This is PyTorch implementation of ProgressiveGAN described in paper "Progressive Growing of GANs for Improved Quality, Stability, and Variation".
Work is in progress. Equivalent lr layers still don't work well.
Use config.json
file for set up model before training.
- exp_name - model name
- batch - batch size
- latent - size of latent space vector
- isize - final generating image size
- epochs - number of epochs
- lr_d - lerning rate of discriminator
- lr_g - lerning rate of generator
- lr_decay_epoch - []
- weights - using in
generate.py
script
To begin trainig use train.py
script.
- device_ids - GPU ids. Use list for initialize
- device - use GPU or CPU for training
- data_path - path of dataset
Train on cpu:
python train.py -c config.json -d cpu
Train on 2 gpus:
python train.py -c config.json -d cuda --dev_ids 0 1
To use generator run generate.py
script:
python generate.py -o out/test/ -c config.json -n 20
- Python 3.7.3
- PyTorch 1.7.1
- CUDA 10.1
- CUDNN 7.6.3
[1] https://arxiv.org/abs/1710.10196 [2] https://github.com/nashory/pggan-pytorch