small_256_model.py

import torch
import torch.nn as nn
import torch.optim as optim
class UNet(nn.Module):
    def __init__(self):
        super(UNet, self).__init__()
        # Encoder
        self.encoder = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=4, stride=2, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(64, 128, kernel_size=4, stride=2, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(128, 256, kernel_size=4, stride=2, padding=1),
            nn.ReLU(inplace=True),
        )
        # Decoder
        self.decoder = nn.Sequential(
            nn.ConvTranspose2d(256, 128, kernel_size=4, stride=2, padding=1),
            nn.ReLU(inplace=True),
            nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1),
            nn.ReLU(inplace=True),
            nn.ConvTranspose2d(64, 3, kernel_size=4, stride=2, padding=1),
            nn.Tanh()
        )

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