/pytorch-wgan

Pytorch implementation of DCGAN, WGAN-CP, WGAN-GP

Primary LanguagePythonMIT LicenseMIT

This repository was forked from https://github.com/Zeleni9/pytorch-wgan for the purpose of using WGAN in the context of training image based inversion.

Pytorch code for GAN models

This is the pytorch implementation of 3 different GAN models using same convolutional architecture.

  • DCGAN (Deep convolutional GAN)
  • WGAN-CP (Wasserstein GAN using weight clipping)
  • WGAN-GP (Wasserstein GAN using gradient penalty)

Dependecies

The prominent packages are:

  • numpy
  • scikit-learn
  • tensorflow 2.5.0
  • pytorch 1.8.1
  • torchvision 0.9.1

To install all the dependencies quickly and easily you should use pip

pip install -r requirements.txt

Training

Running training of WGAN-GP model on 128x128 extractions from ti/zahner.png :

python main.py --model WGAN-GP-128 \
               --is_train True \
               --dataset ti_sampler \
               --ti_file zahner.png \
               --cuda True \
               --batch_size 64 \
               --dataroot ti

This training took around 4h 21 min on a single GPU.

Useful Resources