Unet的pytorch代码

以下是使用PyTorch实现Unet的代码：

import torch
import torch.nn as nn

class DoubleConv(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(DoubleConv, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True)
        )

    def forward(self, x):
        return self.conv(x)

class UNet(nn.Module):
    def __init__(self, in_channels=3, out_channels=1, features=[64, 128, 256, 512]):
        super(UNet, self).__init__()
        self.ups = nn.ModuleList()
        self.downs = nn.ModuleList()
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)

        # Down part of UNet
        for feature in features:
            self.downs.append(DoubleConv(in_channels, feature))
            in_channels = feature

        # Up part of UNet
        for feature in reversed(features):
            self.ups.append(nn.ConvTranspose2d(feature*2, feature, kernel_size=2, stride=2))
            self.ups.append(DoubleConv(feature*2, feature))

        self.bottleneck = DoubleConv(features[-1], features[-1]*2)
        self.final_conv = nn.Conv2d(features[0], out_channels, kernel_size=1)

    def forward(self, x):
        skip_connections = []

        # Down part of UNet
        for down in self.downs:
            x = down(x)
            skip_connections.append(x)
            x = self.pool(x)

        # Bottom part of UNet
        x = self.bottleneck(x)

        # Up part of UNet
        skip_connections = skip_connections[::-1]
        for idx in range(0, len(self.ups), 2):
            x = self.ups[idx](x)
            skip_connection = skip_connections[idx//2]

            if x.shape != skip_connection.shape:
                x = nn.functional.interpolate(x, size=skip_connection.shape[2:], mode='bilinear', align_corners=True)

            concat_skip = torch.cat((skip_connection, x), dim=1)
            x = self.ups[idx+1](concat_skip)

        return self.final_conv(x)

上面的代码实现了一个基本的UNet模型，包括一个双卷积模块和下采样模块(down)，上采样模块(up)，还有一个瓶颈部分(bottleneck)。在forward函数中，首先使用下采样模块(down)将输入图像降采样，然后进入瓶颈部分(bottleneck)，最后使用上采样模块(up)将图像上采样到原始大小。这个模型可以用于图像分割任务。