model.py

import torch
import torch.nn as nn
import torchvision.models as models

# -------- Residual Block --------
class ResidualBlock(nn.Module):
    def __init__(self, channels):
        super().__init__()
        self.block = nn.Sequential(
            nn.Conv2d(channels, channels, 3, 1, 1),
            nn.InstanceNorm2d(channels, affine=True),
            nn.ReLU(inplace=True),
            nn.Conv2d(channels, channels, 3, 1, 1),
            nn.InstanceNorm2d(channels, affine=True),
        )

    def forward(self, x):
        return x + self.block(x)

# -------- Transformer Network --------
class TransformerNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.model = nn.Sequential(
            nn.Conv2d(3, 32, 9, 1, 4),
            nn.InstanceNorm2d(32),
            nn.ReLU(),

            nn.Conv2d(32, 64, 3, 2, 1),
            nn.InstanceNorm2d(64),
            nn.ReLU(),

            nn.Conv2d(64, 128, 3, 2, 1),
            nn.InstanceNorm2d(128),
            nn.ReLU(),

            ResidualBlock(128),
            ResidualBlock(128),
            ResidualBlock(128),
            ResidualBlock(128),
            ResidualBlock(128),

            nn.ConvTranspose2d(128, 64, 3, 2, 1, 1),
            nn.InstanceNorm2d(64),
            nn.ReLU(),

            nn.ConvTranspose2d(64, 32, 3, 2, 1, 1),
            nn.InstanceNorm2d(32),
            nn.ReLU(),

            nn.Conv2d(32, 3, 9, 1, 4),
            nn.Tanh()
        )

    def forward(self, x):
        return self.model(x)