请使用unet项目中的典型代码结构，通过代码的形式向我介绍各部分，从前向传播函数的定义过程开始如 def forward(self, x):

好的，下面是使用PyTorch实现的Unet模型的典型代码结构：

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)

        # 下采样
        for feature in features:
            self.downs.append(DoubleConv(in_channels, feature))
            in_channels = feature

        # 上采样
        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 = []

        for down in self.downs:
            x = down(x)
            skip_connections.append(x)
            x = self.pool(x)

        x = self.bottleneck(x)
        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)

代码的解释如下：

1. `DoubleConv`是一个由两个卷积层和批量归一化层组成的模块，它用于下采样和上采样中的每个层。在构造函数中，我们指定输入通道数和输出通道数，然后创建一个包含两个卷积层和批量归一化层的序列。在正向传播函数中，我们只需将输入张量传递给这个序列，并返回输出张量。

2. `Unet`类是Unet模型的主要部分。在构造函数中，我们首先创建两个模块列表`self.ups`和`self.downs`，它们分别用于存储下采样和上采样的模块。我们还创建一个最大池化层`self.pool`，用于下采样中的每个层。`features`参数是