基于pytorch的unet 和RNN的结合代码

以下是基于PyTorch的UNet和RNN结合的代码示例：

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

class UNetRNN(nn.Module):
    def __init__(self, input_channels, hidden_size, num_layers):
        super(UNetRNN, self).__init__()
        self.input_channels = input_channels
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        
        # UNet encoder
        self.conv1 = nn.Conv2d(input_channels, 64, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
        self.conv3 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
        self.conv4 = nn.Conv2d(256, 512, kernel_size=3, padding=1)
        self.conv5 = nn.Conv2d(512, 1024, kernel_size=3, padding=1)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        
        # UNet decoder
        self.upconv6 = nn.ConvTranspose2d(1024, 512, kernel_size=2, stride=2)
        self.conv6 = nn.Conv2d(1024, 512, kernel_size=3, padding=1)
        self.upconv7 = nn.ConvTranspose2d(512, 256, kernel_size=2, stride=2)
        self.conv7 = nn.Conv2d(512, 256, kernel_size=3, padding=1)
        self.upconv8 = nn.ConvTranspose2d(256, 128, kernel_size=2, stride=2)
        self.conv8 = nn.Conv2d(256, 128, kernel_size=3, padding=1)
        self.upconv9 = nn.ConvTranspose2d(128, 64, kernel_size=2, stride=2)
        self.conv9 = nn.Conv2d(128, 64, kernel_size=3, padding=1)
        self.conv10 = nn.Conv2d(64, input_channels, kernel_size=1)
        
        # RNN layers
        self.rnn = nn.RNN(input_size=input_channels, hidden_size=hidden_size, num_layers=num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_size, input_channels)
        
    def forward(self, x, h0=None):
        # UNet encoding
        x1 = F.relu(self.conv1(x))
        x2 = self.pool(x1)
        x2 = F.relu(self.conv2(x2))
        x3 = self.pool(x2)
        x3 = F.relu(self.conv3(x3))
        x4 = self.pool(x3)
        x4 = F.relu(self.conv4(x4))
        x5 = self.pool(x4)
        x5 = F.relu(self.conv5(x5))
        
        # UNet decoding
        x6 = self.upconv6(x5)
        x6 = torch.cat([x6, x4], dim=1)
        x6 = F.relu(self.conv6(x6))
        x7 = self.upconv7(x6)
        x7 = torch.cat([x7, x3], dim=1)
        x7 = F.relu(self.conv7(x7))
        x8 = self.upconv8(x7)
        x8 = torch.cat([x8, x2], dim=1)
        x8 = F.relu(self.conv8(x8))
        x9 = self.upconv9(x8)
        x9 = torch.cat([x9, x1], dim=1)
        x9 = F.relu(self.conv9(x9))
        x10 = self.conv10(x9)
        
        # RNN encoding
        batch_size, channels, height, width = x10.size()
        x10 = x10.permute(0, 2, 3, 1).contiguous().view(batch_size, height, width, channels)
        x10 = x10.view(batch_size, height * width, channels)
        if h0 is None:
            h0 = torch.zeros(self.num_layers, batch_size, self.hidden_size).to(x.device)
        x10, hn = self.rnn(x10, h0)
        x10 = self.fc(x10)
        x10 = x10.view(batch_size, height, width, channels).permute(0, 3, 1, 2)

        return x10, hn

在这个例子中，我们定义了一个名为`UNetRNN`的类，它包含了一个UNet编码器和解码器，以及一个RNN编码器。在前向传递中，我们首先对输入图像进行UNet编码，然后将编码结果传递给RNN编码器。最后，我们将RNN编码器的输出传递给UNet解码器，以生成最终的输出图像。

注意，在RNN编码器中，我们使用了`nn.RNN`模块，它接受一个批次、序列长度和特征数的输入，并返回一个新的序列和最后一个隐藏状态。我们还添加了一个全连接层，将隐藏状态映射到输出通道数。

此外，在每个UNet解码器中，我们使用了转置卷积层来将特征图的大小增加一倍。我们还使用了`torch.cat`操作来将编码器和解码器的特征图连接起来。

最后，在RNN编码器的第一次迭代中，我们需要初始化隐藏状态。在这个例子中，我们默认使用全零张量作为初始隐藏状态，但你也可以根据需要自定义初始隐藏状态。