        self.decoder = Decoder(encoder_dims=[x.size(1) for x in self.backbone(torch.rand(1,1,20,256,256))], upscale=1)
+    def forward(self, tensor):
+        feat_maps = self.backbone(tensor)
+        feat_maps_pooled = [torch.max(f, dim=2)[0] for f in feat_maps]
+        pred_mask = self.decoder(feat_maps_pooled)
+        return pred_mask

resnetall.py

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+import math
+from functools import partial
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+def get_inplanes():
+    return [64, 128, 256, 512]
+
+
+def conv3x3x3(in_planes, out_planes, stride=1):
+    return nn.Conv3d(in_planes,
+                     out_planes,
+                     kernel_size=3,
+                     stride=stride,
+                     padding=1,
+                     bias=False)
+
+
+def conv1x1x1(in_planes, out_planes, stride=1):
+    return nn.Conv3d(in_planes,
+                     out_planes,
+                     kernel_size=1,
+                     stride=stride,
+                     bias=False)
+
+
+class BasicBlock(nn.Module):
+    expansion = 1
+
+    def __init__(self, in_planes, planes, stride=1, downsample=None):
+        super().__init__()
+
+        self.conv1 = conv3x3x3(in_planes, planes, stride)
+        self.bn1 = nn.BatchNorm3d(planes)
+        self.relu = nn.ReLU(inplace=True)
+        self.conv2 = conv3x3x3(planes, planes)
+        self.bn2 = nn.BatchNorm3d(planes)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class Bottleneck(nn.Module):
+    expansion = 4
+
+    def __init__(self, in_planes, planes, stride=1, downsample=None):
+        super().__init__()
+
+        self.conv1 = conv1x1x1(in_planes, planes)
+        self.bn1 = nn.BatchNorm3d(planes)
+        self.conv2 = conv3x3x3(planes, planes, stride)
+        self.bn2 = nn.BatchNorm3d(planes)
+        self.conv3 = conv1x1x1(planes, planes * self.expansion)
+        self.bn3 = nn.BatchNorm3d(planes * self.expansion)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class ResNet(nn.Module):
+
+    def __init__(self,
+                 block,
+                 layers,
+                 block_inplanes,
+                 n_input_channels=3,
+                 conv1_t_size=7,
+                 conv1_t_stride=1,
+                 no_max_pool=False,
+                 shortcut_type='B',
+                 widen_factor=1.0,
+                 n_classes=400,
+                forward_features=False,
+                ):
+        super().__init__()
+        self.forward_features=forward_features
+        block_inplanes = [int(x * widen_factor) for x in block_inplanes]
+
+        self.in_planes = block_inplanes[0]
+        self.no_max_pool = no_max_pool
+
+        self.conv1 = nn.Conv3d(n_input_channels,
+                               self.in_planes,
+                               kernel_size=(conv1_t_size, 7, 7),
+                               stride=(conv1_t_stride, 2, 2),
+                               padding=(conv1_t_size // 2, 3, 3),
+                               bias=False)
+        self.bn1 = nn.BatchNorm3d(self.in_planes)
+        self.relu = nn.ReLU(inplace=True)
+        self.maxpool = nn.MaxPool3d(kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1))
+        # self.maxpool = nn.MaxPool3d(kernel_size=3, stride=2, padding=1)
+
+        self.layer1 = self._make_layer(block, block_inplanes[0], layers[0],
+                                       shortcut_type)
+        self.layer2 = self._make_layer(block,
+                                       block_inplanes[1],
+                                       layers[1],
+                                       shortcut_type,
+                                       stride=2)
+        self.layer3 = self._make_layer(block,
+                                       block_inplanes[2],
+                                       layers[2],
+                                       shortcut_type,
+                                       stride=2)
+        self.layer4 = self._make_layer(block,
+                                       block_inplanes[3],
+                                       layers[3],
+                                       shortcut_type,
+                                       stride=2)
+
+        self.avgpool = nn.AdaptiveMaxPool3d((1, 1, 1))
+        self.fc = nn.Linear(block_inplanes[3] * block.expansion, n_classes)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv3d):
+                nn.init.kaiming_normal_(m.weight,
+                                        mode='fan_out',
+                                        nonlinearity='relu')
+            elif isinstance(m, nn.BatchNorm3d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+
+    def _downsample_basic_block(self, x, planes, stride):
+        out = F.avg_pool3d(x, kernel_size=1, stride=stride)
+        zero_pads = torch.zeros(out.size(0), planes - out.size(1), out.size(2),
+                                out.size(3), out.size(4))
+        if isinstance(out.data, torch.cuda.FloatTensor):
+            zero_pads = zero_pads.cuda()
+
+        out = torch.cat([out.data, zero_pads], dim=1)
+
+        return out
+
+    def _make_layer(self, block, planes, blocks, shortcut_type, stride=1):
+        downsample = None
+        if stride != 1 or self.in_planes != planes * block.expansion:
+            if shortcut_type == 'A':
+                downsample = partial(self._downsample_basic_block,
+                                     planes=planes * block.expansion,
+                                     stride=stride)
+            else:
+                downsample = nn.Sequential(
+                    conv1x1x1(self.in_planes, planes * block.expansion, stride),
+                    nn.BatchNorm3d(planes * block.expansion))
+
+        layers = []
+        layers.append(
+            block(in_planes=self.in_planes,
+                  planes=planes,
+                  stride=stride,
+                  downsample=downsample))
+        self.in_planes = planes * block.expansion
+        for i in range(1, blocks):
+            layers.append(block(self.in_planes, planes))
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        if not self.no_max_pool:
+            x = self.maxpool(x)
+
+        x1 = self.layer1(x)
+        x2 = self.layer2(x1)
+        x3 = self.layer3(x2)
+        x4 = self.layer4(x3)
+        if self.forward_features:
+            return [x1,x2,x3,x4]
+        else:
+            x = self.avgpool(x4)
+
+            x = x.view(x.size(0), -1)
+            x = self.fc(x)
+
+            return x
+
+
+def generate_model(model_depth, **kwargs):
+    assert model_depth in [10, 18, 34, 50, 101, 152, 200]
+
+    if model_depth == 10:
+        model = ResNet(BasicBlock, [1, 1, 1, 1], get_inplanes(), **kwargs)
+    elif model_depth == 18:
+        model = ResNet(BasicBlock, [2, 2, 2, 2], get_inplanes(), **kwargs)
+    elif model_depth == 34:
+        model = ResNet(BasicBlock, [3, 4, 6, 3], get_inplanes(), **kwargs)
+    elif model_depth == 50:
+        model = ResNet(Bottleneck, [3, 4, 6, 3], get_inplanes(), **kwargs)
+    elif model_depth == 101:
+        model = ResNet(Bottleneck, [3, 4, 23, 3], get_inplanes(), **kwargs)
+    elif model_depth == 152:
+        model = ResNet(Bottleneck, [3, 8, 36, 3], get_inplanes(), **kwargs)
+    elif model_depth == 200:
+        model = ResNet(Bottleneck, [3, 24, 36, 3], get_inplanes(), **kwargs)
+
+    return model