cGANs with conditional weight standarlization

A new way to inject the conditional information

Dependencies:

• PyTorch1.0
• numpy
• scipy
• tensorboardX
• tqdm
• torchviz pip install torchviz and graphviz sudo apt-get install graphviz

Usage:

There are two 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

Parameter	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

Acknowledgement

• sngan_projection
• BigGAN-PyTorch
• pytorch.sngan_projection