pi-GAN

A pytorch implementation of pi-GAN, for 3d-aware image synthesis, following the paper pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis [Home page], Chan et al., Arxiv 2020 | [bibtex]

This pi-GAN implementation is partly based on the the version of lucidrains/pi-GAN-pytorch: Implementation of π-GAN, for 3d-aware image synthesis, in Pytorch (github.com)

Requirements

• python 3.6+
• numpy
• pytorch 1.7+ & cuda

How to run

1. You need to first configure paths in config.py

   test_save_dir: where test results are saved (every 100 iterations).

   data_dir: containing images as training data.

   cache_dir: where dataset are cached so that it will be loaded faster next time training. Otherwise it could take an unbearably long time to read thousands of images every time.

   ckpt_dir: where the check point files are saved (every 1000 iterations).

2. To start a train, You may look into the train.py

   from pi_gan import *
   from data import dataset
   
   # Use the piGAN class. Specify the GPU ids
   model = piGAN(dataset, devices_ids=[0, 1])
   
   # if you have a check point, load it here
   model.load_ckpt('[YOUR CKPT PATH]')
   
   # Train
   model.train(40000) # train for 40000 iterations 

run.ipynb is the same as all of the .py files. You may either run pi-GAN in the notebook which could be convenient for debugging.