hunk.code_window: Hacked together by / Copyright 2020 Ross Wightman
   """
   import csv
   import os
   import wandb
   from collections import OrderedDict
  
hunk.labels: keep keep keep keep keep replace keep
hunk.file_path: timm/utils/summary.py
hunk.type: replace
file: # (Tensorflow) Inception v3
  
  **Inception v3** is a convolutional neural network architecture from the Inception family that makes several improvements including using [Label Smoothing](https://paperswithcode.com/method/label-smoothing), Factorized 7 x 7 convolutions, and the use of an [auxiliary classifer](https://paperswithcode.com/method/auxiliary-classifier) to propagate label information lower down the network (along with the use of batch normalization for layers in the sidehead). The key building block is an [Inception Module](https://paperswithcode.com/method/inception-v3-module).
  
  The weights from this model were ported from [Tensorflow/Models](https://github.com/tensorflow/models).
  
  ## How do I use this model on an image?
  To load a pretrained model:
  
  ```python
  import timm
  model = timm.create_model('tf_inception_v3', 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. `tf_inception_v3`. 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('tf_inception_v3', 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
  @article{DBLP:journals/corr/SzegedyVISW15,
    author    = {Christian Szegedy and
                 Vincent Vanhoucke and
                 Sergey Ioffe and
                 Jonathon Shlens and
                 Zbigniew Wojna},
    title     = {Rethinking the Inception Architecture for Computer Vision},
    journal   = {CoRR},
    volume    = {abs/1512.00567},
    year      = {2015},
    url       = {http://arxiv.org/abs/1512.00567},
    archivePrefix = {arXiv},
    eprint    = {1512.00567},
    timestamp = {Mon, 13 Aug 2018 16:49:07 +0200},
    biburl    = {https://dblp.org/rec/journals/corr/SzegedyVISW15.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }
  ```
  
  <!--
  Type: model-index
  Collections:
  - Name: TF Inception v3
    Paper:
      Title: Rethinking the Inception Architecture for Computer Vision
      URL: https://paperswithcode.com/paper/rethinking-the-inception-architecture-for
  Models:
  - Name: tf_inception_v3
    In Collection: TF Inception v3
    Metadata:
      FLOPs: 7352418880
      Parameters: 23830000
      File Size: 95549439
      Architecture:
      - 1x1 Convolution
      - Auxiliary Classifier
      - Average Pooling
      - Average Pooling
      - Batch Normalization
      - Convolution
      - Dense Connections
      - Dropout
      - Inception-v3 Module
      - Max Pooling
      - ReLU
      - Softmax
      Tasks:
      - Image Classification
      Training Techniques:
      - Gradient Clipping
      - Label Smoothing
      - RMSProp
      - Weight Decay
      Training Data:
      - ImageNet
      Training Resources: 50x NVIDIA Kepler GPUs
      ID: tf_inception_v3
      LR: 0.045
      Dropout: 0.2
      Crop Pct: '0.875'
      Momentum: 0.9
      Image Size: '299'
      Interpolation: bicubic
    Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/inception_v3.py#L449
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_inception_v3-e0069de4.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 77.87%
        Top 5 Accuracy: 93.65%
  -->