用pytorch写一段输入和输出大小均为(1,7,103)的U-NET网络结构

下面是一个输入和输出大小均为(1,7,103)的U-NET网络结构的示例代码：

import torch
import torch.nn as nn

class UNet(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(UNet, self).__init__()

        self.conv1 = nn.Conv2d(in_channels, 64, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
        self.conv3 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
        self.conv4 = nn.Conv2d(256, 512, kernel_size=3, padding=1)
        self.conv5 = nn.Conv2d(512, 1024, kernel_size=3, padding=1)

        self.upconv6 = nn.ConvTranspose2d(1024, 512, kernel_size=2, stride=2)
        self.conv6 = nn.Conv2d(1024, 512, kernel_size=3, padding=1)
        self.upconv7 = nn.ConvTranspose2d(512, 256, kernel_size=2, stride=2)
        self.conv7 = nn.Conv2d(512, 256, kernel_size=3, padding=1)
        self.upconv8 = nn.ConvTranspose2d(256, 128, kernel_size=2, stride=2)
        self.conv8 = nn.Conv2d(256, 128, kernel_size=3, padding=1)
        self.upconv9 = nn.ConvTranspose2d(128, 64, kernel_size=2, stride=2)
        self.conv9 = nn.Conv2d(128, 64, kernel_size=3, padding=1)

        self.conv10 = nn.Conv2d(64, out_channels, kernel_size=1)

    def forward(self, x):
        # Down-sampling
        x1 = nn.functional.relu(self.conv1(x))
        x2 = nn.functional.relu(self.conv2(nn.functional.max_pool2d(x1, 2)))
        x3 = nn.functional.relu(self.conv3(nn.functional.max_pool2d(x2, 2)))
        x4 = nn.functional.relu(self.conv4(nn.functional.max_pool2d(x3, 2)))
        x5 = nn.functional.relu(self.conv5(nn.functional.max_pool2d(x4, 2)))

        # Up-sampling
        y6 = self.upconv6(x5)
        y6 = torch.cat([y6, x4], dim=1)
        y6 = nn.functional.relu(self.conv6(y6))
        y7 = self.upconv7(y6)
        y7 = torch.cat([y7, x3], dim=1)
        y7 = nn.functional.relu(self.conv7(y7))
        y8 = self.upconv8(y7)
        y8 = torch.cat([y8, x2], dim=1)
        y8 = nn.functional.relu(self.conv8(y8))
        y9 = self.upconv9(y8)
        y9 = torch.cat([y9, x1], dim=1)
        y9 = nn.functional.relu(self.conv9(y9))

        # Output
        out = self.conv10(y9)

        return out

这个U-NET网络结构包括了5个下采样层和5个上采样层，使用的卷积核大小为3x3。其中，下采样层使用了最大池化层，而上采样层使用了反卷积层。在每个上采样层中，还加入了一个跳跃连接，将下采样层的输出与上采样层的输入进行拼接。最后，输出层使用了一个1x1的卷积层，将通道数降至目标输出通道数。