hunk.code_window:     assert not torch.isnan(outputs).any(), 'Output included NaNs'
   @pytest.mark.timeout(120)
   @pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS))
   @pytest.mark.parametrize('batch_size', [2])
   def test_model_backward(model_name, batch_size):
       """Run a single forward pass with each model"""
  
hunk.labels: keep keep keep keep replace keep keep keep
hunk.after_edit: @pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS, name_matches_cfg=True))
  
hunk.file_path: tests/test_models.py
hunk.type: replace
file: # (Gluon) ResNet
  
  **Residual Networks**, or **ResNets**, learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers fit a residual mapping. They stack [residual blocks](https://paperswithcode.com/method/residual-block) ontop of each other to form network: e.g. a ResNet-50 has fifty layers using these blocks. 
  
  The weights from this model were ported from [Gluon](https://cv.gluon.ai/model_zoo/classification.html).
  
  ## How do I use this model on an image?
  To load a pretrained model:
  
  ```python
  import timm
  model = timm.create_model('gluon_resnet101_v1b', pretrained=True)
  model.eval()
  ```
  
  To load and preprocess the image:
  ```python 
  import urllib
  from PIL import Image
  from timm.data import resolve_data_config
  from timm.data.transforms_factory import create_transform
  
  config = resolve_data_config({}, model=model)
  transform = create_transform(**config)
  
  url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
  urllib.request.urlretrieve(url, filename)
  img = Image.open(filename).convert('RGB')
  tensor = transform(img).unsqueeze(0) # transform and add batch dimension
  ```
  
  To get the model predictions:
  ```python
  import torch
  with torch.no_grad():
      out = model(tensor)
  probabilities = torch.nn.functional.softmax(out[0], dim=0)
  print(probabilities.shape)
  # prints: torch.Size([1000])
  ```
  
  To get the top-5 predictions class names:
  ```python
  # Get imagenet class mappings
  url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
  urllib.request.urlretrieve(url, filename) 
  with open("imagenet_classes.txt", "r") as f:
      categories = [s.strip() for s in f.readlines()]
  
  # Print top categories per image
  top5_prob, top5_catid = torch.topk(probabilities, 5)
  for i in range(top5_prob.size(0)):
      print(categories[top5_catid[i]], top5_prob[i].item())
  # prints class names and probabilities like:
  # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
  ```
  
  Replace the model name with the variant you want to use, e.g. `gluon_resnet101_v1b`. You can find the IDs in the model summaries at the top of this page.
  
  To extract image features with this model, follow the [timm feature extraction examples](https://rwightman.github.io/pytorch-image-models/feature_extraction/), just change the name of the model you want to use.
  
  ## How do I finetune this model?
  You can finetune any of the pre-trained models just by changing the classifier (the last layer).
  ```python
  model = timm.create_model('gluon_resnet101_v1b', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
  ```
  To finetune on your own dataset, you have to write a training loop or adapt [timm's training
  script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
  
  ## How do I train this model?
  
  You can follow the [timm recipe scripts](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh.
  
  ## Citation
  
  ```BibTeX
  @article{DBLP:journals/corr/HeZRS15,
    author    = {Kaiming He and
                 Xiangyu Zhang and
                 Shaoqing Ren and
                 Jian Sun},
    title     = {Deep Residual Learning for Image Recognition},
    journal   = {CoRR},
    volume    = {abs/1512.03385},
    year      = {2015},
    url       = {http://arxiv.org/abs/1512.03385},
    archivePrefix = {arXiv},
    eprint    = {1512.03385},
    timestamp = {Wed, 17 Apr 2019 17:23:45 +0200},
    biburl    = {https://dblp.org/rec/journals/corr/HeZRS15.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }
  ```
  
  <!--
  Type: model-index
  Collections:
  - Name: Gloun ResNet
    Paper:
      Title: Deep Residual Learning for Image Recognition
      URL: https://paperswithcode.com/paper/deep-residual-learning-for-image-recognition
  Models:
  - Name: gluon_resnet101_v1b
    In Collection: Gloun ResNet
    Metadata:
      FLOPs: 10068547584
      Parameters: 44550000
      File Size: 178723172
      Architecture:
      - 1x1 Convolution
      - Batch Normalization
      - Bottleneck Residual Block
      - Convolution
      - Global Average Pooling
      - Max Pooling
      - ReLU
      - Residual Block
      - Residual Connection
      - Softmax
      Tasks:
      - Image Classification
      Training Data:
      - ImageNet
      ID: gluon_resnet101_v1b
      Crop Pct: '0.875'
      Image Size: '224'
      Interpolation: bicubic
    Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L89
    Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1b-3b017079.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 79.3%
        Top 5 Accuracy: 94.53%
  - Name: gluon_resnet101_v1c
    In Collection: Gloun ResNet
    Metadata:
      FLOPs: 10376567296
      Parameters: 44570000
      File Size: 178802575
      Architecture:
      - 1x1 Convolution
      - Batch Normalization
      - Bottleneck Residual Block
      - Convolution
      - Global Average Pooling
      - Max Pooling
      - ReLU
      - Residual Block
      - Residual Connection
      - Softmax
      Tasks:
      - Image Classification
      Training Data:
      - ImageNet
      ID: gluon_resnet101_v1c
      Crop Pct: '0.875'
      Image Size: '224'
      Interpolation: bicubic
    Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L113
    Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1c-1f26822a.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 79.53%
        Top 5 Accuracy: 94.59%
  - Name: gluon_resnet101_v1d
    In Collection: Gloun ResNet
    Metadata:
      FLOPs: 10377018880
      Parameters: 44570000
      File Size: 178802755
      Architecture:
      - 1x1 Convolution
      - Batch Normalization
      - Bottleneck Residual Block
      - Convolution
      - Global Average Pooling
      - Max Pooling
      - ReLU
      - Residual Block
      - Residual Connection
      - Softmax
      Tasks:
      - Image Classification
      Training Data:
      - ImageNet
      ID: gluon_resnet101_v1d
      Crop Pct: '0.875'
      Image Size: '224'
      Interpolation: bicubic
    Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L138
    Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1d-0f9c8644.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 80.4%
        Top 5 Accuracy: 95.02%
  - Name: gluon_resnet101_v1s
    In Collection: Gloun ResNet
    Metadata:
      FLOPs: 11805511680
      Parameters: 44670000
      File Size: 179221777
      Architecture:
      - 1x1 Convolution
      - Batch Normalization
      - Bottleneck Residual Block
      - Convolution
      - Global Average Pooling
      - Max Pooling
      - ReLU
      - Residual Block
      - Residual Connection
      - Softmax
      Tasks:
      - Image Classification
      Training Data:
      - ImageNet
      ID: gluon_resnet101_v1s
      Crop Pct: '0.875'
      Image Size: '224'
      Interpolation: bicubic
    Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L166
    Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1s-60fe0cc1.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 80.29%
        Top 5 Accuracy: 95.16%
  - Name: gluon_resnet152_v1b
    In Collection: Gloun ResNet
    Metadata:
      FLOPs: 14857660416
      Parameters: 60190000
      File Size: 241534001
      Architecture:
      - 1x1 Convolution
      - Batch Normalization
      - Bottleneck Residual Block
      - Convolution
      - Global Average Pooling
      - Max Pooling
      - ReLU
      - Residual Block
      - Residual Connection
      - Softmax
      Tasks:
      - Image Classification
      Training Data:
      - ImageNet
      ID: gluon_resnet152_v1b
      Crop Pct: '0.875'
      Image Size: '224'
      Interpolation: bicubic
    Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L97
    Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1b-c1edb0dd.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 79.69%
        Top 5 Accuracy: 94.73%
  - Name: gluon_resnet152_v1c
    In Collection: Gloun ResNet
    Metadata:
      FLOPs: 15165680128
      Parameters: 60210000
      File Size: 241613404
      Architecture:
      - 1x1 Convolution
      - Batch Normalization
      - Bottleneck Residual Block
      - Convolution
      - Global Average Pooling
      - Max Pooling
      - ReLU
      - Residual Block
      - Residual Connection
      - Softmax
      Tasks:
      - Image Classification
      Training Data:
      - ImageNet
      ID: gluon_resnet152_v1c
      Crop Pct: '0.875'
      Image Size: '224'
      Interpolation: bicubic
    Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L121
    Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1c-a3bb0b98.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 79.91%
        Top 5 Accuracy: 94.85%
  - Name: gluon_resnet152_v1d
    In Collection: Gloun ResNet
    Metadata:
      FLOPs: 15166131712
      Parameters: 60210000
      File Size: 241613584
      Architecture:
      - 1x1 Convolution
      - Batch Normalization
      - Bottleneck Residual Block
      - Convolution
      - Global Average Pooling
      - Max Pooling
      - ReLU
      - Residual Block
      - Residual Connection
      - Softmax
      Tasks:
      - Image Classification
      Training Data:
      - ImageNet
      ID: gluon_resnet152_v1d
      Crop Pct: '0.875'
      Image Size: '224'
      Interpolation: bicubic
    Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L147
    Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1d-bd354e12.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 80.48%
        Top 5 Accuracy: 95.2%
  - Name: gluon_resnet152_v1s
    In Collection: Gloun ResNet
    Metadata:
      FLOPs: 16594624512
      Parameters: 60320000
      File Size: 242032606
      Architecture:
      - 1x1 Convolution
      - Batch Normalization
      - Bottleneck Residual Block
      - Convolution
      - Global Average Pooling
      - Max Pooling
      - ReLU
      - Residual Block
      - Residual Connection
      - Softmax
      Tasks:
      - Image Classification
      Training Data:
      - ImageNet
      ID: gluon_resnet152_v1s
      Crop Pct: '0.875'
      Image Size: '224'
      Interpolation: bicubic
    Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L175
    Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1s-dcc41b81.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 81.02%
        Top 5 Accuracy: 95.42%
  - Name: gluon_resnet18_v1b
    In Collection: Gloun ResNet
    Metadata:
      FLOPs: 2337073152
      Parameters: 11690000
      File Size: 46816736
      Architecture:
      - 1x1 Convolution
      - Batch Normalization
      - Bottleneck Residual Block
      - Convolution
      - Global Average Pooling
      - Max Pooling
      - ReLU
      - Residual Block
      - Residual Connection
      - Softmax
      Tasks:
      - Image Classification
      Training Data:
      - ImageNet
      ID: gluon_resnet18_v1b
      Crop Pct: '0.875'
      Image Size: '224'
      Interpolation: bicubic
    Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L65
    Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet18_v1b-0757602b.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 70.84%
        Top 5 Accuracy: 89.76%
  - Name: gluon_resnet34_v1b
    In Collection: Gloun ResNet
    Metadata:
      FLOPs: 4718469120
      Parameters: 21800000
      File Size: 87295112
      Architecture:
      - 1x1 Convolution
      - Batch Normalization
      - Bottleneck Residual Block
      - Convolution
      - Global Average Pooling
      - Max Pooling
      - ReLU
      - Residual Block
      - Residual Connection
      - Softmax
      Tasks:
      - Image Classification
      Training Data:
      - ImageNet
      ID: gluon_resnet34_v1b
      Crop Pct: '0.875'
      Image Size: '224'
      Interpolation: bicubic
    Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L73
    Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet34_v1b-c6d82d59.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 74.59%
        Top 5 Accuracy: 92.0%
  - Name: gluon_resnet50_v1b
    In Collection: Gloun ResNet
    Metadata:
      FLOPs: 5282531328
      Parameters: 25560000
      File Size: 102493763
      Architecture:
      - 1x1 Convolution
      - Batch Normalization
      - Bottleneck Residual Block
      - Convolution
      - Global Average Pooling
      - Max Pooling
      - ReLU
      - Residual Block
      - Residual Connection
      - Softmax
      Tasks:
      - Image Classification
      Training Data:
      - ImageNet
      ID: gluon_resnet50_v1b
      Crop Pct: '0.875'
      Image Size: '224'
      Interpolation: bicubic
    Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L81
    Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1b-0ebe02e2.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 77.58%
        Top 5 Accuracy: 93.72%
  - Name: gluon_resnet50_v1c
    In Collection: Gloun ResNet
    Metadata:
      FLOPs: 5590551040
      Parameters: 25580000
      File Size: 102573166
      Architecture:
      - 1x1 Convolution
      - Batch Normalization
      - Bottleneck Residual Block
      - Convolution
      - Global Average Pooling
      - Max Pooling
      - ReLU
      - Residual Block
      - Residual Connection
      - Softmax
      Tasks:
      - Image Classification
      Training Data:
      - ImageNet
      ID: gluon_resnet50_v1c
      Crop Pct: '0.875'
      Image Size: '224'
      Interpolation: bicubic
    Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L105
    Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1c-48092f55.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 78.01%
        Top 5 Accuracy: 93.99%
  - Name: gluon_resnet50_v1d
    In Collection: Gloun ResNet
    Metadata:
      FLOPs: 5591002624
      Parameters: 25580000
      File Size: 102573346
      Architecture:
      - 1x1 Convolution
      - Batch Normalization
      - Bottleneck Residual Block
      - Convolution
      - Global Average Pooling
      - Max Pooling
      - ReLU
      - Residual Block
      - Residual Connection
      - Softmax
      Tasks:
      - Image Classification
      Training Data:
      - ImageNet
      ID: gluon_resnet50_v1d
      Crop Pct: '0.875'
      Image Size: '224'
      Interpolation: bicubic
    Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L129
    Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1d-818a1b1b.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 79.06%
        Top 5 Accuracy: 94.46%
  - Name: gluon_resnet50_v1s
    In Collection: Gloun ResNet
    Metadata:
      FLOPs: 7019495424
      Parameters: 25680000
      File Size: 102992368
      Architecture:
      - 1x1 Convolution
      - Batch Normalization
      - Bottleneck Residual Block
      - Convolution
      - Global Average Pooling
      - Max Pooling
      - ReLU
      - Residual Block
      - Residual Connection
      - Softmax
      Tasks:
      - Image Classification
      Training Data:
      - ImageNet
      ID: gluon_resnet50_v1s
      Crop Pct: '0.875'
      Image Size: '224'
      Interpolation: bicubic
    Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L156
    Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1s-1762acc0.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 78.7%
        Top 5 Accuracy: 94.25%
  -->