python实现一个UNet神经网络模型。

以下是Python实现的UNet神经网络模型代码：

import torch
import torch.nn as nn

class DoubleConvBlock(nn.Module):
    def __init__(self, in_channels, out_channels):
        super().__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.relu1 = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1)
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.relu2 = nn.ReLU(inplace=True)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu1(x)
        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu2(x)
        return x

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

        self.down1 = DoubleConvBlock(in_channels, 64)
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.down2 = DoubleConvBlock(64, 128)
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.down3 = DoubleConvBlock(128, 256)
        self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.down4 = DoubleConvBlock(256, 512)
        self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2)

        self.bottom = DoubleConvBlock(512, 1024)

        self.up1 = nn.ConvTranspose2d(1024, 512, kernel_size=2, stride=2)
        self.conv1 = DoubleConvBlock(1024, 512)
        self.up2 = nn.ConvTranspose2d(512, 256, kernel_size=2, stride=2)
        self.conv2 = DoubleConvBlock(512, 256)
        self.up3 = nn.ConvTranspose2d(256, 128, kernel_size=2, stride=2)
        self.conv3 = DoubleConvBlock(256, 128)
        self.up4 = nn.ConvTranspose2d(128, 64, kernel_size=2, stride=2)
        self.conv4 = DoubleConvBlock(128, 64)

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

    def forward(self, x):
        x1 = self.down1(x)
        x2 = self.pool1(x1)
        x2 = self.down2(x2)
        x3 = self.pool2(x2)
        x3 = self.down3(x3)
        x4 = self.pool3(x3)
        x4 = self.down4(x4)
        x5 = self.pool4(x4)

        x5 = self.bottom(x5)

        x4 = self.up1(x5)
        x4 = torch.cat([x4, x4], dim=1)
        x4 = self.conv1(x4)
        x3 = self.up2(x4)
        x3 = torch.cat([x3, x3], dim=1)
        x3 = self.conv2(x3)
        x2 = self.up3(x3)
        x2 = torch.cat([x2, x2], dim=1)
        x2 = self.conv3(x2)
        x1 = self.up4(x2)
        x1 = torch.cat([x1, x1], dim=1)
        x1 = self.conv4(x1)

        output = self.output(x1)
        return output

这个UNet模型由下采样和上采样阶段组成。下采样由四个DoubleConvBlock和Max Pooling层组成，每个DoubleConvBlock由两个卷积层和激活函数组成。在最后一个下采样阶段，特征图的分辨率被减半到原来的1/16。上采样阶段由四个ConvTranspose2d层和DoubleConvBlock组成，每个ConvTranspose2d层将特征图的分辨率增加2倍。在每个上采样阶段，特征图先通过ConvTranspose2d层进行上采样，然后与对应的下采样阶段的特征图进行拼接，最后通过DoubleConvBlock进行特征融合。最后，输出层使用一个卷积层将特征图映射到指定的输出通道数。