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: # MnasNet
  
  **MnasNet** is a type of convolutional neural network optimized for mobile devices that is discovered through mobile neural architecture search, which explicitly incorporates model latency into the main objective so that the search can identify a model that achieves a good trade-off between accuracy and latency. The main building block is an [inverted residual block](https://paperswithcode.com/method/inverted-residual-block) (from [MobileNetV2](https://paperswithcode.com/method/mobilenetv2)).
  
  ## How do I use this model on an image?
  To load a pretrained model:
  
  ```python
  import timm
  model = timm.create_model('mnasnet_100', 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. `mnasnet_100`. 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('mnasnet_100', 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{tan2019mnasnet,
        title={MnasNet: Platform-Aware Neural Architecture Search for Mobile}, 
        author={Mingxing Tan and Bo Chen and Ruoming Pang and Vijay Vasudevan and Mark Sandler and Andrew Howard and Quoc V. Le},
        year={2019},
        eprint={1807.11626},
        archivePrefix={arXiv},
        primaryClass={cs.CV}
  }
  ```
  
  <!--
  Type: model-index
  Collections:
  - Name: MNASNet
    Paper:
      Title: 'MnasNet: Platform-Aware Neural Architecture Search for Mobile'
      URL: https://paperswithcode.com/paper/mnasnet-platform-aware-neural-architecture
  Models:
  - Name: mnasnet_100
    In Collection: MNASNet
    Metadata:
      FLOPs: 416415488
      Parameters: 4380000
      File Size: 17731774
      Architecture:
      - 1x1 Convolution
      - Batch Normalization
      - Convolution
      - Depthwise Separable Convolution
      - Dropout
      - Global Average Pooling
      - Inverted Residual Block
      - Max Pooling
      - ReLU
      - Residual Connection
      - Softmax
      Tasks:
      - Image Classification
      Training Techniques:
      - RMSProp
      - Weight Decay
      Training Data:
      - ImageNet
      ID: mnasnet_100
      Layers: 100
      Dropout: 0.2
      Crop Pct: '0.875'
      Momentum: 0.9
      Batch Size: 4000
      Image Size: '224'
      Interpolation: bicubic
      RMSProp Decay: 0.9
    Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L894
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mnasnet_b1-74cb7081.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 74.67%
        Top 5 Accuracy: 92.1%
  - Name: semnasnet_100
    In Collection: MNASNet
    Metadata:
      FLOPs: 414570766
      Parameters: 3890000
      File Size: 15731489
      Architecture:
      - 1x1 Convolution
      - Batch Normalization
      - Convolution
      - Depthwise Separable Convolution
      - Dropout
      - Global Average Pooling
      - Inverted Residual Block
      - Max Pooling
      - ReLU
      - Residual Connection
      - Softmax
      - Squeeze-and-Excitation Block
      Tasks:
      - Image Classification
      Training Data:
      - ImageNet
      ID: semnasnet_100
      Crop Pct: '0.875'
      Image Size: '224'
      Interpolation: bicubic
    Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L928
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mnasnet_a1-d9418771.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 75.45%
        Top 5 Accuracy: 92.61%
  -->