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https://github.com/bioinf-jku/TTUR

Two time-scale update rule for training GANs
https://github.com/bioinf-jku/TTUR

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Two time-scale update rule for training GANs

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# Two time-scale update rule for training GANs



This repository contains code accompanying the paper [GANs Trained by a Two Time-Scale Update Rule

Converge to a Local Nash Equilibrium](https://arxiv.org/abs/1706.08500).



## Fréchet Inception Distance (FID)

The FID is the performance measure used to evaluate the experiments in the paper. There, a detailed description can be found

in the experiment section as well as in the the appendix in section A1.



In short:

The Fréchet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1) and X_2 ~ N(mu_2, C_2) is



                       d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)).



The FID is calculated by assuming that X_1 and X_2 are the activations of the coding layer pool_3 of the inception model (see below) for generated samples and real world samples respectivly. mu_n is the mean and C_n the covariance of the activations of the coding layer over all real world or generated samples.



IMPORTANT: The number of samples to calculate the Gaussian statistics (mean and covariance) should be greater than the

dimension of the coding layer, here 2048 for the Inception pool 3 layer. Otherwise the covariance is not full rank resulting in complex numbers and nans by calculating the square root.



We recommend using a minimum sample size of 10,000 to calculate the FID otherwise the true FID of the generator is

underestimated.



### Compatibility notice

Previous versions of this repository contained two implementations to calculate the FID, a "unbatched" and a "batched" version.

The "unbatched" version should not be used anymore. If you've downloaded this code previously, please update it immediately to

the new version. The old version included a bug!



## A pytorch implementation of the FID

If you're looking for a pytorch implementation we recommend https://github.com/mseitzer/pytorch-fid



## Provided Code



Requirements: TF 1.1+, Python 3.x



#### fid.py

This file contains the implementation of all necessary functions to calculate the FID. It can be used either

as a python module imported into your own code, or as a standalone

script to calculate the FID between precalculated (training set) statistics and a directory full of images, or between

two directories of images.



To compare directories with pre-calculated statistics (e.g. the ones from http://bioinf.jku.at/research/ttur/), use:



    fid.py /path/to/images /path/to/precalculated_stats.npz



To compare two directories, use



    fid.py /path/to/images /path/to/other_images



See `fid.py --help` for more details.



#### fid_example.py

Example code to show the usage of `fid.py` in your own Python scripts.



#### precalc_stats_example.py

Example code to show how to calculate and save training set statistics.



#### WGAN_GP

Improved WGAN (WGAN-GP) implementation forked from https://github.com/igul222/improved_wgan_training

with added FID evaluation for the image model and switchable TTUR/orig settings. Lanuage model with

JSD Tensorboard logging and switchable TTUR/orig settings.



## Precalculated Statistics for FID calculation



Precalculated statistics for datasets

- [cropped CelebA](http://bioinf.jku.at/research/ttur/ttur_stats/fid_stats_celeba.npz) (64x64, calculated on all samples)

- [LSUN bedroom](http://bioinf.jku.at/research/ttur/ttur_stats/fid_stats_lsun_train.npz) (calculated on all training samples)

- [CIFAR 10](http://bioinf.jku.at/research/ttur/ttur_stats/fid_stats_cifar10_train.npz) (calculated on all training samples)

- [SVHN](http://bioinf.jku.at/research/ttur/ttur_stats/fid_stats_svhn_train.npz) (calculated on all training samples)

- [ImageNet Train](http://bioinf.jku.at/research/ttur/ttur_stats/fid_stats_imagenet_train.npz) (calculated on all training samples)

- [ImageNet Valid](http://bioinf.jku.at/research/ttur/ttur_stats/fid_stats_imagenet_valid.npz) (calculated on all validation samples)



are provided at: http://bioinf.jku.at/research/ttur/



## Additional Links



For FID evaluation download the Inception modelf from http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz



The cropped CelebA dataset can be downloaded here http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html



To download the LSUN bedroom dataset go to: http://www.yf.io/p/lsun



The 64x64 downsampled ImageNet training and validation datasets can be found here http://image-net.org/small/download.php