怎么用nn.lstm和nn.conv2d搭建convlstm

可以使用nn.ConvLSTM2d来搭建ConvLSTM模型，它可以结合nn.LSTM和nn.Conv2d的功能。首先，需要定义ConvLSTM层的输入和输出通道数、卷积核大小、步幅和填充，然后将其传递给nn.ConvLSTM2d函数。在模型的前向传递过程中，可以使用nn.Conv2d和nn.LSTM来处理输入数据和隐藏状态。具体实现可以参考PyTorch官方文档中的例子。

相关问题

class TPCNN(nn.Module): def __init__(self, num_class=10, head_payload=False): super(TPCNN, self).__init__() # 上 self.uconv1 = nn.Sequential( # nn.Conv2d(1, 16, kernel_size=3, stride=1, padding=1, dilation=1, bias=True), nn.BatchNorm2d(16, eps=1e-05, momentum=0.9, affine=True), nn.ReLU(), ) self.uconv2 = nn.Sequential( # nn.Conv2d(16, 32, kernel_size=3, stride=2, padding=1, dilation=1, bias=True), nn.BatchNorm2d(32, eps=1e-05, momentum=0.9, affine=True), nn.ReLU(), ) # 中 self.mconv1 = nn.Sequential( # nn.Conv2d(1, 32, kernel_size=3, stride=2, padding=1, dilation=1, bias=True), nn.BatchNorm2d(32, eps=1e-05, momentum=0.9, affine=True), nn.ReLU(), ) # 下 self.dconv1 = nn.Sequential( # nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1, dilation=1, bias=True), nn.BatchNorm2d(32, eps=1e-05, momentum=0.9, affine=True), nn.ReLU(), nn.MaxPool2d(kernel_size=2) ) self.uconv3 = nn.Sequential( # nn.Conv2d(96, 128, kernel_size=3, stride=1, padding=1, dilation=1, bias=True), nn.BatchNorm2d(128, eps=1e-05, momentum=0.9, affine=True), nn.ReLU(), ) self.mconv2 = nn.Sequential( # nn.Conv2d(96, 128, kernel_size=3, stride=2, padding=1, dilation=1, bias=True), nn.BatchNorm2d(128, eps=1e-05, momentum=0.9, affine=True), nn.ReLU(), ) self.dconv2 = nn.Sequential( # nn.Conv2d(96, 128, kernel_size=3, stride=1, padding=1, dilation=1, bias=True), nn.BatchNorm2d(128, eps=1e-05, momentum=0.9, affine=True), nn.ReLU(), ) self.uconv4 = nn.Sequential( # nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1, dilation=1, bias=True), nn.BatchNorm2d(512, eps=1e-05, momentum=0.9, affine=True), nn.ReLU(), ) self.globalconv1 = nn.Sequential( nn.Conv2d(896, 1024, kernel_size=3, stride=1, padding=1), nn.BatchNorm2d(1024, eps=1e-05, momentum=0.9, affine=True), nn.ReLU() ) self.dmaxpool = nn.MaxPool2d(kernel_size=2,padding=1) # self.lstm1 = nn.LSTM(256,512, 2) # self.lstm2 = nn.LSTM(self.i_size*2,self.i_size*2, 2) self.avpool = nn.AdaptiveAvgPool2d(2) # self.globallstm = nn.LSTM(512, 256, 1) self.fc1 = nn.Linear(1024*2*2, 512) self.fc2 = nn.Linear(512, num_class)

这段代码定义了一个名为TPCNN的类，继承自nn.Module。它具有一个num_class参数，默认值为10，以及一个head_payload参数，其默认值为False。在__init__函数中，它首先调用父类的构造函数，然后定义了该类的其余属性和方法。

torch.nn没有ConvLSTM2d怎么回事

确实，PyTorch的torch.nn模块中没有提供ConvLSTM2d。不过，你可以使用第三方库如`kornia`或`torchkbnufft`等提供的实现来使用ConvLSTM2d。

也可以自己实现ConvLSTM2d。实现方式类似于标准的LSTM，但是将线性变换替换为卷积操作。以下是一个简单的示例代码：

import torch
import torch.nn as nn

class ConvLSTM2dCell(nn.Module):
    def __init__(self, in_channels, hidden_channels, kernel_size):
        super(ConvLSTM2dCell, self).__init__()

        padding = kernel_size // 2
        
        self.conv_xh = nn.Conv2d(in_channels, hidden_channels, kernel_size, padding=padding)
        self.conv_hh = nn.Conv2d(hidden_channels, hidden_channels, kernel_size, padding=padding)

    def forward(self, x, h, c):
        input = torch.cat([x, h], dim=1)

        gates = self.conv_xh(input) + self.conv_hh(h)

        i_gate, f_gate, o_gate, g_gate = torch.split(gates, gates.size(1) // 4, dim=1)

        i_gate = torch.sigmoid(i_gate)
        f_gate = torch.sigmoid(f_gate)
        o_gate = torch.sigmoid(o_gate)
        g_gate = torch.tanh(g_gate)

        c_new = f_gate * c + i_gate * g_gate
        h_new = o_gate * torch.tanh(c_new)

        return h_new, c_new


class ConvLSTM2d(nn.Module):
    def __init__(self, in_channels, hidden_channels, kernel_size, num_layers):
        super(ConvLSTM2d, self).__init__()

        self.in_channels = in_channels
        self.hidden_channels = hidden_channels
        self.kernel_size = kernel_size
        self.num_layers = num_layers

        cell_list = []
        for i in range(num_layers):
            if i == 0:
                input_channels = in_channels
            else:
                input_channels = hidden_channels

            cell_list.append(ConvLSTM2dCell(input_channels, hidden_channels, kernel_size))

        self.cell_list = nn.ModuleList(cell_list)

    def forward(self, x):
        batch_size, seq_len, channels, height, width = x.size()
        h_list = [torch.zeros(batch_size, self.hidden_channels, height, width).to(x.device)]
        c_list = [torch.zeros(batch_size, self.hidden_channels, height, width).to(x.device)]

        for layer_idx in range(self.num_layers):
            for seq_idx in range(seq_len):
                x_input = x[:, seq_idx, :, :, :]
                h, c = h_list[-1], c_list[-1]
                h_new, c_new = self.cell_list[layer_idx](x_input, h, c)
                h_list.append(h_new)
                c_list.append(c_new)

        return torch.stack(h_list[1:], dim=1)

这是一个ConvLSTM2d的简单实现，包含了单个ConvLSTM2d单元和多层ConvLSTM2d的实现。你可以根据自己的需要进行调整和修改。