News
- 28 June 2019: We re-implement these GANs by Pytorch 1.1! The old version is here: v0 or in the "v0" directory.
- Tensorflow 2 Version
GANs - Pytorch
Pytorch implementations of DCGAN, LSGAN, WGAN-GP(LP) and DRAGAN.
Exemplar results
Fashion-MNIST
| DCGAN | LSGAN | WGAN-GP | DRAGAN |
|---|---|---|---|
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CelebA
| DCGAN | LSGAN |
|---|---|
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| WGAN-GP | DRAGAN |
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| WGAN-LP | DRAGAN-LP |
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Anime
| WGAN-GP | DRAGAN |
|---|---|
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Usage
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Prerequisites
- PyTorch 1.1
- tensorboardX
- scikit-image, oyaml, tqdm
- Python 3.6
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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: Dropbox
- the above link might be inaccessible, the alternatives are (find "img_align_celeba.zip")
- the Anime dataset should be prepared by yourself in ./data/faces/*.jpg
- dataset link: https://pan.baidu.com/s/1eSifHcA, password: g5qa
- reference: https://zhuanlan.zhihu.com/p/24767059
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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=1-gp --gradient_penalty_sample_mode=dragan -
Anime WGAN-GP
CUDA_VISIBLE_DEVICES=0 python train.py --dataset=anime --epoch=200 --adversarial_loss_mode=wgan --gradient_penalty_mode=1-gp --gradient_penalty_sample_mode=line --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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