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, 256, kernel_size=3, stride=1, padding=1),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.ReLU(),
            nn.BatchNorm2d(256),
            nn.Conv2d(256, 128, kernel_size=3, stride=1, padding=1),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.ReLU(),
            nn.BatchNorm2d(128),
            nn.Conv2d(128, 64, kernel_size=3, stride=1, padding=1),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.ReLU(),
            nn.BatchNorm2d(64),
            nn.Flatten(),
            nn.Linear(64 * 16 * 16, 1024),
        )
        self.decoder = nn.Sequential(
            nn.Linear(1024, 64 * 16 * 16),
            nn.ReLU(),
            nn.Unflatten(1, (64, 16, 16)),
            nn.ConvTranspose2d(64, 128, kernel_size=2, stride=2),
            nn.ReLU(),
            nn.BatchNorm2d(128),
            nn.ConvTranspose2d(128, 256, kernel_size=2, stride=2),
            nn.ReLU(),
            nn.BatchNorm2d(256),
            nn.ConvTranspose2d(256, 3, kernel_size=2, stride=2),
            nn.Sigmoid(),
        )

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