import torch
from torch import nn
from torch.utils.data import DataLoader

# Hyperparameters
image_size = (224, 224, 3)  # Adjust based on your data

# Define the Generator Network
class Generator(nn.Module):
  def __init__(self):
    super(Generator, self).__init__()
    # Define convolutional layers with appropriate filters and activations
    self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1)
    # ... Add more convolutional layers as needed
    self.conv_final = nn.Conv2d(128, 3, kernel_size=3, stride=1, padding=1, activation=nn.Tanh)  # Tanh for shadow intensity

  def forward(self, x):
    # Define the forward pass through the convolutional layers
    x = self.conv1(x)
    # ... Forward pass through remaining convolutional layers
    return self.conv_final(x)

# Define the Discriminator Network
class Discriminator(nn.Module):
  def __init__(self):
    super(Discriminator, self).__init__()
    # Define convolutional layers with appropriate filters and activations
    self.conv1 = nn.Conv2d(6, 32, kernel_size=3, stride=1, padding=1)
    # ... Add more convolutional layers as needed
    self.linear = nn.Linear(128, 1)  # Final layer with sigmoid activation

  def forward(self, car, shadow):
    # Concatenate car and shadow features
    x = torch.cat([car, shadow], dim=1)
    # Define the forward pass through the convolutional layers
    x = self.conv1(x)
    # ... Forward pass through remaining convolutional layers
    return torch.sigmoid(self.linear(x))

# Create data loaders for training and validation data
# ... (Implement data loading logic using PyTorch's DataLoader)

# Create the models
generator = Generator()
discriminator = Discriminator()

# Define loss function and optimizer
criterion = nn.BCELoss()
g_optimizer = torch.optim.Adam(generator.parameters(), lr=0.0002)
d_optimizer = torch.optim.Adam(discriminator.parameters(), lr=0.0002)

# Training loop
for epoch in range(epochs):
  # Train the Discriminator
  # ... (Implement discriminator training logic with loss calculation and updates)

  # Train the Generator
  # ... (Implement generator training logic with loss calculation and updates)

  # Print training progress
  # ... (Print loss values or other metrics)

# Save the trained generator
torch.save(generator.state_dict(), 'generator.pt')