hunk.code_window: def update_summary(epoch, train_metrics, eval_metrics, filename, write_header=False, log_wandb=False):
       rowd = OrderedDict(epoch=epoch)
       rowd.update([('train_' + k, v) for k, v in train_metrics.items()])
       rowd.update([('eval_' + k, v) for k, v in eval_metrics.items()])
       if log_wandb:
           wandb.log(rowd)
       with open(filename, mode='a') as cf:
           dw = csv.DictWriter(cf, fieldnames=rowd.keys())
           if write_header:  # first iteration (epoch == 1 can't be used)
  
hunk.labels: keep keep keep keep keep replace replace keep keep keep
hunk.file_path: timm/utils/summary.py
hunk.type: replace
file: # Xception
  
  **Xception** is a convolutional neural network architecture that relies solely on [depthwise separable convolution layers](https://paperswithcode.com/method/depthwise-separable-convolution).
  
  The weights from this model were ported from [Tensorflow/Models](https://github.com/tensorflow/models).
  
  ## How do I use this model on an image?
  To load a pretrained model:
  
  ```python
  import timm
  model = timm.create_model('xception', 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. `xception`. 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('xception', 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/ZagoruykoK16,
  @misc{chollet2017xception,
        title={Xception: Deep Learning with Depthwise Separable Convolutions}, 
        author={François Chollet},
        year={2017},
        eprint={1610.02357},
        archivePrefix={arXiv},
        primaryClass={cs.CV}
  }
  ```
  
  <!--
  Type: model-index
  Collections:
  - Name: Xception
    Paper:
      Title: 'Xception: Deep Learning with Depthwise Separable Convolutions'
      URL: https://paperswithcode.com/paper/xception-deep-learning-with-depthwise
  Models:
  - Name: xception
    In Collection: Xception
    Metadata:
      FLOPs: 10600506792
      Parameters: 22860000
      File Size: 91675053
      Architecture:
      - 1x1 Convolution
      - Convolution
      - Dense Connections
      - Depthwise Separable Convolution
      - Global Average Pooling
      - Max Pooling
      - ReLU
      - Residual Connection
      - Softmax
      Tasks:
      - Image Classification
      Training Data:
      - ImageNet
      ID: xception
      Crop Pct: '0.897'
      Image Size: '299'
      Interpolation: bicubic
    Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/xception.py#L229
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/xception-43020ad28.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 79.05%
        Top 5 Accuracy: 94.4%
  - Name: xception41
    In Collection: Xception
    Metadata:
      FLOPs: 11681983232
      Parameters: 26970000
      File Size: 108422028
      Architecture:
      - 1x1 Convolution
      - Convolution
      - Dense Connections
      - Depthwise Separable Convolution
      - Global Average Pooling
      - Max Pooling
      - ReLU
      - Residual Connection
      - Softmax
      Tasks:
      - Image Classification
      Training Data:
      - ImageNet
      ID: xception41
      Crop Pct: '0.903'
      Image Size: '299'
      Interpolation: bicubic
    Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/xception_aligned.py#L181
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_xception_41-e6439c97.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 78.54%
        Top 5 Accuracy: 94.28%
  - Name: xception65
    In Collection: Xception
    Metadata:
      FLOPs: 17585702144
      Parameters: 39920000
      File Size: 160536780
      Architecture:
      - 1x1 Convolution
      - Convolution
      - Dense Connections
      - Depthwise Separable Convolution
      - Global Average Pooling
      - Max Pooling
      - ReLU
      - Residual Connection
      - Softmax
      Tasks:
      - Image Classification
      Training Data:
      - ImageNet
      ID: xception65
      Crop Pct: '0.903'
      Image Size: '299'
      Interpolation: bicubic
    Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/xception_aligned.py#L200
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_xception_65-c9ae96e8.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 79.55%
        Top 5 Accuracy: 94.66%
  - Name: xception71
    In Collection: Xception
    Metadata:
      FLOPs: 22817346560
      Parameters: 42340000
      File Size: 170295556
      Architecture:
      - 1x1 Convolution
      - Convolution
      - Dense Connections
      - Depthwise Separable Convolution
      - Global Average Pooling
      - Max Pooling
      - ReLU
      - Residual Connection
      - Softmax
      Tasks:
      - Image Classification
      Training Data:
      - ImageNet
      ID: xception71
      Crop Pct: '0.903'
      Image Size: '299'
      Interpolation: bicubic
    Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/xception_aligned.py#L219
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_xception_71-8eec7df1.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 79.88%
        Top 5 Accuracy: 94.93%
  -->