unet_model.py

import torch
import torch.nn as nn

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

        # Encoder
        self.enc1 = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(64, 64, kernel_size=3, padding=1),
            nn.ReLU(inplace=True)
        )
        self.pool1 = nn.MaxPool2d(2)

        self.enc2 = nn.Sequential(
            nn.Conv2d(64, 128, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(128, 128, kernel_size=3, padding=1),
            nn.ReLU(inplace=True)
        )
        self.pool2 = nn.MaxPool2d(2)

        # Middle
        self.middle = nn.Sequential(
            nn.Conv2d(128, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True)
        )

        # Decoder
        self.up1 = nn.ConvTranspose2d(256, 128, kernel_size=2, stride=2)
        self.dec1 = nn.Sequential(
            nn.Conv2d(256, 128, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(128, 128, kernel_size=3, padding=1),
            nn.ReLU(inplace=True)
        )

        self.up2 = nn.ConvTranspose2d(128, 64, kernel_size=2, stride=2)
        self.dec2 = nn.Sequential(
            nn.Conv2d(128, 64, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(64, 64, kernel_size=3, padding=1),
            nn.ReLU(inplace=True)
        )

        self.final = nn.Conv2d(64, 1, kernel_size=1)

    def forward(self, x):
        enc1 = self.enc1(x)
        enc2 = self.enc2(self.pool1(enc1))
        middle = self.middle(self.pool2(enc2))

        up1 = self.up1(middle)
        cat1 = torch.cat([up1, enc2], dim=1)
        dec1 = self.dec1(cat1)

        up2 = self.up2(dec1)
        cat2 = torch.cat([up2, enc1], dim=1)
        dec2 = self.dec2(cat2)

        return self.final(dec2)

def get_unet():
    return UNet()