Unet.py

import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict

from torchsummary import summary


# SE模块（Squeeze-and-Excitation）
class SEBlock(nn.Module):
    def __init__(self, channel, reduction=16):
        super(SEBlock, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.fc1 = nn.Conv2d(channel, channel // reduction, 1, bias=False)
        self.relu = nn.ReLU(inplace=True)
        self.fc2 = nn.Conv2d(channel // reduction, channel, 1, bias=False)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        b, c, _, _ = x.size()
        y = self.avg_pool(x)
        y = self.fc1(y)
        y = self.relu(y)
        y = self.fc2(y)
        y = self.sigmoid(y)
        return x * y  # 逐通道加权


# UNet结构
class UNet(nn.Module):
    def __init__(self, in_channels=3, out_channels=1, init_features=32):
        super(UNet, self).__init__()

        features = init_features
        self.encoder1 = UNet._block(in_channels, features, name="enc1")
        self.se1 = SEBlock(features)  # SE模块
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)

        self.encoder2 = UNet._block(features, features * 2, name="enc2")
        self.se2 = SEBlock(features * 2)  # SE模块
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)

        self.encoder3 = UNet._block(features * 2, features * 4, name="enc3")
        self.se3 = SEBlock(features * 4)  # SE模块
        self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)

        self.encoder4 = UNet._block(features * 4, features * 8, name="enc4")
        self.se4 = SEBlock(features * 8)  # SE模块
        self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2)

        self.bottleneck = UNet._block(features * 8, features * 16, name="bottleneck")
        self.se_bottleneck = SEBlock(features * 16)  # SE模块

        self.upconv4 = nn.ConvTranspose2d(
            features * 16, features * 8, kernel_size=2, stride=2
        )
        self.decoder4 = UNet._block((features * 8) * 2, features * 8, name="dec4")
        self.se_dec4 = SEBlock(features * 8)  # SE模块

        self.upconv3 = nn.ConvTranspose2d(
            features * 8, features * 4, kernel_size=2, stride=2
        )
        self.decoder3 = UNet._block((features * 4) * 2, features * 4, name="dec3")
        self.se_dec3 = SEBlock(features * 4)  # SE模块

        self.upconv2 = nn.ConvTranspose2d(
            features * 4, features * 2, kernel_size=2, stride=2
        )
        self.decoder2 = UNet._block((features * 2) * 2, features * 2, name="dec2")
        self.se_dec2 = SEBlock(features * 2)  # SE模块

        self.upconv1 = nn.ConvTranspose2d(
            features * 2, features, kernel_size=2, stride=2
        )
        self.decoder1 = UNet._block(features * 2, features, name="dec1")
        self.se_dec1 = SEBlock(features)  # SE模块

        self.conv = nn.Conv2d(
            in_channels=features, out_channels=out_channels, kernel_size=1
        )

    def forward(self, x):
        enc1 = self.encoder1(x)
        enc1 = self.se1(enc1)  # 添加SE模块
        enc2 = self.encoder2(self.pool1(enc1))
        enc2 = self.se2(enc2)  # 添加SE模块
        enc3 = self.encoder3(self.pool2(enc2))
        enc3 = self.se3(enc3)  # 添加SE模块
        enc4 = self.encoder4(self.pool3(enc3))
        enc4 = self.se4(enc4)  # 添加SE模块

        bottleneck = self.bottleneck(self.pool4(enc4))
        bottleneck = self.se_bottleneck(bottleneck)  # 添加SE模块

        dec4 = self.upconv4(bottleneck)
        dec4 = F.interpolate(dec4, size=enc4.shape[2:], mode='bilinear', align_corners=False)
        dec4 = torch.cat((dec4, enc4), dim=1)
        dec4 = self.decoder4(dec4)
        dec4 = self.se_dec4(dec4)  # 添加SE模块

        dec3 = self.upconv3(dec4)
        dec3 = torch.cat((dec3, enc3), dim=1)
        dec3 = self.decoder3(dec3)
        dec3 = self.se_dec3(dec3)  # 添加SE模块

        dec2 = self.upconv2(dec3)
        dec2 = torch.cat((dec2, enc2), dim=1)
        dec2 = self.decoder2(dec2)
        dec2 = self.se_dec2(dec2)  # 添加SE模块

        dec1 = self.upconv1(dec2)
        dec1 = torch.cat((dec1, enc1), dim=1)
        dec1 = self.decoder1(dec1)
        dec1 = self.se_dec1(dec1)  # 添加SE模块

        return self.conv(dec1)

    @staticmethod
    def _block(in_channels, features, name):
        return nn.Sequential(
            OrderedDict(
                [
                    (
                        name + "conv1",
                        nn.Conv2d(
                            in_channels=in_channels,
                            out_channels=features,
                            kernel_size=3,
                            padding=1,
                            bias=False,
                        ),
                    ),
                    (name + "norm1", nn.BatchNorm2d(num_features=features)),
                    (name + "relu1", nn.ReLU(inplace=True)),
                    (
                        name + "conv2",
                        nn.Conv2d(
                            in_channels=features,
                            out_channels=features,
                            kernel_size=3,
                            padding=1,
                            bias=False,
                        ),
                    ),
                    (name + "norm2", nn.BatchNorm2d(num_features=features)),
                    (name + "relu2", nn.ReLU(inplace=True)),
                ]
            )
        )


import torchinfo
model = UNet()  # 你的UNet模型
summary(model, input_size=(3, 128, 128))  # 假设输入图像大小为 (3, 128, 128)
torchinfo.summary(model, input_size=(1, 3, 128, 128))