hunk.code_window: except AttributeError:
       pass
   torch.backends.cudnn.benchmark = True
   _logger = logging.getLogger('train')
   # The first arg parser parses out only the --config argument, this argument is used to
   # load a yaml file containing key-values that override the defaults for the main parser below
  
hunk.labels: keep keep add keep keep keep keep keep
hunk.after_edit: try:
       import wandb
       has_wandb = True
   except ModuleNotFoundError: 
       has_wandb = False
  
hunk.file_path: train.py
hunk.type: add
file: # (Tensorflow) MixNet
  
  **MixNet** is a type of convolutional neural network discovered via AutoML that utilises [MixConvs](https://paperswithcode.com/method/mixconv) instead of regular [depthwise convolutions](https://paperswithcode.com/method/depthwise-convolution).
  
  The weights from this model were ported from [Tensorflow/TPU](https://github.com/tensorflow/tpu).
  
  {% include 'code_snippets.md' %}
  
  ## 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
  @misc{tan2019mixconv,
        title={MixConv: Mixed Depthwise Convolutional Kernels}, 
        author={Mingxing Tan and Quoc V. Le},
        year={2019},
        eprint={1907.09595},
        archivePrefix={arXiv},
        primaryClass={cs.CV}
  }
  ```
  
  <!--
  Type: model-index
  Collections:
  - Name: TF MixNet
    Paper:
      Title: 'MixConv: Mixed Depthwise Convolutional Kernels'
      URL: https://paperswithcode.com/paper/mixnet-mixed-depthwise-convolutional-kernels
  Models:
  - Name: tf_mixnet_l
    In Collection: TF MixNet
    Metadata:
      FLOPs: 688674516
      Parameters: 7330000
      File Size: 29620756
      Architecture:
      - Batch Normalization
      - Dense Connections
      - Dropout
      - Global Average Pooling
      - Grouped Convolution
      - MixConv
      - Squeeze-and-Excitation Block
      - Swish
      Tasks:
      - Image Classification
      Training Techniques:
      - MNAS
      Training Data:
      - ImageNet
      ID: tf_mixnet_l
      Crop Pct: '0.875'
      Image Size: '224'
      Interpolation: bicubic
    Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1720
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_l-6c92e0c8.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 78.78%
        Top 5 Accuracy: 94.0%
  - Name: tf_mixnet_m
    In Collection: TF MixNet
    Metadata:
      FLOPs: 416633502
      Parameters: 5010000
      File Size: 20310871
      Architecture:
      - Batch Normalization
      - Dense Connections
      - Dropout
      - Global Average Pooling
      - Grouped Convolution
      - MixConv
      - Squeeze-and-Excitation Block
      - Swish
      Tasks:
      - Image Classification
      Training Techniques:
      - MNAS
      Training Data:
      - ImageNet
      ID: tf_mixnet_m
      Crop Pct: '0.875'
      Image Size: '224'
      Interpolation: bicubic
    Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1709
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_m-0f4d8805.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 76.96%
        Top 5 Accuracy: 93.16%
  - Name: tf_mixnet_s
    In Collection: TF MixNet
    Metadata:
      FLOPs: 302587678
      Parameters: 4130000
      File Size: 16738218
      Architecture:
      - Batch Normalization
      - Dense Connections
      - Dropout
      - Global Average Pooling
      - Grouped Convolution
      - MixConv
      - Squeeze-and-Excitation Block
      - Swish
      Tasks:
      - Image Classification
      Training Techniques:
      - MNAS
      Training Data:
      - ImageNet
      ID: tf_mixnet_s
      Crop Pct: '0.875'
      Image Size: '224'
      Interpolation: bicubic
    Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1698
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_s-89d3354b.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 75.68%
        Top 5 Accuracy: 92.64%
  -->
  