model.py

import torch
import torch.nn as nn

class Autoencoder(nn.Module):
    def __init__(self):
        super(Autoencoder, self).__init__()

        self.encoder = nn.Sequential(
            nn.Conv2d(3, 64, 4, 2, 1),
            nn.ReLU(),
            nn.Conv2d(64, 128, 4, 2, 1),
            nn.BatchNorm2d(128),
            nn.ReLU(),
            nn.Conv2d(128, 256, 4, 2, 1),
            nn.BatchNorm2d(256),
            nn.ReLU(),
            nn.Conv2d(256, 512, 4, 2, 1),
            nn.ReLU()
        )

        self.decoder = nn.Sequential(
            nn.ConvTranspose2d(512, 256, 4, 2, 1),
            nn.BatchNorm2d(256),
            nn.ReLU(),
            nn.ConvTranspose2d(256, 128, 4, 2, 1),
            nn.BatchNorm2d(128),
            nn.ReLU(),
            nn.ConvTranspose2d(128, 64, 4, 2, 1),
            nn.ReLU(),
            nn.ConvTranspose2d(64, 3, 4, 2, 1),
            nn.Sigmoid()
        )

    def forward(self, x):
        return self.decoder(self.encoder(x))