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

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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:

image

CIFAR10 results

200K:

image

500K:

image

600K:

image

800K:

image

1000K:

image

Acknowledgement

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

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