mvisionai/DCGAN-LSGAN-WGAN-GP-DRAGAN-Tensorflow-2

DCGAN LSGAN WGAN-GP DRAGAN Tensorflow 2

PythonMIT

Recommendation

Our GAN based work for facial attribute editing - AttGAN.

News

8 April 2019: We re-implement these GANs by Tensorflow 2! The old version is here: v1 or in the "v1" directory.
PyTorch Version

GANs - Tensorflow 2

Tensorflow 2 implementations of DCGAN, LSGAN, WGAN-GP and DRAGAN.

Exemplar results

Fashion-MNIST

DCGAN	LSGAN	WGAN-GP	DRAGAN

CelebA

DCGAN	LSGAN

WGAN-GP	DRAGAN

Anime

WGAN-GP	DRAGAN

Usage

Environment
- Python 3.6
- TensorFlow 2.2, TensorFlow Addons 0.10.0
- OpenCV, scikit-image, tqdm, oyaml
- we recommend Anaconda or Miniconda, then you can create the TensorFlow 2.2 environment with commands below
```
conda create -n tensorflow-2.2 python=3.6

source activate tensorflow-2.2

conda install scikit-image tqdm tensorflow-gpu=2.2

conda install -c conda-forge oyaml

pip install tensorflow-addons==0.10.0
```
- NOTICE: if you create a new conda environment, remember to activate it before any other command
```
source activate tensorflow-2.2
```
Datasets
- Fashion-MNIST will be automatically downloaded
- CelebA should be prepared by yourself in ./data/img_align_celeba/*.jpg
  - dataset link (find "img_align_celeba.zip"):
    - Baidu Netdisk (password rp0s) or
    - Google Drive
- the Anime dataset should be prepared by yourself in ./data/faces/*.jpg
  - dataset link: https://www.kaggle.com/splcher/animefacedataset

Examples of training

Fashion-MNIST DCGAN

CUDA_VISIBLE_DEVICES=0 python train.py --dataset=fashion_mnist --epoch=25 --adversarial_loss_mode=gan

CelebA DRAGAN

CUDA_VISIBLE_DEVICES=0 python train.py --dataset=celeba --epoch=25 --adversarial_loss_mode=gan --gradient_penalty_mode=dragan

Anime WGAN-GP

CUDA_VISIBLE_DEVICES=0 python train.py --dataset=anime --epoch=200 --adversarial_loss_mode=wgan --gradient_penalty_mode=wgan-gp --n_d=5

see more training exampls in commands.sh

tensorboard for loss visualization

tensorboard --logdir ./output/fashion_mnist_gan/summaries --port 6006