Awesome
Improved Training of Wasserstein GANs in Pytorch
This is a Pytorch implementation of gan_64x64.py from Improved Training of Wasserstein GANs.
To do:
- Support parameters in cli *
- Add requirements.txt *
- Add Dockerfile if possible
- Multiple GPUs *
- Clean up code, remove unused code *
* not ready for conditional gan yet
Run
- Example:
Fresh training
CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py --train_dir /path/to/train --validation_dir /path/to/validation/ --output_path /path/to/output/ --dim 64 --saving_step 300 --num_workers 8
Continued training:
CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py --train_dir /path/to/train --validation_dir /path/to/validation/ --output_path /path/to/output/ --dim 64 --saving_step 300 --num_workers 8 --restore_mode --start_iter 5000
Model
train.py: This model is mainly based onGoodGeneratorandGoodDiscriminatorofgan_64x64.pymodel from Improved Training of Wasserstein GANs. It has been trained on LSUN dataset for around 100k iters.congan_train.py: ACGAN implementation, trained on 4 classes of LSUN dataset
Result
1. WGAN: trained on bedroom dataset (100k iters)
| Sample 1 | Sample 2 |
|---|---|
 |  |
2. ACGAN: trained on 4 classes (100k iters)
- dining_room: 1
- bridge: 2
- restaurant: 3
- tower: 4
| Sample 1 | Sample 2 |
|---|---|
 |  |
Testing
During the implementation of this model, we built a test module to compare the result between original model (Tensorflow) and our model (Pytorch) for every layer we implemented. It is available at compare-tensorflow-pytorch
TensorboardX
Results such as costs, generated images (every 200 iters) for tensorboard will be written to ./runs folder.
To display the results to tensorboard, run: tensorboard --logdir runs