class ConvBlock(nn.Module):
    def __init__(self, in_ch, out_ch, dropout=0.1):
        super().__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(in_ch, out_ch, 3, padding=1),
            nn.BatchNorm2d(out_ch),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_ch, out_ch, 3, padding=1),
            nn.BatchNorm2d(out_ch),
            nn.ReLU(inplace=True),
            nn.Dropout2d(dropout)
        )
    def forward(self, x):
        return self.conv(x)

class ImprovedUNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.enc1 = ConvBlock(1, 64, dropout=0.1)
        self.enc2 = ConvBlock(64, 128, dropout=0.1)
        self.enc3 = ConvBlock(128, 256, dropout=0.2)
        self.enc4 = ConvBlock(256, 512, dropout=0.2)
        
        self.pool = nn.MaxPool2d(2)
        
        self.bottleneck = ConvBlock(512, 1024, dropout=0.3)
        
        self.up4 = nn.ConvTranspose2d(1024, 512, 2, stride=2)
        self.dec4 = ConvBlock(1024, 512, dropout=0.2)
        
        self.up3 = nn.ConvTranspose2d(512, 256, 2, stride=2)
        self.dec3 = ConvBlock(512, 256, dropout=0.2)
        
        self.up2 = nn.ConvTranspose2d(256, 128, 2, stride=2)
        self.dec2 = ConvBlock(256, 128, dropout=0.1)
        
        self.up1 = nn.ConvTranspose2d(128, 64, 2, stride=2)
        self.dec1 = ConvBlock(128, 64, dropout=0.1)
        
        self.out_conv = nn.Conv2d(64, 3, 1)
        self.out_act = nn.Tanh()
    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.up4(b)
        d4 = torch.cat([d4, e4], dim=1)
        d4 = self.dec4(d4)
        
        d3 = self.up3(d4)
        d3 = torch.cat([d3, e3], dim=1)
        d3 = self.dec3(d3)
        
        d2 = self.up2(d3)
        d2 = torch.cat([d2, e2], dim=1)
        d2 = self.dec2(d2)
        
        d1 = self.up1(d2)
        d1 = torch.cat([d1, e1], dim=1)
        d1 = self.dec1(d1)
        
        out = self.out_conv(d1)
        return self.out_act(out)