将lenet网络和convlstm结合，写代码pytorch

以下是一个简单的示例代码，将LeNet网络和ConvLSTM结合在一起，使用PyTorch实现：

import torch
import torch.nn as nn

class LeNetConvLSTM(nn.Module):
    def __init__(self, input_size, hidden_size, kernel_size):
        super(LeNetConvLSTM, self).__init__()
        
        # LeNet网络部分
        self.conv1 = nn.Conv2d(in_channels=1, out_channels=6, kernel_size=5)
        self.pool1 = nn.MaxPool2d(kernel_size=2)
        self.conv2 = nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5)
        self.pool2 = nn.MaxPool2d(kernel_size=2)
        self.fc1 = nn.Linear(in_features=16*5*5, out_features=120)
        self.fc2 = nn.Linear(in_features=120, out_features=84)
        
        # ConvLSTM部分
        self.lstm = nn.LSTMCell(input_size, hidden_size)
        self.hidden_size = hidden_size
        self.kernel_size = kernel_size
        self.padding = kernel_size // 2
        
    def forward(self, x):
        # LeNet网络部分
        x = self.pool1(torch.relu(self.conv1(x)))
        x = self.pool2(torch.relu(self.conv2(x)))
        x = x.view(-1, 16*5*5)
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        
        # 将输出转换为ConvLSTM所需的格式
        batch_size, channels, height, width = x.shape
        x = x.view(batch_size, channels, height*width)
        x = x.permute(0, 2, 1)
        
        # ConvLSTM部分
        hx = torch.zeros(batch_size, self.hidden_size).to(x.device)
        cx = torch.zeros(batch_size, self.hidden_size).to(x.device)
        for i in range(height*width):
            hx, cx = self.lstm(x[:, i, :], (hx, cx))
            hx = hx.view(batch_size, self.hidden_size, 1, 1)
            cx = cx.view(batch_size, self.hidden_size, 1, 1)
            if i == 0:
                output = hx
            else:
                output = torch.cat((output, hx), dim=1)
        
        # 将输出转换为正常的格式
        output = output.permute(0, 2, 3, 1)
        output = output.view(batch_size, height, width, self.hidden_size)
        
        return output

这个模型将输入图片经过LeNet网络处理后，再输入到ConvLSTM中进行时序处理，并返回输出。你可以根据自己的需求修改LeNet网络和ConvLSTM的参数。