first commit
A Jittor implementation of Conditional GAN (CGAN).
You should install Jittor before running this program. See: cg.cs.tsinghua.edu.cn/jittor/download/
python CGAN.py [arguments]
--n_epochs
--batch_size
--lr
--b1
--b2
--n_cpu
--latent_dim
--n_classes
--img_size
--sample_interval
result.png
number
<n>.png
generator_last.pkl
discriminator_last.pkl
CGAN_jittor
A Jittor implementation of Conditional GAN (CGAN).
Prerequisite
You should install Jittor before running this program. See: cg.cs.tsinghua.edu.cn/jittor/download/
Usage
python CGAN.py [arguments]Arguments
--n_epochs: Number of epochs of training.--batch_size: Size of the batches.--lr: Adam: learning rate.--b1: Adam: decay of 1st order momentum of gradient.--b2: Adam: decay of 2nd order momentum of gradient.--n_cpu: Number of cpu threads to use during batch generation.--latent_dim: Dimensionality of the latent space.--n_classes: Number of classes for dataset.--img_size: Size of each image dimension.--sample_interval: Interval between image sampling.Outputs
result.png: Generated figures ofnumber.<n>.png: Generated figures after <n> batches.generator_last.pkl: Pickle file of the latest generator.discriminator_last.pkl: Pickle file of the latest discriminator.