🔴 Developing a GAN for Moderate-Resolution Image Synthesis

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Developing a GAN for Moderate-Resolution Image Synthesis

Objective

Create a Generative Adversarial Network (GAN) capable of generating realistic images at moderate resolutions. This project involves implementing manageable GAN architectures, such as DCGAN or Progressive Growing GAN, training the model on smaller image datasets, and tackling challenges like mode collapse and training stability on limited hardware.

Learning Outcomes

By completing this project, you will:

• Understand GAN architectures and the challenges involved in training them.
• Implement basic GAN models capable of moderate-resolution image synthesis.
• Gain experience with training techniques specific to GANs.
• Learn to handle moderate-scale image data and optimize data pipelines.
• Evaluate generative models using metrics like FID and visual inspection.
• Develop problem-solving skills for common GAN issues like mode collapse and convergence.

Prerequisites and Theoretical Foundations

1. Advanced Python Programming

• Deep Learning Frameworks: Proficiency with PyTorch.
• Efficient Data Loading: Experience with PyTorch Datasets and DataLoaders.

2. Mathematics and Machine Learning Foundations

• Generative Models:
  • Understanding of GANs and basic generative modeling concepts.
• Optimization Techniques:
  • Knowledge of adversarial training and loss functions.
• Computer Vision:
  • Familiarity with Convolutional Neural Networks (CNNs).
  • Basic image preprocessing and augmentation techniques.

3. Understanding of GAN Architectures

• DCGAN:
  • Convolutional GAN architecture.
  • Batch normalization and ReLU activation functions.
• Progressive Growing GAN:
  • Incremental increase of image resolution during training.
  • Techniques for stability in progressive GAN training.

4. Experience with Image Datasets

• Dataset Handling:
  • Smaller datasets like CIFAR-10, CelebA, or custom datasets.
• Data Augmentation:
  • Basic augmentation techniques to increase data variability.

Tools Required

• Programming Language: Python 3.8+
• Libraries and Frameworks:
  • PyTorch: Deep learning framework (pip install torch>=1.9.0)
  • Torchvision: For datasets and image transformations (pip install torchvision>=0.10.0)

Hardware Requirements

• Minimum: Single GPU with at least 6GB VRAM (e.g., NVIDIA GTX 1660), 8GB RAM.
• Recommended: Single GPU with 8GB VRAM (e.g., NVIDIA RTX 3060), 16GB RAM.

Dataset Options

• CIFAR-10: A dataset with 60,000 small images (32x32 resolution), suitable for quick experimentation.
  • Access via Hugging Face Datasets: load_dataset("cifar10")
• CelebA (non-HQ): A collection of 202,599 celebrity images for moderate-resolution image synthesis.
  • Access via Hugging Face Datasets: load_dataset("celeba")

Project Structure

gan_image_synthesis/
│
├── data/
│   └── dataset_name/
│       └── images/
│
├── src/
│   ├── dataset.py
│   ├── generator.py
│   ├── discriminator.py
│   ├── train.py
│   ├── utils.py
│   └── fid_score.py
│
└── notebooks/
    └── exploration.ipynb

Steps and Tasks

1. Data Preparation

Tasks:

• Download and Preprocess the Dataset:
  • Resize images to 64x64 for manageable resolution.
• Create Custom Dataset Class:
  • Efficiently load data using DataLoader.

Implementation:

from torchvision import transforms
from torchvision.datasets import ImageFolder

transform = transforms.Compose([
    transforms.Resize(64),
    transforms.CenterCrop(64),
    transforms.ToTensor(),
    transforms.Normalize([0.5]*3, [0.5]*3)
])

dataset = ImageFolder(root='data/dataset_name/', transform=transform)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=32, shuffle=True, num_workers=2)

---

2. Implementing the GAN Architecture

Tasks:

• Define the Generator and Discriminator:
  • Use the DCGAN or Progressive GAN architecture.
• Use Batch Normalization:
  • Apply in generator and discriminator layers for stability.

Implementation:

import torch.nn as nn

class Generator(nn.Module):
    def __init__(self):
        super(Generator, self).__init__()
        # Define layers based on DCGAN architecture

    def forward(self, z):
        return img

class Discriminator(nn.Module):
    def __init__(self):
        super(Discriminator, self).__init__()
        # Define layers with batch normalization

    def forward(self, img):
        return validity

---

3. Setting Up the Training Loop

Tasks:

• Define Loss Functions:
  • Use binary cross-entropy for the adversarial loss.
• Implement Training Steps:
  • Alternate between updating the generator and discriminator.

Implementation:

# Loss function
adversarial_loss = nn.BCELoss()

# Optimizers
optimizer_G = torch.optim.Adam(generator.parameters(), lr=0.0002, betas=(0.5, 0.999))
optimizer_D = torch.optim.Adam(discriminator.parameters(), lr=0.0002, betas=(0.5, 0.999))

# Training loop
for epoch in range(num_epochs):
    for i, (imgs, _) in enumerate(dataloader):
        # Update Discriminator
        optimizer_D.zero_grad()
        # Compute loss and backpropagate
        # ...

        # Update Generator
        optimizer_G.zero_grad()
        # Compute loss and backpropagate
        # ...

---

4. Implementing Training Techniques

Tasks:

• Apply Gradient Penalty (optional):
  • Add a gradient penalty term to improve training stability.
• Implement Mixed-Precision Training:
  • Use PyTorch AMP for efficient resource usage.

Implementation:

# AMP setup for mixed-precision training
scaler = torch.cuda.amp.GradScaler()

# Gradient penalty
def compute_gradient_penalty(D, real_samples, fake_samples):
    return gradient_penalty

---

5. Evaluating the Model

Tasks:

• Calculate FID Score (optional):
  • Quantify quality of generated images.
• Visual Inspection:
  • Regularly save and review generated images to track progress.

Implementation:

# Generate samples
with torch.no_grad():
    generated_imgs = generator(fixed_noise)

# Save images
save_image(generated_imgs, 'images/generated.png', nrow=8, normalize=True)

# Optional: FID calculation
from fid_score import calculate_fid_given_paths
fid_value = calculate_fid_given_paths(['path/to/real', 'path/to/fake'], batch_size, device)
print(f"FID: {fid_value}")

---

6. Addressing Training Challenges

Tasks:

• Handle Mode Collapse:
  • Apply minibatch discrimination to maintain diversity.
• Stabilize Training:
  • Experiment with learning rate schedules and regularization.

Implementation:

# Minibatch discrimination
class Discriminator(nn.Module):
    def __init__(self):
        super(Discriminator, self).__init__()
        # Add minibatch discrimination layer

# Learning rate scheduler
scheduler_G = torch.optim.lr_scheduler.StepLR(optimizer_G, step_size=10, gamma=0.5)
scheduler_D = torch.optim.lr_scheduler.StepLR(optimizer_D, step_size=10, gamma=0.5)

---

7. Optimization and Scaling

Tasks:

• Experiment with Progressive Growing:
  • Start training with low-resolution images and increase gradually if feasible.
• Data Augmentation:
  • Apply basic augmentations like random flips or color jitter.

Implementation:

# Progressive growing example
# Dynamically increase resolution during training based on resource availability

---

Further Enhancements

• Implement Conditional GANs:
  • Generate images conditioned on class labels.
• Explore GAN Stabilization Techniques:
  • Experiment with spectral normalization or dropout in the discriminator.
• Integrate Model for Applications:
  • Use the model for specific tasks like image generation or style transfer.

Additional Resources:

Reference Implementations

• StyleGAN2-ADA PyTorch
• PyTorch GAN Zoo

Evaluation Metrics

• FID Score Implementation
• Inception Score Implementation

Documentation

• PyTorch Distributed Training Guide
• NVIDIA Mixed Precision Training
• GAN Training Tips and Tricks