hunk.code_window:         if target and max(input_size) > target:
               input_size = default_cfg['min_input_size']
       else:
           if target and max(input_size) > target:
               input_size = tuple([min(x, target) for x in input_size])
       return input_size
  
hunk.labels: keep replace keep keep keep keep keep
hunk.after_edit:             input_size = min_input_size
  
hunk.file_path: tests/test_models.py
hunk.type: replace
file: # SSL 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 model in this collection utilises semi-supervised learning to improve the performance of the model. The approach brings important gains to standard architectures for image, video and fine-grained classification. 
  
  Please note the CC-BY-NC 4.0 license on theses weights, non-commercial use only.
  
  ## How do I use this model on an image?
  To load a pretrained model:
  
  ```python
  import timm
  model = timm.create_model('ssl_resnet18', 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. `ssl_resnet18`. 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('ssl_resnet18', 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/abs-1905-00546,
    author    = {I. Zeki Yalniz and
                 Herv{\'{e}} J{\'{e}}gou and
                 Kan Chen and
                 Manohar Paluri and
                 Dhruv Mahajan},
    title     = {Billion-scale semi-supervised learning for image classification},
    journal   = {CoRR},
    volume    = {abs/1905.00546},
    year      = {2019},
    url       = {http://arxiv.org/abs/1905.00546},
    archivePrefix = {arXiv},
    eprint    = {1905.00546},
    timestamp = {Mon, 28 Sep 2020 08:19:37 +0200},
    biburl    = {https://dblp.org/rec/journals/corr/abs-1905-00546.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }
  ```
  
  <!--
  Type: model-index
  Collections:
  - Name: SSL ResNet
    Paper:
      Title: Billion-scale semi-supervised learning for image classification
      URL: https://paperswithcode.com/paper/billion-scale-semi-supervised-learning-for
  Models:
  - Name: ssl_resnet18
    In Collection: SSL ResNet
    Metadata:
      FLOPs: 2337073152
      Parameters: 11690000
      File Size: 46811375
      Architecture:
      - 1x1 Convolution
      - Batch Normalization
      - Bottleneck Residual Block
      - Convolution
      - Global Average Pooling
      - Max Pooling
      - ReLU
      - Residual Block
      - Residual Connection
      - Softmax
      Tasks:
      - Image Classification
      Training Techniques:
      - SGD with Momentum
      - Weight Decay
      Training Data:
      - ImageNet
      - YFCC-100M
      Training Resources: 64x GPUs
      ID: ssl_resnet18
      LR: 0.0015
      Epochs: 30
      Layers: 18
      Crop Pct: '0.875'
      Batch Size: 1536
      Image Size: '224'
      Weight Decay: 0.0001
      Interpolation: bilinear
    Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L894
    Weights: https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnet18-d92f0530.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 72.62%
        Top 5 Accuracy: 91.42%
  - Name: ssl_resnet50
    In Collection: SSL ResNet
    Metadata:
      FLOPs: 5282531328
      Parameters: 25560000
      File Size: 102480594
      Architecture:
      - 1x1 Convolution
      - Batch Normalization
      - Bottleneck Residual Block
      - Convolution
      - Global Average Pooling
      - Max Pooling
      - ReLU
      - Residual Block
      - Residual Connection
      - Softmax
      Tasks:
      - Image Classification
      Training Techniques:
      - SGD with Momentum
      - Weight Decay
      Training Data:
      - ImageNet
      - YFCC-100M
      Training Resources: 64x GPUs
      ID: ssl_resnet50
      LR: 0.0015
      Epochs: 30
      Layers: 50
      Crop Pct: '0.875'
      Batch Size: 1536
      Image Size: '224'
      Weight Decay: 0.0001
      Interpolation: bilinear
    Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L904
    Weights: https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnet50-08389792.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 79.24%
        Top 5 Accuracy: 94.83%
  -->