我需要一段实现u-net卷积神经网络的python代码

我可以提供一段实现u-net卷积神经网络的python代码：import torch
import torch.nn as nnclass UNet(nn.Module):
def __init__(self):
super(UNet, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)
self.conv2 = nn.Conv2d(64, 64, kernel_size=3, padding=1)
self.pool1 = nn.MaxPool2d(2, stride=2, return_indices=True) self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
self.conv4 = nn.Conv2d(128, 128, kernel_size=3, padding=1)
self.pool2 = nn.MaxPool2d(2, stride=2, return_indices=True) self.conv5 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
self.conv6 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
self.pool3 = nn.MaxPool2d(2, stride=2, return_indices=True) self.conv7 = nn.Conv2d(256, 512, kernel_size=3, padding=1)
self.conv8 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
self.pool4 = nn.MaxPool2d(2, stride=2, return_indices=True) self.upconv1 = nn.ConvTranspose2d(512, 256, kernel_size=2, stride=2)
self.conv9 = nn.Conv2d(512, 256, kernel_size=3, padding=1)
self.conv10 = nn.Conv2d(256, 256, kernel_size=3, padding=1) self.upconv2 = nn.ConvTranspose2d(256, 128, kernel_size=2, stride=2)
self.conv11 = nn.Conv2d(256, 128, kernel_size=3, padding=1)
self.conv12 = nn.Conv2d(128, 128, kernel_size=3, padding=1) self.upconv3 = nn.ConvTranspose2d(128, 64, kernel_size=2, stride=2)
self.conv13 = nn.Conv2d(128, 64, kernel_size=3, padding=1)
self.conv14 = nn.Conv2d(64, 64, kernel_size=3, padding=1) self.conv15 = nn.Conv2d(64, 1, kernel_size=1)

def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x1, id1 = self.pool1(x) x = self.conv3(x1)
x = self.conv4(x)
x2, id2 = self.pool2(x) x = self.conv5(x2)
x = self.conv6(x)
x3, id3 = self.pool3(x) x = self.conv7(x3)
x = self.conv8(x)
x4, id4 = self.pool4(x) x = self.upconv1(x4, output_size=x3.size())
x = torch.cat([x, x3], dim=1)
x = self.conv9(x)
x = self.conv10(x) x = self.upconv2(x, output_size=x2.size())
x = torch.cat([x, x2], dim=1)
x = self.conv11(x)
x = self.conv12(x) x = self.upconv3(x, output_size=x1.size())
x = torch.cat([x, x1], dim=1)
x = self.conv13(x)
x = self.conv14(x) x = self.conv15(x) return x