hunk.code_window: from contextlib import suppress
   from datetime import datetime
   import wandb 
   import torch
   import torch.nn as nn
   import torchvision.utils
   from torch.nn.parallel import DistributedDataParallel as NativeDDP
  
hunk.labels: keep keep keep replace replace keep keep keep keep
hunk.file_path: train.py
hunk.type: replace
file: # SE-ResNet
  
  **SE ResNet** is a variant of a [ResNet](https://www.paperswithcode.com/method/resnet) that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel-wise feature recalibration.
  
  ## How do I use this model on an image?
  To load a pretrained model:
  
  ```python
  import timm
  model = timm.create_model('seresnet152d', 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. `seresnet152d`. 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('seresnet152d', 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{hu2019squeezeandexcitation,
        title={Squeeze-and-Excitation Networks}, 
        author={Jie Hu and Li Shen and Samuel Albanie and Gang Sun and Enhua Wu},
        year={2019},
        eprint={1709.01507},
        archivePrefix={arXiv},
        primaryClass={cs.CV}
  }
  ```
  
  <!--
  Type: model-index
  Collections:
  - Name: SE ResNet
    Paper:
      Title: Squeeze-and-Excitation Networks
      URL: https://paperswithcode.com/paper/squeeze-and-excitation-networks
  Models:
  - Name: seresnet152d
    In Collection: SE ResNet
    Metadata:
      FLOPs: 20161904304
      Parameters: 66840000
      File Size: 268144497
      Architecture:
      - 1x1 Convolution
      - Batch Normalization
      - Bottleneck Residual Block
      - Convolution
      - Global Average Pooling
      - Max Pooling
      - ReLU
      - Residual Block
      - Residual Connection
      - Softmax
      - Squeeze-and-Excitation Block
      Tasks:
      - Image Classification
      Training Techniques:
      - Label Smoothing
      - SGD with Momentum
      - Weight Decay
      Training Data:
      - ImageNet
      Training Resources: 8x NVIDIA Titan X GPUs
      ID: seresnet152d
      LR: 0.6
      Epochs: 100
      Layers: 152
      Dropout: 0.2
      Crop Pct: '0.94'
      Momentum: 0.9
      Batch Size: 1024
      Image Size: '256'
      Interpolation: bicubic
    Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/resnet.py#L1206
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnet152d_ra2-04464dd2.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 83.74%
        Top 5 Accuracy: 96.77%
  - Name: seresnet50
    In Collection: SE ResNet
    Metadata:
      FLOPs: 5285062320
      Parameters: 28090000
      File Size: 112621903
      Architecture:
      - 1x1 Convolution
      - Batch Normalization
      - Bottleneck Residual Block
      - Convolution
      - Global Average Pooling
      - Max Pooling
      - ReLU
      - Residual Block
      - Residual Connection
      - Softmax
      - Squeeze-and-Excitation Block
      Tasks:
      - Image Classification
      Training Techniques:
      - Label Smoothing
      - SGD with Momentum
      - Weight Decay
      Training Data:
      - ImageNet
      Training Resources: 8x NVIDIA Titan X GPUs
      ID: seresnet50
      LR: 0.6
      Epochs: 100
      Layers: 50
      Dropout: 0.2
      Crop Pct: '0.875'
      Momentum: 0.9
      Batch Size: 1024
      Image Size: '224'
      Interpolation: bicubic
    Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/resnet.py#L1180
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnet50_ra_224-8efdb4bb.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 80.26%
        Top 5 Accuracy: 95.07%
  -->