GANs-Implementations
GANs Implementations and other generative models + Training (in ./notebooks)
Implemented:
- Vanilla GAN
- DCGAN - Deep Convolutional GAN
- WGAN - Wasserstein GAN
- SNGAN - Spectrally Normalized GAN
- SRGAN - Super Resolution GAN
- StyleGAN
- Pix2PixHD
- C-VAE - Convolutional Variational Auto-encoder
Installation
$ pip install gans-implementationsLocal Install and Run:
$ cd {PROJECT_DIRECTORY}
$ pip install -e .Example
In notebooks directory there is a notebook on how to use each of these models for their intented use case; such as image generation for StyleGAN and others. Check them out!
from gans_package.models import StyleGAN_Generator, StyleGAN_Discriminator
in_channels = 256
out_channels = 3
hidden_channels = 512
z_dim = 128
mapping_hidden_size = 256
w_dim = 512
synthesis_layers = 5
kernel_size=3
in_size = 3
d_hidden_size = 16
g = StyleGAN_Generator(in_channels,
out_channels,
hidden_channels,
z_dim,
mapping_hidden_size,
w_dim,
synthesis_layers,
kernel_size,
device=DEVICE).to(DEVICE)
d = StyleGAN_Discriminator(in_size, d_hidden_size).to(DEVICE)
import torch
noise = torch.randn(BATCH_SIZE, z_dim).to(DEVICE)
fake = g(noise)
pred = d(fake)Handwritten Digits - MNIST
Work Cited
https://arxiv.org/pdf/1609.04802v5.pdf
https://arxiv.org/pdf/1812.04948.pdf
https://www.coursera.org/specializations/generative-adversarial-networks-gans?
