hunk.code_window:     model = create_model(model_name, pretrained=False, num_classes=42)
       num_params = sum([x.numel() for x in model.parameters()])
       model.train()
       input_size = _get_input_size(model, TARGET_BWD_SIZE)
       if max(input_size) > MAX_BWD_SIZE:
           pytest.skip("Fixed input size model > limit.")
       inputs = torch.randn((batch_size, *input_size))
       outputs = model(inputs)
       if isinstance(outputs, tuple):
  
hunk.labels: keep keep keep keep replace replace replace replace keep keep keep
hunk.file_path: tests/test_models.py
hunk.type: replace
file: # SelecSLS
  
  **SelecSLS** uses novel selective long and short range skip connections to improve the information flow allowing for a drastically faster network without compromising accuracy.
  
  ## How do I use this model on an image?
  To load a pretrained model:
  
  ```python
  import timm
  model = timm.create_model('selecsls42b', 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. `selecsls42b`. 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('selecsls42b', 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{Mehta_2020,
     title={XNect},
     volume={39},
     ISSN={1557-7368},
     url={http://dx.doi.org/10.1145/3386569.3392410},
     DOI={10.1145/3386569.3392410},
     number={4},
     journal={ACM Transactions on Graphics},
     publisher={Association for Computing Machinery (ACM)},
     author={Mehta, Dushyant and Sotnychenko, Oleksandr and Mueller, Franziska and Xu, Weipeng and Elgharib, Mohamed and Fua, Pascal and Seidel, Hans-Peter and Rhodin, Helge and Pons-Moll, Gerard and Theobalt, Christian},
     year={2020},
     month={Jul}
  }
  ```
  
  <!--
  Type: model-index
  Collections:
  - Name: SelecSLS
    Paper:
      Title: 'XNect: Real-time Multi-Person 3D Motion Capture with a Single RGB Camera'
      URL: https://paperswithcode.com/paper/xnect-real-time-multi-person-3d-human-pose
  Models:
  - Name: selecsls42b
    In Collection: SelecSLS
    Metadata:
      FLOPs: 3824022528
      Parameters: 32460000
      File Size: 129948954
      Architecture:
      - Batch Normalization
      - Convolution
      - Dense Connections
      - Dropout
      - Global Average Pooling
      - ReLU
      - SelecSLS Block
      Tasks:
      - Image Classification
      Training Techniques:
      - Cosine Annealing
      - Random Erasing
      Training Data:
      - ImageNet
      ID: selecsls42b
      Crop Pct: '0.875'
      Image Size: '224'
      Interpolation: bicubic
    Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/selecsls.py#L335
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-selecsls/selecsls42b-8af30141.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 77.18%
        Top 5 Accuracy: 93.39%
  - Name: selecsls60
    In Collection: SelecSLS
    Metadata:
      FLOPs: 4610472600
      Parameters: 30670000
      File Size: 122839714
      Architecture:
      - Batch Normalization
      - Convolution
      - Dense Connections
      - Dropout
      - Global Average Pooling
      - ReLU
      - SelecSLS Block
      Tasks:
      - Image Classification
      Training Techniques:
      - Cosine Annealing
      - Random Erasing
      Training Data:
      - ImageNet
      ID: selecsls60
      Crop Pct: '0.875'
      Image Size: '224'
      Interpolation: bicubic
    Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/selecsls.py#L342
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-selecsls/selecsls60-bbf87526.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 77.99%
        Top 5 Accuracy: 93.83%
  - Name: selecsls60b
    In Collection: SelecSLS
    Metadata:
      FLOPs: 4657653144
      Parameters: 32770000
      File Size: 131252898
      Architecture:
      - Batch Normalization
      - Convolution
      - Dense Connections
      - Dropout
      - Global Average Pooling
      - ReLU
      - SelecSLS Block
      Tasks:
      - Image Classification
      Training Techniques:
      - Cosine Annealing
      - Random Erasing
      Training Data:
      - ImageNet
      ID: selecsls60b
      Crop Pct: '0.875'
      Image Size: '224'
      Interpolation: bicubic
    Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/selecsls.py#L349
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-selecsls/selecsls60b-94e619b5.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 78.41%
        Top 5 Accuracy: 94.18%
  -->