defectArch.py

import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import models


class DoubleConv(nn.Module):
    def __init__(self, in_channels, out_channels, dropout=0.0):
        super(DoubleConv, self).__init__()
        layers = [
            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),
        ]
        if dropout > 0:
            layers.append(nn.Dropout2d(p=dropout))
        self.net = nn.Sequential(*layers)

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


def crop_to_match(enc_feat, dec_feat):
    """Center-crop encoder feature map to match size of decoder feature map."""
    _, _, H, W = dec_feat.shape
    enc_H, enc_W = enc_feat.shape[2], enc_feat.shape[3]

    crop_top = (enc_H - H) // 2
    crop_left = (enc_W - W) // 2
    return enc_feat[:, :, crop_top:crop_top+H, crop_left:crop_left+W]


class UNet(nn.Module):
    def __init__(self, in_channels=1, out_channels=1, dropout=0.1):
        super(UNet, self).__init__()

        # Encoder
        self.enc1 = DoubleConv(in_channels, 64, dropout=dropout)
        self.enc2 = DoubleConv(64, 128, dropout=dropout)
        self.enc3 = DoubleConv(128, 256, dropout=dropout)
        self.enc4 = DoubleConv(256, 512, dropout=dropout)

        self.pool = nn.MaxPool2d(2)

        self.bottleneck = DoubleConv(512, 1024, dropout=dropout)

        # Decoder
        self.up4 = nn.ConvTranspose2d(1024, 512, kernel_size=2, stride=2)
        self.dec4 = DoubleConv(1024, 512, dropout=dropout)
        self.up3 = nn.ConvTranspose2d(512, 256, kernel_size=2, stride=2)
        self.dec3 = DoubleConv(512, 256, dropout=dropout)
        self.up2 = nn.ConvTranspose2d(256, 128, kernel_size=2, stride=2)
        self.dec2 = DoubleConv(256, 128, dropout=dropout)
        self.up1 = nn.ConvTranspose2d(128, 64, kernel_size=2, stride=2)
        self.dec1 = DoubleConv(128, 64, dropout=dropout)

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

    def forward(self, x):
        input_size = x.shape[2:]  # (H, W)

        # Encoder
        e1 = self.enc1(x)
        e2 = self.enc2(self.pool(e1))
        e3 = self.enc3(self.pool(e2))
        e4 = self.enc4(self.pool(e3))

        # Bottleneck
        b = self.bottleneck(self.pool(e4))

        # Decoder with cropping
        d4 = self.up4(b)
        e4_cropped = crop_to_match(e4, d4)
        d4 = self.dec4(torch.cat([d4, e4_cropped], dim=1))

        d3 = self.up3(d4)
        e3_cropped = crop_to_match(e3, d3)
        d3 = self.dec3(torch.cat([d3, e3_cropped], dim=1))

        d2 = self.up2(d3)
        e2_cropped = crop_to_match(e2, d2)
        d2 = self.dec2(torch.cat([d2, e2_cropped], dim=1))

        d1 = self.up1(d2)
        e1_cropped = crop_to_match(e1, d1)
        d1 = self.dec1(torch.cat([d1, e1_cropped], dim=1))

        out = self.final(d1)

        # Resize output back to input size (200x200)
        out = F.interpolate(out, size=input_size, mode="bilinear", align_corners=False)

        return out


# # =========================================================
# # 1. U-Net
# # =========================================================
# class UNet(nn.Module):
#     def __init__(self, in_channels=1, out_channels=1):
#         super().__init__()

#         def CBR(in_c, out_c):
#             return nn.Sequential(
#                 nn.Conv2d(in_c, out_c, 3, padding=1),
#                 nn.BatchNorm2d(out_c),
#                 nn.ReLU(inplace=True)
#             )

#         self.enc1 = nn.Sequential(CBR(in_channels, 64), CBR(64, 64))
#         self.enc2 = nn.Sequential(CBR(64, 128), CBR(128, 128))
#         self.enc3 = nn.Sequential(CBR(128, 256), CBR(256, 256))
#         self.enc4 = nn.Sequential(CBR(256, 512), CBR(512, 512))

#         self.pool = nn.MaxPool2d(2, 2)
#         self.center = nn.Sequential(CBR(512, 1024), CBR(1024, 512))

#         self.up4 = nn.ConvTranspose2d(512, 512, 2, stride=2)
#         self.dec4 = nn.Sequential(CBR(1024, 512), CBR(512, 256))
#         self.up3 = nn.ConvTranspose2d(256, 256, 2, stride=2)
#         self.dec3 = nn.Sequential(CBR(512, 256), CBR(256, 128))
#         self.up2 = nn.ConvTranspose2d(128, 128, 2, stride=2)
#         self.dec2 = nn.Sequential(CBR(256, 128), CBR(128, 64))
#         self.up1 = nn.ConvTranspose2d(64, 64, 2, stride=2)
#         self.dec1 = nn.Sequential(CBR(128, 64), nn.Conv2d(64, out_channels, 1))

#     def crop_to_match(self, x, target):
#         _, _, h, w = target.size()
#         return x[:, :, :h, :w]

#     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))
#         c = self.center(self.pool(e4))

#         d4 = self.up4(c)
#         e4_c = self.crop_to_match(e4, d4)
#         d4 = torch.cat([d4, e4_c], dim=1)
#         d4 = self.dec4(d4)

#         d3 = self.up3(d4)
#         e3_c = self.crop_to_match(e3, d3)
#         d3 = torch.cat([d3, e3_c], dim=1)
#         d3 = self.dec3(d3)

#         d2 = self.up2(d3)
#         e2_c = self.crop_to_match(e2, d2)
#         d2 = torch.cat([d2, e2_c], dim=1)
#         d2 = self.dec2(d2)

#         d1 = self.up1(d2)
#         e1_c = self.crop_to_match(e1, d1)
#         d1 = torch.cat([d1, e1_c], dim=1)
#         out = self.dec1(d1)
#         out = F.interpolate(out, size=x.shape[2:], mode="bilinear", align_corners=False)
#         return out