This project has been renamed to pytorch-image-generation-metrics. Please visit the new repository here.

Pytorch Implementation of Common GAN metrics

Install

pip install pytorch-gan-metrics

• torch>=1.8.2
• torchvision>=0.9.2

Quick Start

from pytorch_gan_metrics import get_inception_score, get_fid

images = ... # [N, 3, H, W] normalized to [0, 1]
IS, IS_std = get_inception_score(images)        # Inception Score
FID = get_fid(images, 'path/to/statistics.npz') # Frechet Inception Distance

path/to/statistics.npz is compatiable with official FID implementation.

Notes

The FID implementation is inspired from pytorch-fid.

This repository is developed for personal research. If you think this package can also benefit your life, please feel free to open issues.

Features

• Currently, this package supports following metrics:
  • Inception Score (IS)
  • Fréchet Inception Distance (FID)
• The computation procedure of IS and FID are integrated to avoid multiple forward propagations.
• Support reading images on the fly to avoid out of memory especially for large scale images.
• Support computation on GPU to speed up some cpu operations such as np.cov and scipy.linalg.sqrtm.

Reproducing Results of Official Implementations on CIFAR-10

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

Documentation

Prepare Statistics (for FID)

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

  python -m pytorch_gan_metrics.calc_fid_stats \
      --path path/to/images \
      --stats path/to/statistics.npz
  

  See calc_fid_stats.py for details.

Inception Features

• When getting IS or FID, the InceptionV3 will be loaded into torch.device('cuda:0') if GPU is availabel; Otherwise, torch.device('cpu') will be used.
• Change device argument in get_* functions to set torch device.

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 to Provide Images

• Use pytorch_gan_metrics.ImageDataset to collect images on your storage or use your custom torch.utils.data.Dataset.

  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')
  

Load Images from a Directory

• Calculate metrics for images in a directory and its subfolders.

  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', 'path/to/statistics.npz')
  (IS, IS_std), FID = get_inception_score_and_fid_from_directory(
      'path/to/images', 'path/to/statistics.npz')
  

Accelerating Matrix Computation by PyTorch

• Set use_torch=True when calling functions get_* such as get_inception_score, get_fid, etc.

• WARNING when use_torch=True is used, the FID might be nan due to the unstable implementation of matrix sqrt.

• This option is recommended to be used when evaluating generative models on a server which is equipped with high efficiency GPUs while the cpu frequency is low.

Tested Versions

• python 3.9 + torch 1.8.2 + CUDA 10.2
• python 3.9 + torch 1.11.0 + CUDA 10.2
• python 3.9 + torch 1.12.1 + CUDA 10.2

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