Experimenting with Progressive Growing of GANs in PyTorch

Generative Adversarial Networks (GANs) have become a pivotal tool in deep learning, particularly for generating realistic images. Among the different architectures of GANs, Progressive Growing of GANs (ProGANs) introduces a promising approach that incrementally grows the network, leading to better scalability and stabilization. In this article, we'll delve into implementing ProGANs using PyTorch. The experimentation covers the initial setup, architecture, and the growth strategy, punctuated with code snippets for a practical grasp.

Setting Up Your Environment

Before we begin implementing ProGANs, ensure you have the necessary setup of PyTorch. You can quickly get started by installing PyTorch from the PyTorch official site and setting up a virtual environment.

conda create -n progan-env python=3.8
conda activate progan-env
pip install torch torchvision

Understanding the Progressive Growing of GANs

The main idea behind Progressive Growing is to start with a very low-resolution image and progressively increase the resolution by adding layers to the generator and discriminator gradually. This gradual growth helps in stabilizing training and often results in higher quality outputs. During training, the resolution is increased by doubling the dimensions of the generated images until the desired resolution is achieved.

Implementing the ProGAN Architecture

Let’s break down the code for the generator and discriminator in ProGAN.

Step 1: Design the Generator

The generator starts with a simple fully connected layer that projects the input latent vector to a low-resolution feature map, which is then progressively grown.

import torch
import torch.nn as nn

class Generator(nn.Module):
    def __init__(self, z_dim, in_channels, img_channels):
        super(Generator, self).__init__()
        self.initial = nn.Sequential(
            nn.Linear(z_dim, in_channels * 4 * 4),
            nn.LeakyReLU(0.2)
        )
        self.layers = nn.ModuleList()
        self.to_rgb = nn.Sequential(
            nn.Conv2d(in_channels, img_channels, kernel_size=1),
            nn.Tanh()
        )
        
    def forward(self, x):
        x = self.initial(x)
        x = x.view(x.size(0), -1, 4, 4)  # Reshape the flattened image
        for layer in self.layers:
            x = layer(x)
        return self.to_rgb(x)

The generator begins with initializing a dense layer followed by conversion operations to upscale to the desired dimensions incrementally.

Step 2: Design the Discriminator

The discriminator starts from the highest resolution and progressively reduces the resolution.

class Discriminator(nn.Module):
    def __init__(self, in_channels, img_channels):
        super(Discriminator, self).__init__()
        self.from_rgb = nn.Conv2d(img_channels, in_channels, kernel_size=1)
        self.layers = nn.ModuleList()
        self.initial_rgb = nn.Sequential(
            nn.BatchNorm2d(img_channels),
            nn.LeakyReLU(0.2)
        )
        self.final = nn.Linear(in_channels, 1)

    def forward(self, x):
        x = self.from_rgb(x)
        x = self.initial_rgb(x)
        for layer in self.layers:
            x = layer(x)
        x = torch.flatten(x, start_dim=1)
        return self.final(x)

Progressive Training

The crux of ProGAN is to initially train on a low-resolution image and then use that knowledge to learn higher resolutions. The training involves a fade-in period whenever new layers are added. This helps in a smooth transition and avoids overwhelming the network with a sudden increase in complexity.

Example Training Loop

Here, we present a simple structure for our training loop. Note that actual implementation will involve additional complexities such as optimizers, loss calculations, and data loading strategies.

def train(gen, disc, data_loader, num_epochs=10):
    for epoch in range(num_epochs):
        for data in data_loader:
            z = torch.randn(data.size(0), z_dim)  # Random noise
            fake_images = gen(z)
            # Training discriminator and generator logic places here

            # Example fade-in transition implementation:
            alpha = epoch / num_epochs  # For smooth transition
            output = (1 - alpha) * disc(real_images) + alpha * disc(fake_images)

Conclusion

Experimenting with Progressive Growing of GANs involves methodically constructing both Generator and Discriminator networks that can adapt as image resolutions grow. The code snippets provided here lay the groundwork, but real-world applications will require tuning, optimizations like spectral normalization, and preparing suitable datasets. Be sure to experiment with resolutions, transition phases, and network complexities to learn how these affect the quality and performance of the ProGAN.