Pytorch implementation of DCGAN with high extensibility. You can easily extend this code to add more models and datasets.
- Python 3
- Pytorch 0.4.0
The default config in config.py is set for 'mnist' dataset.
To train the dcgan on 'mnist':
python main.py
To train the dcgan on 'celeba':
python main.py --dataset celeba --dataset_root [celeba dataset folder] --c_dim 3 --height 64 --width 64
To train the dcgan on your own dataset:
- implement a dataset loader file in ./datasets/
- modify
get_data_loader()function inutils.py - train the model
PS: height and width must be a multiple of 16, see config.py to find more details.
Generate images with trained model.
If 'mnist' dataset:
python main.py --mode test --generator_restored ./results/checkpoints/Generator-final.pt
If 'celeba' dataset or your own dataset:
python main.py --mode test --c_dim [c] --height [h] --width [w] --generator_restored [model path]
