mchong6/FID_IS_infinity

Effectively Unbiased FID and Inception Score and where to find them

This is the PyTorch implementation of Effectively Unbiased FID and Inception Score and where to find them. Note that since the scores are calculated with PyTorch, they are not directly comparable with the numbers obtained from TensorFlow.

Abstract:
Deep Generative Models have been getting a lot of attention in the past few years. However, evaluating how well they perform have thus far been lacking and inconsistent. We show that two commonly used evaluation metrics, Fréchet Inception Distance (FID) and Inception Score (IS), are biased. This bias depends on the number of images we use for calculating the score as well as the generators themselves, making objective comparisons between models difficult. This bias can cause model rankings to change and does not go away by fixing . We thus introduce and , two effectively unbiased metrics evaluated with , and show that we can estimate them via extrapolation. We further make use of Quasi-Monte Carlo integration as a form of variance reduction method to improve the estimates. and are simple drop-in replacements for FID and IS respectively, and allow us to have a fair comparison between different models. Lastly, we show that applying Quasi-Monte Carlo integration for GAN training leads to small improvements.

Dependency

$ pip install -r requirements.txt

How to use

First, generate the inception statistics for the groundtruth dataset to save time for future calculations. To do so, edit the dataloader in score_infinty.py to how you preprocess your data for your generator. Then run

$ python score_infinity.py --path path_to_dataset --out_path output_statistics.npz

Evaluating FID infinity and IS infinity given a fake dataset

To evaluate IS infinity or FID infinity given a pre-generated fake dataset, add this following to your script

from score_infinity import calculate_FID_infinity_path, calculate_IS_infinity_path

FID_infinity = calculate_FID_infinity_path('output_statistics.npz', fake_path, batch_size)
IS_infinity = calculate_IS_infinity_path(fake_path, batch_size)

where fake_path is the path to the folder containing your generated images. Alternatively, you can skip step 1 which precomputes output_statistics.npz and instead call

FID_infinity = calculate_FID_infinity_path(real_path, fake_path, batch_size)

which will recompute the activations for the real dataset every single call.

Evaluating FID infinity and IS infinity given a generator

To evaluate IS infinity or FID infinity given a generator, add this following to your script

from score_infinity import calculate_FID_infinity, calculate_IS_infinity

generator = load_your_generator()

FID_infinity = calculate_FID_infinity(generator, ndim, batch_size, gt_path=output_statistics.npz)
IS_infinity = calculate_IS_infinity(generator, ndim, batch_size)

This script assumes that your generator takes in a z~N(0,1) and outputs an image. It will use scrambled Sobol sequence with inverse CDF by default. Change z_sampler in each function if you wish to change the sampling method.

Using Sobol sequence for training/evaluation GANs

from score_infinity import randn_sampler

# For evaluating
sampler = randn_sampler(128, True)
z = sampler.draw(10) # Generates [10, 128] vector

# For training we need a separate sampler for gen/disc
# use_cache generates Sobol points and then randomly reorders them
gen_sampler = randn_sampler(128, True, use_cache=True)
disc_sampler = randn_sampler(128, True, use_cache=True)

Take a look at the comments in the code to understand the arguments it take.

Citation

If you use this code or ideas from our paper, please cite our paper:

@article{chong2019effectively,
  title={Effectively Unbiased FID and Inception Score and where to find them},
  author={Chong, Min Jin and Forsyth, David},
  journal={arXiv preprint arXiv:1911.07023},
  year={2019}
}

Acknowledgments

This code borrows heavily from BigGAN-PyTorch and pytorch-fid.