pytorch-gan-metrics 0.1.0

Package for calculating GAN metrics using Pytorch

Pytorch Implementation of Common GAN metrics

Notes

The FID implementation is inspired from pytorch-fid.

Feature

• Currently, this package supports following metrics:
  • Inception Score (IS)
  • Fréchet Inception Distance (FID)
• The computation processes of IS and FID are integrated to avoid multiple forward propagations.
• Read image on the fly for both metrics.

Reproducing Results of Official Implementations

• CIFAR-10

  	Train IS	Test IS	Train(50k) vs Test(10k) FID
  Official	11.24±0.20	10.98±0.22	3.1508
  pytorch-gan-metrics	11.26±0.27	10.97±0.33	3.1517

  Due to the framework difference between PyTorch and TensorFlow, the results are slightly different from official implementations.

Install

python setup.py install

Prepare Statistics for FID

• Download precalculated statistics for dataset or
• Calculate statistics for your custom dataset using command line tool

  python -m pytorch_gan_metrics.calc_fid_stats --path path/to/images --output name.npz
  

  See calc_fid_stats.py for implementation details.

Documentation

Using torch.Tensor as images

• Prepare images in type torch.float32 with shape [N, 3, H, W] and normalized to [0,1].

  from pytorch_gan_metrics import (get_inception_score,
                                   get_fid,
                                   get_inception_score_and_fid)
  images = ... # [N, 3, H, W]
  assert 0 <= images.min() and images.max() <= 1
  # Inception Score
  IS, IS_std = get_inception_score(images)
  # Frechet Inception Distance
  FID = get_fid(images, 'path/to/statistics.npz')
  # Inception Score + Frechet Inception Distance
  (IS, IS_std), FID = get_inception_score_and_fid(
      images, 'path/to/statistics.npz')
  

Using PyTorch DataLoader

• Use pytorch_gan_metrics.ImageDataset to collect images on disk or use custom dataset which should only return an image in __getitem__.

  from pytorch_gan_metrics import ImageDataset
  
  dataset = ImageDataset(path_to_dir, exts=['png', 'jpg'])
  loader = DataLoader(dataset, batch_size=50, num_workers=4)
  

• It is possible to wrap a generative model in a dataset to support generating images on the fly. Remember to set num_workers=0 to avoid copying models across multiprocess.

  class GeneratorDataset(Dataset):
      def __init__(self, G, z_dim):
          self.G = G
          self.z_dim = z_dim
      
      def __len__(self):
          return 50000
      
      def __getitem__(self, index):
          return self.G(torch.randn(1, self.z_dim).cuda())[0]
  
  dataset = GeneratorDataset(G, z=128)
  loader = DataLoader(dataset, batch_size=50, num_workers=0)
  

• Calculate metrics

  from pytorch_gan_metrics import (get_inception_score,
                                   get_fid,
                                   get_inception_score_and_fid)
  # Inception Score
  IS, IS_std = get_inception_score(loader)
  # Frechet Inception Distance
  FID = get_fid(loader, 'path/to/statistics.npz')
  # Inception Score + Frechet Inception Distance
  (IS, IS_std), FID = get_inception_score_and_fid(
      loader, 'path/to/statistics.npz')
  

From directory

• Calculate metrics for images in the directory.

  from pytorch_gan_metrics import (
      get_inception_score_from_directory,
      get_fid_from_directory,
      get_inception_score_and_fid_from_directory)
  
  IS, IS_std = get_inception_score_from_directory('path/to/images')
  FID = get_fid_from_directory('path/to/images', fid_stats_path)
  (IS, IS_std), FID = get_inception_score_and_fid_from_directory(
      'path/to/images', fid_stats_path)
  

License

This implementation is licensed under the Apache License 2.0.

This implementation is derived from pytorch-fid, licensed under the Apache License 2.0.

FID was introduced by Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler and Sepp Hochreiter in "GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium", see https://arxiv.org/abs/1706.08500

The original implementation is by the Institute of Bioinformatics, JKU Linz, licensed under the Apache License 2.0. See https://github.com/bioinf-jku/TTUR.