给我一份可逆u-net代码

下面是一个基于PyTorch的可逆U-Net代码：

import torch
import torch.nn as nn
import torch.nn.functional as F


class ReversibleBlock(nn.Module):
    def __init__(self, f, g):
        super(ReversibleBlock, self).__init__()
        self.f = f
        self.g = g

    def forward(self, x):
        x1, x2 = torch.chunk(x, 2, dim=1)
        y1 = x1 + self.f(x2)
        y2 = x2 + self.g(y1)
        return torch.cat([y1, y2], dim=1)


class ReversibleSequence(nn.Module):
    def __init__(self, blocks):
        super(ReversibleSequence, self).__init__()
        self.blocks = nn.ModuleList(blocks)

    def forward(self, x):
        for block in self.blocks:
            x = block(x)
        return x


class ReversibleUNet(nn.Module):
    def __init__(self, in_channels, out_channels, depth, f_block, g_block):
        super(ReversibleUNet, self).__init__()

        self.f_block = f_block
        self.g_block = g_block

        self.bottom_block = ReversibleBlock(self.f_block, self.g_block)

        self.down_blocks = nn.ModuleList()
        for i in range(depth):
            f = nn.Sequential(
                nn.Conv2d(in_channels=2 ** i, out_channels=2 ** (i + 1), kernel_size=3, stride=2, padding=1),
                nn.ReLU(inplace=True),
                self.f_block
            )
            g = nn.Sequential(
                nn.Conv2d(in_channels=2 ** i, out_channels=2 ** (i + 1), kernel_size=3, stride=2, padding=1),
                nn.ReLU(inplace=True),
                self.g_block
            )
            block = ReversibleBlock(f, g)
            self.down_blocks.append(block)

        self.up_blocks = nn.ModuleList()
        for i in reversed(range(depth)):
            f = nn.Sequential(
                nn.ConvTranspose2d(in_channels=2 ** (i + 1), out_channels=2 ** i, kernel_size=3, stride=2, padding=1,
                                   output_padding=1),
                nn.ReLU(inplace=True),
                self.f_block
            )
            g = nn.Sequential(
                nn.ConvTranspose2d(in_channels=2 ** (i + 1), out_channels=2 ** i, kernel_size=3, stride=2, padding=1,
                                   output_padding=1),
                nn.ReLU(inplace=True),
                self.g_block
            )
            block = ReversibleBlock(f, g)
            self.up_blocks.append(block)

        self.output_block = nn.Conv2d(in_channels=2, out_channels=out_channels, kernel_size=1)

    def forward(self, x):
        x = x.unsqueeze(0)
        xs = [x]
        for block in self.down_blocks:
            x = block(x)
            xs.append(x)
        x = self.bottom_block(x)
        for block, skip in zip(self.up_blocks, reversed(xs[:-1])):
            x = block(x)
            x = torch.cat([x, skip], dim=1)
        x = self.output_block(x)
        return x.squeeze(0)

其中，ReversibleBlock是可逆块，由两个函数f和g组成，f和g都是nn.Module。ReversibleSequence是可逆块的序列，由多个可逆块组成。ReversibleUNet是可逆U-Net模型，由多个可逆块序列组成。

在实现f和g时，需要保证它们是可逆的。可以考虑使用可逆层（如可逆卷积层，可逆池化层）或者使用自定义的可逆函数。

需要注意的是，由于可逆U-Net中每个可逆块的输入和输出通道数都是相同的，因此需要在每个可逆块中将输入张量切分为两个部分，分别进行计算，然后将两个部分合并。这里使用了`torch.chunk`和`torch.cat`函数实现。