ResUnet代码实现pytorch

抱歉，作为AI语言模型，我无法提供完整的代码实现。但是，以下是一个基于PyTorch实现的ResUNet的代码示例，您可以参考它进行自己的实现。

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

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

        # Initial convolution block
        self.conv1 = nn.Conv2d(in_channels, init_features, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(init_features)
        self.relu1 = nn.ReLU(inplace=True)

        # Residual blocks
        self.conv2 = nn.Conv2d(init_features, init_features * 2, kernel_size=3, stride=2, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(init_features * 2)
        self.relu2 = nn.ReLU(inplace=True)
        self.conv3 = nn.Conv2d(init_features * 2, init_features * 2, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn3 = nn.BatchNorm2d(init_features * 2)
        self.relu3 = nn.ReLU(inplace=True)

        self.conv4 = nn.Conv2d(init_features * 2, init_features * 4, kernel_size=3, stride=2, padding=1, bias=False)
        self.bn4 = nn.BatchNorm2d(init_features * 4)
        self.relu4 = nn.ReLU(inplace=True)
        self.conv5 = nn.Conv2d(init_features * 4, init_features * 4, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn5 = nn.BatchNorm2d(init_features * 4)
        self.relu5 = nn.ReLU(inplace=True)

        self.conv6 = nn.Conv2d(init_features * 4, init_features * 8, kernel_size=3, stride=2, padding=1, bias=False)
        self.bn6 = nn.BatchNorm2d(init_features * 8)
        self.relu6 = nn.ReLU(inplace=True)
        self.conv7 = nn.Conv2d(init_features * 8, init_features * 8, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn7 = nn.BatchNorm2d(init_features * 8)
        self.relu7 = nn.ReLU(inplace=True)

        self.conv8 = nn.Conv2d(init_features * 8, init_features * 16, kernel_size=3, stride=2, padding=1, bias=False)
        self.bn8 = nn.BatchNorm2d(init_features * 16)
        self.relu8 = nn.ReLU(inplace=True)
        self.conv9 = nn.Conv2d(init_features * 16, init_features * 16, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn9 = nn.BatchNorm2d(init_features * 16)
        self.relu9 = nn.ReLU(inplace=True)

        # Upsampling blocks
        self.upconv1 = nn.ConvTranspose2d(init_features * 16, init_features * 8, kernel_size=3, stride=2, padding=1, output_padding=1, bias=False)
        self.bn10 = nn.BatchNorm2d(init_features * 8)
        self.relu10 = nn.ReLU(inplace=True)
        self.conv10 = nn.Conv2d(init_features * 16, init_features * 8, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn11 = nn.BatchNorm2d(init_features * 8)
        self.relu11 = nn.ReLU(inplace=True)

        self.upconv2 = nn.ConvTranspose2d(init_features * 8, init_features * 4, kernel_size=3, stride=2, padding=1, output_padding=1, bias=False)
        self.bn12 = nn.BatchNorm2d(init_features * 4)
        self.relu12 = nn.ReLU(inplace=True)
        self.conv11 = nn.Conv2d(init_features * 8, init_features * 4, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn13 = nn.BatchNorm2d(init_features * 4)
        self.relu13 = nn.ReLU(inplace=True)

        self.upconv3 = nn.ConvTranspose2d(init_features * 4, init_features * 2, kernel_size=3, stride=2, padding=1, output_padding=1, bias=False)
        self.bn14 = nn.BatchNorm2d(init_features * 2)
        self.relu14 = nn.ReLU(inplace=True)
        self.conv12 = nn.Conv2d(init_features * 4, init_features * 2, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn15 = nn.BatchNorm2d(init_features * 2)
        self.relu15 = nn.ReLU(inplace=True)

        self.upconv4 = nn.ConvTranspose2d(init_features * 2, init_features, kernel_size=3, stride=2, padding=1, output_padding=1, bias=False)
        self.bn16 = nn.BatchNorm2d(init_features)
        self.relu16 = nn.ReLU(inplace=True)
        self.conv13 = nn.Conv2d(init_features * 2, init_features, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn17 = nn.BatchNorm2d(init_features)
        self.relu17 = nn.ReLU(inplace=True)

        # Output layer
        self.outconv = nn.Conv2d(init_features, out_channels, kernel_size=1, stride=1, padding=0, bias=False)

    def forward(self, x):
        # Initial convolution block
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu1(x)

        # Residual blocks
        x_res1 = x
        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu2(x)
        x = self.conv3(x)
        x = self.bn3(x)
        x = x + x_res1
        x = self.relu3(x)

        x_res2 = x
        x = self.conv4(x)
        x = self.bn4(x)
        x = self.relu4(x)
        x = self.conv5(x)
        x = self.bn5(x)
        x = x + x_res2
        x = self.relu5(x)

        x_res3 = x
        x = self.conv6(x)
        x = self.bn6(x)
        x = self.relu6(x)
        x = self.conv7(x)
        x = self.bn7(x)
        x = x + x_res3
        x = self.relu7(x)

        x_res4 = x
        x = self.conv8(x)
        x = self.bn8(x)
        x = self.relu8(x)
        x = self.conv9(x)
        x = self.bn9(x)
        x = x + x_res4
        x = self.relu9(x)

        # Upsampling blocks
        x = self.upconv1(x)
        x = self.bn10(x)
        x = self.relu10(x)
        x = torch.cat([x, x_res4], dim=1)
        x = self.conv10(x)
        x = self.bn11(x)
        x = self.relu11(x)

        x = self.upconv2(x)
        x = self.bn12(x)
        x = self.relu12(x)
        x = torch.cat([x, x_res3], dim=1)
        x = self.conv11(x)
        x = self.bn13(x)
        x = self.relu13(x)

        x = self.upconv3(x)
        x = self.bn14(x)
        x = self.relu14(x)
        x = torch.cat([x, x_res2], dim=1)
        x = self.conv12(x)
        x = self.bn15(x)
        x = self.relu15(x)

        x = self.upconv4(x)
        x = self.bn16(x)
        x = self.relu16(x)
        x = torch.cat([x, x_res1], dim=1)
        x = self.conv13(x)
        x = self.bn17(x)
        x = self.relu17(x)

        # Output layer
        x = self.outconv(x)
        x = torch.sigmoid(x)

        return x