Awesome
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
<p align="center"> GANs - Tensorflow 2 </p>
Tensorflow 2 implementations of DCGAN, LSGAN, WGAN-GP and DRAGAN.
Exemplar results
Fashion-MNIST
| DCGAN | LSGAN | WGAN-GP | DRAGAN |
|---|---|---|---|
| <img src="./pics/fashion-mnist_gan.jpg"> | <img src="./pics/fashion-mnist_lsgan.jpg"> | <img src="./pics/fashion-mnist_wgan-gp.jpg"> | <img src="./pics/fashion-mnist_dragan.jpg"> |
CelebA
| DCGAN | LSGAN |
|---|---|
| <img src="./pics/celeba_gan.jpg"> | <img src="./pics/celeba_lsgan.jpg"> |
| WGAN-GP | DRAGAN |
| <img src="./pics/celeba_wgan-gp.jpg"> | <img src="./pics/celeba_dragan.jpg"> |
Anime
| WGAN-GP | DRAGAN |
|---|---|
| <img src="./pics/anime_wgan-gp.jpg"> | <img src="./pics/anime_dragan.jpg"> |
Usage
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Environment
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Python 3.6
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TensorFlow 2.2, TensorFlow Addons 0.10.0
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OpenCV, scikit-image, tqdm, oyaml
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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
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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
- dataset link (find "img_align_celeba.zip"):
- the Anime dataset should be prepared by yourself in ./data/faces/*.jpg
- dataset link: https://www.kaggle.com/splcher/animefacedataset
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Examples of training
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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
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tensorboard for loss visualization
tensorboard --logdir ./output/fashion_mnist_gan/summaries --port 6006
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