U-Net +SEResNeXt50代码

这里提供一个U-Net + SEResNeXt50的PyTorch代码示例，仅供参考：

import torch
import torch.nn as nn
from torchvision.models import resnext50_32x4d

class DoubleConv(nn.Module):
    def __init__(self, in_ch, out_ch):
        super(DoubleConv, self).__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)
        )

    def forward(self, x):
        x = self.conv(x)
        return x

class Up(nn.Module):
    def __init__(self, in_ch, out_ch, bilinear=True):
        super(Up, self).__init__()

        if bilinear:
            self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
        else:
            self.up = nn.ConvTranspose2d(in_ch//2, in_ch//2, 2, stride=2)

        self.conv = DoubleConv(in_ch, out_ch)

    def forward(self, x1, x2):
        x1 = self.up(x1)

        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])
        
        x = torch.cat([x2, x1], dim=1)
        x = self.conv(x)
        return x

class UNetSEResNeXt50(nn.Module):
    def __init__(self, n_channels, n_classes, bilinear=True):
        super(UNetSEResNeXt50, self).__init__()
        self.n_channels = n_channels
        self.n_classes = n_classes
        self.bilinear = bilinear

        self.resnext50 = resnext50_32x4d(pretrained=True)

        self.conv1 = nn.Sequential(
            self.resnext50.conv1,
            self.resnext50.bn1,
            self.resnext50.relu,
            self.resnext50.maxpool
        )

        self.encoder1 = self.resnext50.layer1
        self.encoder2 = self.resnext50.layer2
        self.encoder3 = self.resnext50.layer3
        self.encoder4 = self.resnext50.layer4

        self.center = DoubleConv(2048, 2048)

        self.decoder4 = Up(2048 + 1024, 1024, bilinear)
        self.decoder3 = Up(1024 + 512, 512, bilinear)
        self.decoder2 = Up(512 + 256, 256, bilinear)
        self.decoder1 = Up(256 + 64, 64, bilinear)

        self.final_conv = nn.Conv2d(64, n_classes, 1)

    def forward(self, x):
        x1 = self.conv1(x)
        x2 = self.encoder1(x1)
        x3 = self.encoder2(x2)
        x4 = self.encoder3(x3)
        x5 = self.encoder4(x4)

        center = self.center(x5)

        d4 = self.decoder4(center, x4)
        d3 = self.decoder3(d4, x3)
        d2 = self.decoder2(d3, x2)
        d1 = self.decoder1(d2, x1)

        out = self.final_conv(d1)
        return out

该代码实现了一个使用SEResNeXt50作为编码器的U-Net模型，可以用于图像分割任务。其中`DoubleConv`表示一个双卷积层，`Up`表示一个上采样层，`UNetSEResNeXt50`表示整个模型的结构。