model.py

import torch
import torch.nn as nn

class UNetInpaint(nn.Module):
    def __init__(self, input_channels=4, output_channels=3):
        super().__init__()
        self.enc1 = self.conv_block(input_channels, 64)
        self.enc2 = self.conv_block(64, 128)
        self.enc3 = self.conv_block(128, 256)
        self.enc4 = self.conv_block(256, 512)
        self.pool = nn.MaxPool2d(2, 2)
        self.bottleneck = self.conv_block(512, 1024)
        self.upconv4 = self.up_conv_block(1024, 512)
        self.dec4 = self.conv_block(1024, 512)
        self.upconv3 = self.up_conv_block(512, 256)
        self.dec3 = self.conv_block(512, 256)
        self.upconv2 = self.up_conv_block(256, 128)
        self.dec2 = self.conv_block(256, 128)
        self.upconv1 = self.up_conv_block(128, 64)
        self.dec1 = self.conv_block(128, 64)
        self.out_conv = nn.Conv2d(64, output_channels, 1)
        self.final_activation = nn.Sigmoid()

    def conv_block(self, in_channels, out_channels):
        return nn.Sequential(
            nn.Conv2d(in_channels, out_channels, 3, padding=1, bias=False),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels, out_channels, 3, padding=1, bias=False),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True)
        )

    def up_conv_block(self, in_channels, out_channels):
        return nn.Sequential(
            nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True),
            nn.Conv2d(in_channels, out_channels, 3, padding=1, bias=False),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True)
        )

    def forward(self, x):
        e1 = self.enc1(x)
        e2 = self.enc2(self.pool(e1))
        e3 = self.enc3(self.pool(e2))
        e4 = self.enc4(self.pool(e3))
        b = self.bottleneck(self.pool(e4))
        d4 = self.upconv4(b)
        d4 = self.dec4(torch.cat([d4, e4], dim=1))
        d3 = self.upconv3(d4)
        d3 = self.dec3(torch.cat([d3, e3], dim=1))
        d2 = self.upconv2(d3)
        d2 = self.dec2(torch.cat([d2, e2], dim=1))
        d1 = self.upconv1(d2)
        d1 = self.dec1(torch.cat([d1, e1], dim=1))
        out = self.out_conv(d1)
        return self.final_activation(out)