Gradient Normalization for Generative Adversarial Networks

Yi-Lun Wu, Hong-Han Shuai, Zhi-Rui Tam, Hong-Yu Chiu

Paper: https://arxiv.org/abs/2109.02235

This is the official implementation of Gradient Normalized GAN (GN-GAN).

Requirements

• Python 3.8.9
• Python packages

  # update `pip` for installing tensorboard.
  pip install -U pip setuptools
  pip install -r requirements.txt

Datasets

• CIFAR-10

  Pytorch build-in CIFAR-10 will be downloaded automatically.

• STL-10

  Pytorch build-in STL-10 will be downloaded automatically.

• CelebA-HQ 128/256

  We obtain celeba-hq from this repository and preprocess it into lmdb file.

  • 256x256

    python dataset.py path/to/celebahq/256 ./data/celebahq/256
    

  • 128x128

    We split data into train test splits by filenames, the test set contains images from 27001.jpg to 30000.jpg.

    python dataset.py path/to/celebahq/128/train ./data/celebahq/128
    

  The folder structure:

  ./data/celebahq
  ├── 128
  │   ├── data.mdb
  │   └── lock.mdb
  └── 256
      ├── data.mdb
      └── lock.mdb
  

• LSUN Church Outdoor 256x256 (training set)

  The folder structure:

  ./data/lsun/church/
  ├── data.mdb
  └── lock.mdb
  

Preprocessing Datasets for FID

Pre-calculated statistics for FID can be downloaded here:

• cifar10.train.npz - Training set of CIFAR10
• cifar10.test.npz - Testing set of CIFAR10
• stl10.unlabeled.48.npz - Unlabeled set of STL10 in resolution 48x48
• celebahq.3k.128.npz - Last 3k images of CelebA-HQ 128x128
• celebahq.all.256.npz - Full dataset of CelebA-HQ 256x256
• church.train.256.npz - Training set of LSUN Church Outdoor

Folder structure:

./stats
├── celebahq.3k.128.npz
├── celebahq.all.256.npz
├── church.train.256.npz
├── cifar10.test.npz
├── cifar10.train.npz
└── stl10.unlabeled.48.npz

NOTE

All the reported values (Inception Score and FID) in our paper are calculated by official implementation instead of our implementation.

Training

• Configuration files

  • We use absl-py to parse, save and reload the command line arguments.

  • All the configuration files can be found in ./config.

  • The compatible configuration list is shown in the following table:

    Script	Configurations	Multi-GPU
    train.py	GN-GAN_CIFAR10_CNN.txt GN-GAN_CIFAR10_RES.txt GN-GAN_CIFAR10_BIGGAN.txt GN-GAN_STL10_CNN.txt GN-GAN_STL10_RES.txt GN-GAN-CR_CIFAR10_CNN.txt GN-GAN-CR_CIFAR10_RES.txt GN-GAN-CR_CIFAR10_BIGGAN.txt GN-GAN-CR_STL10_CNN.txt GN-GAN-CR_STL10_RES.txt
    train_ddp.py	GN-GAN_CELEBAHQ128_RES.txt GN-GAN_CELEBAHQ256_RES.txt GN-GAN_CHURCH256_RES.txt	✔️

• Run the training script with the compatible configuration, e.g.,

  • train.py supports training gan on CIFAR10 and STL10, e.g.,

    python train.py \
        --flagfile ./config/GN-GAN_CIFAR10_RES.txt

  • train_ddp.py is optimized for multi-gpu training, e.g.,

    CUDA_VISIBLE_DEVICES=0,1,2,3 python train_ddp.py \
        --flagfile ./config/GN-GAN_CELEBAHQ256_RES.txt
    

• Generate images from checkpoints, e.g.,

  --eval: evaluate best checkpoint.

  --save PATH: save the generated images to PATH

  python train.py \
      --flagfile ./logs/GN-GAN_CIFAR10_RES/flagfile.txt \
      --eval \
      --save path/to/generated/images
  

How to integrate Gradient Normalization into your work?

The function normalize_gradient is implemented based on torch.autograd module, which can easily normalize your forward propagation of discriminator by updating a single line.

from torch.nn import BCEWithLogitsLoss
from models.gradnorm import normalize_gradient

net_D = ...     # discriminator
net_G = ...     # generator
loss_fn = BCEWithLogitsLoss()

# Update discriminator
x_real = ...                                    # real data
x_fake = net_G(torch.randn(64, 3, 32, 32))      # fake data
pred_real = normalize_gradient(net_D, x_real)   # net_D(x_real)
pred_fake = normalize_gradient(net_D, x_fake)   # net_D(x_fake)
loss_real = loss_fn(pred_real, torch.ones_like(pred_real))
loss_fake = loss_fn(pred_fake, torch.zeros_like(pred_fake))
(loss_real + loss_fake).backward()              # backward propagation
...

# Update generator
x_fake = net_G(torch.randn(64, 3, 32, 32))      # fake data
pred_fake = normalize_gradient(net_D, x_fake)   # net_D(x_fake)
loss_fake = loss_fn(pred_fake, torch.ones_like(pred_fake))
loss.backward()                                 # backward propagation
...

Citation

If you find our work is relevant to your research, please cite:

@InProceedings{GNGAN_2021_ICCV,
    author = {Yi-Lun Wu, Hong-Han Shuai, Zhi Rui Tam, Hong-Yu Chiu},
    title = {Gradient Normalization for Generative Adversarial Networks},
    booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
    month = {Oct},
    year = {2021}
}