hunk.code_window:         filter (str) - Wildcard filter string that works with fnmatch
           module (str) - Limit model selection to a specific sub-module (ie 'gen_efficientnet')
           pretrained (bool) - Include only models with pretrained weights if True
           exclude_filters (str or list[str]) - Wildcard filters to exclude models after including them with filter
       Example:
           model_list('gluon_resnet*') -- returns all models starting with 'gluon_resnet'
           model_list('*resnext*, 'resnet') -- returns all models with 'resnext' in 'resnet' module
       """
  
hunk.labels: keep keep keep add keep keep keep keep keep
hunk.after_edit:         name_matches_cfg (bool) - Include only models w/ model_name matching default_cfg name (excludes some aliases)
  
hunk.file_path: timm/models/registry.py
hunk.type: add
file: # HRNet
  
  **HRNet**, or **High-Resolution Net**, is a general purpose convolutional neural network for tasks like semantic segmentation, object detection and image classification. It is able to maintain high resolution representations through the whole process. We start from a high-resolution convolution stream, gradually add high-to-low resolution convolution streams one by one, and connect the multi-resolution streams in parallel. The resulting network consists of several ($4$ in the paper) stages and the $n$th stage contains $n$ streams corresponding to $n$ resolutions. The authors conduct repeated multi-resolution fusions by exchanging the information across the parallel streams over and over.
  
  ## How do I use this model on an image?
  To load a pretrained model:
  
  ```python
  import timm
  model = timm.create_model('hrnet_w18', 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. `hrnet_w18`. 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('hrnet_w18', 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{sun2019highresolution,
        title={High-Resolution Representations for Labeling Pixels and Regions}, 
        author={Ke Sun and Yang Zhao and Borui Jiang and Tianheng Cheng and Bin Xiao and Dong Liu and Yadong Mu and Xinggang Wang and Wenyu Liu and Jingdong Wang},
        year={2019},
        eprint={1904.04514},
        archivePrefix={arXiv},
        primaryClass={cs.CV}
  }
  ```
  
  <!--
  Type: model-index
  Collections:
  - Name: HRNet
    Paper:
      Title: Deep High-Resolution Representation Learning for Visual Recognition
      URL: https://paperswithcode.com/paper/190807919
  Models:
  - Name: hrnet_w18
    In Collection: HRNet
    Metadata:
      FLOPs: 5547205500
      Parameters: 21300000
      File Size: 85718883
      Architecture:
      - Batch Normalization
      - Convolution
      - ReLU
      - Residual Connection
      Tasks:
      - Image Classification
      Training Techniques:
      - Nesterov Accelerated Gradient
      - Weight Decay
      Training Data:
      - ImageNet
      Training Resources: 4x NVIDIA V100 GPUs
      ID: hrnet_w18
      Epochs: 100
      Layers: 18
      Crop Pct: '0.875'
      Momentum: 0.9
      Batch Size: 256
      Image Size: '224'
      Weight Decay: 0.001
      Interpolation: bilinear
    Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/hrnet.py#L800
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnetv2_w18-8cb57bb9.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 76.76%
        Top 5 Accuracy: 93.44%
  - Name: hrnet_w18_small
    In Collection: HRNet
    Metadata:
      FLOPs: 2071651488
      Parameters: 13190000
      File Size: 52934302
      Architecture:
      - Batch Normalization
      - Convolution
      - ReLU
      - Residual Connection
      Tasks:
      - Image Classification
      Training Techniques:
      - Nesterov Accelerated Gradient
      - Weight Decay
      Training Data:
      - ImageNet
      Training Resources: 4x NVIDIA V100 GPUs
      ID: hrnet_w18_small
      Epochs: 100
      Layers: 18
      Crop Pct: '0.875'
      Momentum: 0.9
      Batch Size: 256
      Image Size: '224'
      Weight Decay: 0.001
      Interpolation: bilinear
    Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/hrnet.py#L790
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnet_w18_small_v1-f460c6bc.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 72.34%
        Top 5 Accuracy: 90.68%
  - Name: hrnet_w18_small_v2
    In Collection: HRNet
    Metadata:
      FLOPs: 3360023160
      Parameters: 15600000
      File Size: 62682879
      Architecture:
      - Batch Normalization
      - Convolution
      - ReLU
      - Residual Connection
      Tasks:
      - Image Classification
      Training Techniques:
      - Nesterov Accelerated Gradient
      - Weight Decay
      Training Data:
      - ImageNet
      Training Resources: 4x NVIDIA V100 GPUs
      ID: hrnet_w18_small_v2
      Epochs: 100
      Layers: 18
      Crop Pct: '0.875'
      Momentum: 0.9
      Batch Size: 256
      Image Size: '224'
      Weight Decay: 0.001
      Interpolation: bilinear
    Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/hrnet.py#L795
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnet_w18_small_v2-4c50a8cb.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 75.11%
        Top 5 Accuracy: 92.41%
  - Name: hrnet_w30
    In Collection: HRNet
    Metadata:
      FLOPs: 10474119492
      Parameters: 37710000
      File Size: 151452218
      Architecture:
      - Batch Normalization
      - Convolution
      - ReLU
      - Residual Connection
      Tasks:
      - Image Classification
      Training Techniques:
      - Nesterov Accelerated Gradient
      - Weight Decay
      Training Data:
      - ImageNet
      Training Resources: 4x NVIDIA V100 GPUs
      ID: hrnet_w30
      Epochs: 100
      Layers: 30
      Crop Pct: '0.875'
      Momentum: 0.9
      Batch Size: 256
      Image Size: '224'
      Weight Decay: 0.001
      Interpolation: bilinear
    Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/hrnet.py#L805
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnetv2_w30-8d7f8dab.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 78.21%
        Top 5 Accuracy: 94.22%
  - Name: hrnet_w32
    In Collection: HRNet
    Metadata:
      FLOPs: 11524528320
      Parameters: 41230000
      File Size: 165547812
      Architecture:
      - Batch Normalization
      - Convolution
      - ReLU
      - Residual Connection
      Tasks:
      - Image Classification
      Training Techniques:
      - Nesterov Accelerated Gradient
      - Weight Decay
      Training Data:
      - ImageNet
      Training Resources: 4x NVIDIA V100 GPUs
      Training Time: 60 hours
      ID: hrnet_w32
      Epochs: 100
      Layers: 32
      Crop Pct: '0.875'
      Momentum: 0.9
      Batch Size: 256
      Image Size: '224'
      Weight Decay: 0.001
      Interpolation: bilinear
    Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/hrnet.py#L810
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnetv2_w32-90d8c5fb.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 78.45%
        Top 5 Accuracy: 94.19%
  - Name: hrnet_w40
    In Collection: HRNet
    Metadata:
      FLOPs: 16381182192
      Parameters: 57560000
      File Size: 230899236
      Architecture:
      - Batch Normalization
      - Convolution
      - ReLU
      - Residual Connection
      Tasks:
      - Image Classification
      Training Techniques:
      - Nesterov Accelerated Gradient
      - Weight Decay
      Training Data:
      - ImageNet
      Training Resources: 4x NVIDIA V100 GPUs
      ID: hrnet_w40
      Epochs: 100
      Layers: 40
      Crop Pct: '0.875'
      Momentum: 0.9
      Batch Size: 256
      Image Size: '224'
      Weight Decay: 0.001
      Interpolation: bilinear
    Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/hrnet.py#L815
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnetv2_w40-7cd397a4.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 78.93%
        Top 5 Accuracy: 94.48%
  - Name: hrnet_w44
    In Collection: HRNet
    Metadata:
      FLOPs: 19202520264
      Parameters: 67060000
      File Size: 268957432
      Architecture:
      - Batch Normalization
      - Convolution
      - ReLU
      - Residual Connection
      Tasks:
      - Image Classification
      Training Techniques:
      - Nesterov Accelerated Gradient
      - Weight Decay
      Training Data:
      - ImageNet
      Training Resources: 4x NVIDIA V100 GPUs
      ID: hrnet_w44
      Epochs: 100
      Layers: 44
      Crop Pct: '0.875'
      Momentum: 0.9
      Batch Size: 256
      Image Size: '224'
      Weight Decay: 0.001
      Interpolation: bilinear
    Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/hrnet.py#L820
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnetv2_w44-c9ac8c18.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 78.89%
        Top 5 Accuracy: 94.37%
  - Name: hrnet_w48
    In Collection: HRNet
    Metadata:
      FLOPs: 22285865760
      Parameters: 77470000
      File Size: 310603710
      Architecture:
      - Batch Normalization
      - Convolution
      - ReLU
      - Residual Connection
      Tasks:
      - Image Classification
      Training Techniques:
      - Nesterov Accelerated Gradient
      - Weight Decay
      Training Data:
      - ImageNet
      Training Resources: 4x NVIDIA V100 GPUs
      Training Time: 80 hours
      ID: hrnet_w48
      Epochs: 100
      Layers: 48
      Crop Pct: '0.875'
      Momentum: 0.9
      Batch Size: 256
      Image Size: '224'
      Weight Decay: 0.001
      Interpolation: bilinear
    Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/hrnet.py#L825
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnetv2_w48-abd2e6ab.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 79.32%
        Top 5 Accuracy: 94.51%
  - Name: hrnet_w64
    In Collection: HRNet
    Metadata:
      FLOPs: 37239321984
      Parameters: 128060000
      File Size: 513071818
      Architecture:
      - Batch Normalization
      - Convolution
      - ReLU
      - Residual Connection
      Tasks:
      - Image Classification
      Training Techniques:
      - Nesterov Accelerated Gradient
      - Weight Decay
      Training Data:
      - ImageNet
      Training Resources: 4x NVIDIA V100 GPUs
      ID: hrnet_w64
      Epochs: 100
      Layers: 64
      Crop Pct: '0.875'
      Momentum: 0.9
      Batch Size: 256
      Image Size: '224'
      Weight Decay: 0.001
      Interpolation: bilinear
    Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/hrnet.py#L830
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnetv2_w64-b47cc881.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 79.46%
        Top 5 Accuracy: 94.65%
  -->