pytorch搭建unet3+网络，并输出网络结构

以下是使用PyTorch搭建UNET3网络，并输出网络结构的代码：

import torch
import torch.nn as nn


class UNet3(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(UNet3, self).__init__()
        # 编码器部分
        self.conv1 = nn.Conv2d(in_channels, 64, kernel_size=3, stride=1, padding=1, bias=True)
        self.relu1 = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=True)
        self.relu2 = nn.ReLU(inplace=True)
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)

        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1, bias=True)
        self.relu3 = nn.ReLU(inplace=True)
        self.conv4 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1, bias=True)
        self.relu4 = nn.ReLU(inplace=True)
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)

        self.conv5 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1, bias=True)
        self.relu5 = nn.ReLU(inplace=True)
        self.conv6 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=True)
        self.relu6 = nn.ReLU(inplace=True)
        self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)

        self.conv7 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1, bias=True)
        self.relu7 = nn.ReLU(inplace=True)
        self.conv8 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1, bias=True)
        self.relu8 = nn.ReLU(inplace=True)

        # 解码器部分
        self.upconv1 = nn.ConvTranspose2d(512, 256, kernel_size=2, stride=2, bias=True)
        self.conv9 = nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=1, bias=True)
        self.relu9 = nn.ReLU(inplace=True)
        self.conv10 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=True)
        self.relu10 = nn.ReLU(inplace=True)

        self.upconv2 = nn.ConvTranspose2d(256, 128, kernel_size=2, stride=2, bias=True)
        self.conv11 = nn.Conv2d(256, 128, kernel_size=3, stride=1, padding=1, bias=True)
        self.relu11 = nn.ReLU(inplace=True)
        self.conv12 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1, bias=True)
        self.relu12 = nn.ReLU(inplace=True)

        self.upconv3 = nn.ConvTranspose2d(128, 64, kernel_size=2, stride=2, bias=True)
        self.conv13 = nn.Conv2d(128, 64, kernel_size=3, stride=1, padding=1, bias=True)
        self.relu13 = nn.ReLU(inplace=True)
        self.conv14 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=True)
        self.relu14 = nn.ReLU(inplace=True)

        # 最终输出
        self.conv15 = nn.Conv2d(64, out_channels, kernel_size=1, stride=1, bias=True)

    def forward(self, x):
        # 编码器部分
        x1 = self.conv1(x)
        x1 = self.relu1(x1)
        x1 = self.conv2(x1)
        x1 = self.relu2(x1)
        x2 = self.pool1(x1)

        x2 = self.conv3(x2)
        x2 = self.relu3(x2)
        x2 = self.conv4(x2)
        x2 = self.relu4(x2)
        x3 = self.pool2(x2)

        x3 = self.conv5(x3)
        x3 = self.relu5(x3)
        x3 = self.conv6(x3)
        x3 = self.relu6(x3)
        x4 = self.pool3(x3)

        x4 = self.conv7(x4)
        x4 = self.relu7(x4)
        x4 = self.conv8(x4)
        x4 = self.relu8(x4)

        # 解码器部分
        x5 = self.upconv1(x4)
        x5 = torch.cat([x5, x3], dim=1)
        x5 = self.conv9(x5)
        x5 = self.relu9(x5)
        x5 = self.conv10(x5)
        x5 = self.relu10(x5)

        x6 = self.upconv2(x5)
        x6 = torch.cat([x6, x2], dim=1)
        x6 = self.conv11(x6)
        x6 = self.relu11(x6)
        x6 = self.conv12(x6)
        x6 = self.relu12(x6)

        x7 = self.upconv3(x6)
        x7 = torch.cat([x7, x1], dim=1)
        x7 = self.conv13(x7)
        x7 = self.relu13(x7)
        x7 = self.conv14(x7)
        x7 = self.relu14(x7)

        # 最终输出
        out = self.conv15(x7)
        return out


# 输出网络结构
if __name__ == '__main__':
    net = UNet3(in_channels=3, out_channels=2)
    print(net)