pipeline.py

import torch
from PIL import Image
from torchvision import transforms
import torch.nn as nn

class Generator(nn.Module):
    def __init__(self, input_size, output_channels):
        super(Generator, self).__init__()
        
        # Define the architecture of the generator
        self.model = nn.Sequential(
            nn.Linear(input_size, 128),         # Input layer
            nn.LeakyReLU(0.2),                 # Activation function
            nn.Linear(128, 256),               # Hidden layer
            nn.BatchNorm1d(256),               # Batch normalization
            nn.LeakyReLU(0.2),                 # Activation function
            nn.Linear(256, 512),               # Hidden layer
            nn.BatchNorm1d(512),               # Batch normalization
            nn.LeakyReLU(0.2),                 # Activation function
            nn.Linear(512, output_channels),    # Output layer
            nn.Tanh()                          # Tanh activation for output
        )

    def forward(self, x):
        # Forward pass through the generator network
        return self.model(x)

class PreTrainedPipeline():
    def __init__(self, path=""):
        """
        Initialize model
        """
        self.model = Generator()  # Initialize your Generator model here

    def generate_random_image(self):
        """
        Generate a random image using the GAN model.
        Return:
            A :obj:`PIL.Image` with the generated image.
        """
        noise = torch.randn(1, 100, 1, 1)
        with torch.no_grad():
            output_image = self.model(noise)

        # Scale generated image
        output_image = (output_image + 1) / 2

        # Convert to PIL Image
        pil_image = transforms.ToPILImage()(output_image[0])

        return pil_image

# Example usage
if __name__ == "__main__":
    pipeline = PreTrainedPipeline()
    generated_image = pipeline.generate_random_image()
    generated_image.save('generated_image.jpg')
    print("Generated image saved at 'generated_image.jpg'")