Official code of Reparameterized Sampling for Generative Adversarial Networks [ECML-PKDD 2021 Best ML paper]
Authors: Yifei Wang, Yisen Wang, Jiansheng Yang, and Zhouchen Lin (Peking University)
What for: boostrapping the sample quality of pretrained GANs with latent-sample coupling MCMC
Install dependencies with pip install -r requirements.txt
To draw samples with REPGAN, we load a pretrained GAN on CIFAR-10 and run the code
python main.py --dataroot [dataroot] --load-g [generator filename] --load-d [discriminator filename] --calibrate --num-images 50000
It will generate 50,000 images and save them in the numpy format.
Notice: here we take DCGAN in dcgan.py for an example. Other architectures (including WGAN) can also be adapted to fit our algorithm as it is model agnostic.
To intergrate REPGAN in your code, you can directly use / modify the repgan function in repgan.py, where it takes GANs as input and return a batch of samples. Detailed descriptions:
def repgan(netG,
netD,
calibrator,
device,
nz=100,
batch_size=100,
clen=640,
tau=0.01,
eta=0.1):
'''
1) network config
netG: generator network. Input: latent (B x latent_dim x 1 x 1). Output: images (B x C x H x W)
netD: discriminator network. Input: images (B x C x H x W). Output: raw score (B x 1)
calibrator: calibrator network for calibrating the discriminator score. Input: raw score (B x 1). Ouput: calibrated score: (B x 1)
nz: the dimension of the latent z of the generator
2) sampling config
batch_size: number of samples per batch
clen: length the Markov chain (only the last sample at the end of the chain is left)
tau: step size in L2MC
eta: scale of white noise in L2MC. Default: sqrt(tau)
3) update rule
- (a) Langevin. z' = zk + tau/2 * grad + eta * epsilon
- (b) MH test. Calculate alpha, and flip a coin with probability alpha.
'''
If you find this codebase helpful, please cite
@inproceedings{wang2021reparameterized,
title={Reparameterized Sampling for Generative Adversarial Networks},
author={Wang, Yifei and Wang, Yisen and Yang, Jiansheng and Lin, Zhouchen},
booktitle={Joint European Conference on Machine Learning and Knowledge Discovery in Databases},
pages={494--509},
year={2021},
organization={Springer}
}
DCGAN example https://github.com/pytorch/examples/blob/master/dcgan/main.py
MHGAN code https://github.com/uber-research/metropolis-hastings-gans
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