model.py

import torch
import torch.nn as nn
import torch.nn.functional as F

class DoubleConv(nn.Module):
    """
    DoubleConv Module
    =================
    A standard building block for UNet, consisting of two consecutive convolution layers.
    Each 3x3 convolution is followed by Batch Normalization and ReLU activation.
    
    Structure:
        Input -> [Conv3x3 -> BatchNorm -> ReLU] -> [Conv3x3 -> BatchNorm -> ReLU] -> Output
    """
    def __init__(self, in_channels, out_channels):
        super().__init__()
        self.double_conv = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True)
        )

    def forward(self, x):
        return self.double_conv(x)

class Down(nn.Module):
    """
    Down Module
    ===========
    Handles the downsampling step in the encoder part of the UNet.
    It supports two modes of downsampling:
    
    1. 'maxpool': Uses MaxPool2d(2) to halve the spatial dimensions.
    2. 'strided': Uses a Strided Conv (kernel=3, stride=2) to halve dimensions while learning features.
    
    After downsampling, a DoubleConv block processes the features.
    """
    def __init__(self, in_channels, out_channels, mode='maxpool'):
        super().__init__()
        self.mode = mode
        if mode == 'maxpool':
            # Option 1: MaxPool downsampling (Standard UNet)
            self.down_layer = nn.MaxPool2d(2)
            self.conv = DoubleConv(in_channels, out_channels)
        elif mode == 'strided':
            # Option 2: Strided Convolution downsampling
            # Replaces the pooling operation with a learnable strided convolution
            self.down_layer = nn.Sequential(
                nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=2, padding=1),
                nn.BatchNorm2d(out_channels),
                nn.ReLU(inplace=True)
            )
            # The strided conv handles the channel change (in -> out).
            # The following DoubleConv refines these features (out -> out).
            self.conv = DoubleConv(out_channels, out_channels)
        else:
            raise ValueError(f"Unknown downsample mode: {mode}")

    def forward(self, x):
        if self.mode == 'maxpool':
            x = self.down_layer(x)
            return self.conv(x)
        else:
            # Strided path
            x = self.down_layer(x) # [B, OutCh, H/2, W/2]
            return self.conv(x)    # [B, OutCh, H/2, W/2]

class Up(nn.Module):
    """
    Up Module
    =========
    Handles the upsampling step in the decoder part of the UNet.
    It supports two modes of upsampling:
    
    1. 'transpose': Uses ConvTranspose2d to learn how to upsample.
    2. 'upsample': Uses bilinear interpolation (nn.Upsample).
    
    Steps:
    1. Upsample the input tensor (x1) from the previous lower layer.
    2. Concatenate it with the corresponding feature map from the encoder (x2) (Skip Connection).
       - Handles padding if dimensions don't match perfectly.
    3. Process the combined features with a DoubleConv block.
    """
    def __init__(self, in_channels, out_channels, mode='transpose'):
        super().__init__()

        if mode == 'transpose':
            # Option 1: Transpose Convolution
            # Typical for original UNet. Upsamples and reduces channels by half.
            # in_channels is the dimension of the deep feature map coming UP.
            self.up = nn.ConvTranspose2d(in_channels, in_channels // 2, kernel_size=2, stride=2)
            self.up_mode = 'transpose'
        elif mode == 'upsample':
            # Option 2: Bilinear Upsampling
            # Does not reduce channels itself, so we need a 1x1 conv to reduce channels
            # to match the skip connection size before DoubleConv.
            self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
            self.conv_adjust = nn.Conv2d(in_channels, in_channels // 2, kernel_size=1) 
            self.up_mode = 'upsample'
        else:
            raise ValueError(f"Unknown upsample mode: {mode}")
        
        # DoubleConv takes the concatenated input.
        # Channels = (in_channels // 2 from Up) + (in_channels // 2 from Skip) = in_channels
        # Outputs count = out_channels
        self.conv = DoubleConv(in_channels, out_channels)

            
    def forward(self, x1, x2):
        """
        x1: Input from the previous decoder layer (to be upsampled)
        x2: Input from the encoder layer (skip connection)
        """
        x1 = self.up(x1)
        
        if hasattr(self, 'conv_adjust'):
             x1 = self.conv_adjust(x1)
             
        # Handle padding if x1 and x2 have slightly different sizes due to odd dimensions
        # input is CHW
        diffY = x2.size()[2] - x1.size()[2]
        diffX = x2.size()[3] - x1.size()[3]

        x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2,
                        diffY // 2, diffY - diffY // 2])
        
        # Concatenate x2 (skip) and x1 (upsampled) along the channel dimension
        x = torch.cat([x2, x1], dim=1)
        return self.conv(x)


class UNet(nn.Module):
    """
    UNet Architecture
    =================
    A U-shaped encoder-decoder architecture for image segmentation.
    
    Configurable Parameters:
    - n_channels: Number of input image channels (e.g., 3 for RGB).
    - n_classes: Number of output classes (e.g., 1 for binary mask).
    - downsample_mode: 'maxpool' or 'strided'.
    - upsample_mode: 'transpose' or 'upsample' (bilinear).
    """
    def __init__(self, n_channels, n_classes, downsample_mode='maxpool', upsample_mode='transpose'):
        super(UNet, self).__init__()
        self.n_channels = n_channels
        self.n_classes = n_classes
        self.downsample_mode = downsample_mode
        self.upsample_mode = upsample_mode

        # Initial Feature Extraction
        # Input: [B, n_channels, H, W] -> Output: [B, 64, H, W]
        self.inc = DoubleConv(n_channels, 64)
        
        # Encoder (Downsampling Path)
        # Each step reduces H,W by 2 and doubles Channels
        # Down 1: 64 -> 128
        self.down1 = Down(64, 128, mode=downsample_mode)
        # Down 2: 128 -> 256
        self.down2 = Down(128, 256, mode=downsample_mode)
        # Down 3: 256 -> 512
        self.down3 = Down(256, 512, mode=downsample_mode)
        
        # Bridge / Bottleneck
        # Standard UNet goes to 1024.
        self.down4 = Down(512, 1024, mode=downsample_mode)
        
        # Decoder (Upsampling Path)
        # Each step doubles H,W and halves Channels (logic handled in Up block)
        self.up1 = Up(1024, 512, mode=upsample_mode)
        self.up2 = Up(512, 256, mode=upsample_mode)
        self.up3 = Up(256, 128, mode=upsample_mode)
        self.up4 = Up(128, 64, mode=upsample_mode)
        
        # Final Classification Layer
        # Reduces 64 channels to n_classes (1 per pixel for binary)
        self.outc = nn.Conv2d(64, n_classes, kernel_size=1)

    def forward(self, x):
        # Encoder Path with Skip Connections
        x1 = self.inc(x)       # [B, 64, H, W]
        x2 = self.down1(x1)    # [B, 128, H/2, W/2]
        x3 = self.down2(x2)    # [B, 256, H/4, W/4]
        x4 = self.down3(x3)    # [B, 512, H/8, W/8]
        x5 = self.down4(x4)    # [B, 1024, H/16, W/16] (Bottleneck)
        
        # Decoder Path
        # Pass skip connections (x4, x3, x2, x1) to Up modules
        x = self.up1(x5, x4)   # [B, 512, H/8, W/8]
        x = self.up2(x, x3)    # [B, 256, H/4, W/4]
        x = self.up3(x, x2)    # [B, 128, H/2, W/2]
        x = self.up4(x, x1)    # [B, 64, H, W]
        
        logits = self.outc(x)  # [B, n_classes, H, W]
        return logits