Skip to content

Code for the paper "FusedProp: Towards Efficient Training of Generative Adversarial Networks"

License

Notifications You must be signed in to change notification settings

zplizzi/fusedprop

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

3 Commits
 
 
 
 
 
 
 
 

Repository files navigation

FusedProp

Read the paper here.

Installation

  • Clone this repository
  • Install pip packages:
    • torch
    • torchvision
    • tensorflow (for FID/IS calculation)
    • wandb
    • numpy
    • scipy
    • tqdm
    • imageio
  • For Weights and Biases (wandb), follow install/setup instructions here. If you want to disable wandb, you can set the env variable WANDB_MODE=dryrun, although there is no alternate logging.
  • Download the FID statistics of the test set following the instructions here. Downlaod to [DATA_ROOT]/fid_stat/fid_stats_[DATASET]_train.npz, where DATA_ROOT defaults to ..

Usage:

Train a model by running a command a command similar to the following from the root directory of this repository: python -m gr_gan.launch --dataset=cifar10 --model=resnet --loss=nonsaturating --lr=.0001 --lr_dis=.0004 --z_dim=128 --batch_size=64 --train_fn=baseline --evaluate_freq=5000 --iterations=100000 --log_freq=10 --spectral_norm=True

Descriptions of all command-line options are available by running: python -m gr_gan.trainable --help

About

Code for the paper "FusedProp: Towards Efficient Training of Generative Adversarial Networks"

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published