SNGAN.pytorch

An unofficial Pytorch implementation of Spectral Normalization for Generative Adversarial Networks. For official Chainer implementation please refer to https://github.com/pfnet-research/sngan_projection

Our implementation achieves Inception score of 8.21 and FID score of 14.21 on unconditional CIFAR-10 image generation task. In comparison, the original paper claims 8.22 and 21.7 respectively.

Set-up

install libraries:

pip install -r requirements.txt

prepare fid statistic file

mkdir fid_stat

Download the pre-calculated statistics for CIFAR10, fid_stats_cifar10_train.npz, to ./fid_stat.

train

sh exps/sngan_cifar10.sh

test

mkdir pre_trained

Download the pre-trained SNGAN model sngan_cifar10.pth to ./pre_trained. Run the following script:

sh exps/eval.sh

Acknowledgement

1. Inception Score code from OpenAI's Improved GAN (official).
2. FID code and statistics file from https://github.com/bioinf-jku/TTUR (official).
3. The code of Spectral Norm GAN is inspired by https://github.com/pfnet-research/sngan_projection (official).