model.py

import torch.nn as nn
from huggingface_hub import PyTorchModelHubMixin

class ModelColorization(nn.Module, PyTorchModelHubMixin):
    def __init__(self):
        super(ModelColorization, self).__init__()
        self.encoder = nn.Sequential(
            nn.Conv2d(1, 64, kernel_size=3, stride=1, padding=1),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.ReLU(),
            nn.BatchNorm2d(64),
            nn.Conv2d(64, 32, kernel_size=3, stride=1, padding=1),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.ReLU(),
            nn.BatchNorm2d(32),
            nn.Conv2d(32, 16, kernel_size=3, stride=1, padding=1),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.ReLU(),
            nn.BatchNorm2d(16),
            nn.Flatten(),
            nn.Linear(16*45*45, 4000),
        )
        self.decoder = nn.Sequential(
            nn.Linear(4000, 16 * 45 * 45),
            nn.ReLU(),
            nn.Unflatten(1, (16, 45, 45)),
            nn.ConvTranspose2d(16, 32, kernel_size=3, stride=2, padding=1, output_padding=1),
            nn.ReLU(),
            nn.BatchNorm2d(32),
            nn.ConvTranspose2d(32, 64, kernel_size=3, stride=2, padding=1, output_padding=1),
            nn.ReLU(),
            nn.BatchNorm2d(64),
            nn.ConvTranspose2d(64, 3, kernel_size=3, stride=2, padding=1, output_padding=1),
            nn.Sigmoid()
        )

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