Awesome
GANs with spectral normalization and projection discriminator
This is an unofficial PyTorch implementation of sngan_projection
Dependencies:
- PyTorch1.0
- numpy
- scipy
- tensorboardX
- tqdm
- torchviz pip install torchviz and graphviz sudo apt-get install graphviz
Usage:
There are two ways to run the training script:
- Run the script directly (We recommend this way):
python3 main.py
orpython main.py
. In this way, the training parameters can be modified by modifying theparameter.py
parameter defaults.
Parameters
Parameters | Function |
---|---|
--version | Experiment name |
--train | Set the model stage, Ture---training stage; False---testing stage |
--experiment_description | Descriptive text for this experiment |
--total_step | Totally training step |
--batch_size | Batch size |
--g_lr | Learning rate of generator |
--d_lr | Learning rate of discriminator |
--parallel | Enable the parallel training |
--dataset | Set the dataset name,lsun,celeb,cifar10 |
--cuda | Set GPU device number |
--image_path | The root dir to training dataset |
--FID_mean_cov | The root dir to dataset moments npz file |
Results
We have reproduced the FID (in Cifar-10, best result is FID=17.2) result reported in the paper.
The convergence curve of FID is as follows:
CIFAR10 results
200K:
500K:
600K:
800K:
1000K: