neuralchen / SNGAN_Projection

An unofficial PyTorch implementation of SNGAN (ICLR 2018) and cGANs with projection discriminator (ICLR 2018)

GANs with spectral normalization and projection discriminator

This is an unofficial PyTorch implementation of sngan_projection

Miyato, Takeru, and Masanori Koyama. "cGANs with projection discriminator." arXiv preprint arXiv:1802.05637 (2018).

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 or python main.py. In this way, the training parameters can be modified by modifying the parameter.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:

Acknowledgement

• sngan_projection
• BigGAN-PyTorch
• pytorch.sngan_projection