clean-fid 0.1.1

standardise the FID

clean-fid: Fixing Inconsistencies in FID

Project | Paper

The FID calculation involves many steps that can produce inconsistencies in the final metric. As shown below, different implementations use different low-level image quantization and resizing functions, the latter of which are often implemented incorrectly. We provide this library to address the issues found and make the FID values comparable across different methods, papers, and groups.

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On Buggy Resizing Libraries and Surprising Subtleties in FID Calculation
Gaurav Parmar, Richard Zhang, Jun-Yan Zhu
In arXiv 1811.10959
CMU and Adobe

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Buggy Resizing Operations

Resizing operation is often implemented incorrectly by popular libraries.

JPEG Image Compression

Image compression can have a surprisingly large effect on FID. Images are perceptually indistinguishable from each other but have a large FID score. The FID scores under the images are calculated between all FFHQ images saved using the corresponding JPEG format and the PNG format.

Below, we study the effect of JPEG compression for StyleGAN2 models trained on the FFHQ dataset (left) and LSUN outdoor Church dataset (right). Note that LSUN dataset images were collected with JPEG compression (quality 75), whereas FFHQ images were collected as PNG. Interestingly, for LSUN dataset, the best FID score (3.48) is obtained when the generated images are compressed with JPEG quality 87.

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Quick Start

• install requirements

  pip install -r requirements.txt
  

• install the library

  pip install clean-fid
  

• FID between two image folders

  from cleanfid import fid
  
  score = fid.compare_folders(fdir1, fdir2, num_workers=0,
              batch_size=8, device=torch.device("cuda"),
              use_legacy_pytorch=False,
              use_legacy_tensorflow=False,)
  

• FID of a folder of generated images

  from cleanfid import fid
  
  score = fid.fid_folder(fdir, dataset_name="FFHQ", dataset_res=1024,
             model=None, use_legacy_pytorch=False,
             use_legacy_tensorflow=False, num_workers=12,
             batch_size=128, device=torch.device("cuda"))
  

• FID inline

  from cleanfid import fid
  
  # function that accepts a latent and returns an image in range[0,255]
  gen = lambda z: return GAN(latent=z, ... , <other_flags>)
  
  fid_score = fid.fid_model(gen, dataset_name="FFHQ, dataset_res=1024,
            model=None, z_dim=512, num_fid=50_000,
            use_legacy_pytorch=False, use_legacy_tensorflow=False,
            num_workers=0, batch_size=128,
            device=torch.device("cuda"))
  

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Make Custom Dataset Statistics

• dataset_path: folder where the dataset images are stored
• Generate and save the inception statistics

  import numpy as np
  from cleanfid import fid
  dataset_path = ...
  mu, sigma = fid.get_folder_features(dataset_path, num=50_000)
  np.savez_compressed("stats.npz", mu=mu, sigma=sigma)
  

• See examples/ffhq_stats.py for a concrete example

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Backwards Compatibility

We provide two flags to reproduce the legacy FID score.

• use_legacy_pytorch
  This flag is equivalent to using the popular PyTorch FID implementation provided here
  The difference between using CleanFID with use_legacy_pytorch flag and code is ~1.9e-06
  See doc for how the methods are compared

• use_legacy_tensorflow
  This flag is equivalent to using the official implementation of FID released by the authors. To use this flag, you need to additionally install tensorflow.

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CleanFID Leaderboard for common tasks

FFHQ @ 1024x1024

Model	Legacy-FID	Clean-FID
StyleGAN2	2.85 ± 0.05	3.08 ± 0.05
StyleGAN	4.44 ± 0.04	4.82 ± 0.04
MSG-GAN	6.09 ± 0.04	6.58 ± 0.06

Image-to-Image (horse->zebra @ 256x256) Computed using test images

Model	Legacy-FID	Clean-FID
CycleGAN	77.20	75.17
CUT	45.51	43.71

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Building from source

python setup.py bdist_wheel
pip install dist/*

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Citation

If you find this repository useful for your research, please cite the following work.

@article{parmar2021cleanfid,
  title={On Buggy Resizing Libraries and Surprising Subtleties in FID Calculation},
  author={Parmar, Gaurav and Zhang, Richard and Zhu, Jun-Yan},
  journal={arXiv preprint arXiv:2104.11222},
  year={2021}
}

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Credits

PyTorch-StyleGAN2: code | License

PyTorch-FID: code | License

StyleGAN2: code | LICENSE

converted FFHQ weights: code | License