hunk.code_window:             attn_drop_rate (float): attention dropout rate
               drop_path_rate (float): stochastic depth rate
               weight_init (str): weight init scheme
               class_token (bool): use class token
               fc_norm (Optional[bool]): pre-fc norm after pool, set if global_pool == 'avg' if None (default: None)
               embed_layer (nn.Module): patch embedding layer
               norm_layer: (nn.Module): normalization layer
               act_layer: (nn.Module): MLP activation layer
           """
  
hunk.labels: keep keep keep replace replace keep keep keep keep
hunk.file_path: timm/models/vision_transformer.py
hunk.type: replace
file: # EfficientNet (Knapsack Pruned)
  
  **EfficientNet** is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a *compound coefficient*. Unlike conventional practice that arbitrary scales  these factors, the EfficientNet scaling method uniformly scales network width, depth, and resolution with a set of fixed scaling coefficients. For example, if we want to use $2^N$ times more computational resources, then we can simply increase the network depth by $\alpha ^ N$,  width by $\beta ^ N$, and image size by $\gamma ^ N$, where $\alpha, \beta, \gamma$ are constant coefficients determined by a small grid search on the original small model. EfficientNet uses a compound coefficient $\phi$ to uniformly scales network width, depth, and resolution in a  principled way.
  
  The compound scaling method is justified by the intuition that if the input image is bigger, then the network needs more layers to increase the receptive field and more channels to capture more fine-grained patterns on the bigger image.
  
  The base EfficientNet-B0 network is based on the inverted bottleneck residual blocks of [MobileNetV2](https://paperswithcode.com/method/mobilenetv2), in addition to [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block).
  
  This collection consists of pruned EfficientNet models.
  
  ## How do I use this model on an image?
  To load a pretrained model:
  
  ```python
  import timm
  model = timm.create_model('efficientnet_b1_pruned', 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. `efficientnet_b1_pruned`. 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('efficientnet_b1_pruned', 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
  @misc{tan2020efficientnet,
        title={EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks}, 
        author={Mingxing Tan and Quoc V. Le},
        year={2020},
        eprint={1905.11946},
        archivePrefix={arXiv},
        primaryClass={cs.LG}
  }
  ```
  
  ```
  @misc{aflalo2020knapsack,
        title={Knapsack Pruning with Inner Distillation},
        author={Yonathan Aflalo and Asaf Noy and Ming Lin and Itamar Friedman and Lihi Zelnik},
        year={2020},
        eprint={2002.08258},
        archivePrefix={arXiv},
        primaryClass={cs.LG}
  }
  ```
  
  <!--
  Type: model-index
  Collections:
  - Name: EfficientNet Pruned
    Paper:
      Title: Knapsack Pruning with Inner Distillation
      URL: https://paperswithcode.com/paper/knapsack-pruning-with-inner-distillation
  Models:
  - Name: efficientnet_b1_pruned
    In Collection: EfficientNet Pruned
    Metadata:
      FLOPs: 489653114
      Parameters: 6330000
      File Size: 25595162
      Architecture:
      - 1x1 Convolution
      - Average Pooling
      - Batch Normalization
      - Convolution
      - Dense Connections
      - Dropout
      - Inverted Residual Block
      - Squeeze-and-Excitation Block
      - Swish
      Tasks:
      - Image Classification
      Training Data:
      - ImageNet
      ID: efficientnet_b1_pruned
      Crop Pct: '0.882'
      Image Size: '240'
      Interpolation: bicubic
    Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/efficientnet.py#L1208
    Weights: https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45403/outputs/effnetb1_pruned_9ebb3fe6.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 78.25%
        Top 5 Accuracy: 93.84%
  - Name: efficientnet_b2_pruned
    In Collection: EfficientNet Pruned
    Metadata:
      FLOPs: 878133915
      Parameters: 8310000
      File Size: 33555005
      Architecture:
      - 1x1 Convolution
      - Average Pooling
      - Batch Normalization
      - Convolution
      - Dense Connections
      - Dropout
      - Inverted Residual Block
      - Squeeze-and-Excitation Block
      - Swish
      Tasks:
      - Image Classification
      Training Data:
      - ImageNet
      ID: efficientnet_b2_pruned
      Crop Pct: '0.89'
      Image Size: '260'
      Interpolation: bicubic
    Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/efficientnet.py#L1219
    Weights: https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45403/outputs/effnetb2_pruned_203f55bc.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 79.91%
        Top 5 Accuracy: 94.86%
  - Name: efficientnet_b3_pruned
    In Collection: EfficientNet Pruned
    Metadata:
      FLOPs: 1239590641
      Parameters: 9860000
      File Size: 39770812
      Architecture:
      - 1x1 Convolution
      - Average Pooling
      - Batch Normalization
      - Convolution
      - Dense Connections
      - Dropout
      - Inverted Residual Block
      - Squeeze-and-Excitation Block
      - Swish
      Tasks:
      - Image Classification
      Training Data:
      - ImageNet
      ID: efficientnet_b3_pruned
      Crop Pct: '0.904'
      Image Size: '300'
      Interpolation: bicubic
    Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/efficientnet.py#L1230
    Weights: https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45403/outputs/effnetb3_pruned_5abcc29f.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 80.86%
        Top 5 Accuracy: 95.24%
  -->