基于convlstm的地铁客流预测模型代码

以下是基于ConvLSTM的地铁客流预测模型的PyTorch代码示例：

import torch
import torch.nn as nn
from torch.autograd import Variable

class ConvLSTMCell(nn.Module):
    def __init__(self, input_channels, hidden_channels, kernel_size):
        super(ConvLSTMCell, self).__init__()

        self.input_channels = input_channels
        self.hidden_channels = hidden_channels
        self.kernel_size = kernel_size
        self.num_features = 4

        self.padding = int((kernel_size - 1) / 2)
        self.conv = nn.Conv2d(in_channels=self.input_channels + self.hidden_channels,
                              out_channels=self.num_features * self.hidden_channels,
                              kernel_size=self.kernel_size,
                              padding=self.padding)

    def forward(self, input, hx):
        if hx is None:
            hx = (Variable(torch.zeros(input.size(0), self.hidden_channels, input.size(2), input.size(3)).cuda()),
                  Variable(torch.zeros(input.size(0), self.hidden_channels, input.size(2), input.size(3)).cuda()))

        h_cur, c_cur = hx

        combined = torch.cat([input, h_cur], dim=1)
        gates = self.conv(combined)

        i_gate, f_gate, o_gate, g_gate = torch.split(gates, self.hidden_channels, 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_next = (f_gate * c_cur) + (i_gate * g_gate)
        h_next = o_gate * torch.tanh(c_next)

        return h_next, c_next

class ConvLSTM(nn.Module):
    def __init__(self, input_channels, hidden_channels, kernel_size, num_layers, batch_first=False):
        super(ConvLSTM, self).__init__()

        self.input_channels = [input_channels] + hidden_channels
        self.hidden_channels = hidden_channels
        self.kernel_size = kernel_size
        self.num_layers = num_layers
        self.batch_first = batch_first

        cell_list = []
        for i in range(self.num_layers):
            cur_input_channels = self.input_channels[i]
            cur_hidden_channels = self.hidden_channels[i]
            cell_list.append(ConvLSTMCell(input_channels=cur_input_channels,
                                          hidden_channels=cur_hidden_channels,
                                          kernel_size=self.kernel_size))

        self.cell_list = nn.ModuleList(cell_list)

    def forward(self, input, hidden_state=None):
        if hidden_state is None:
            hidden_state = [None] * self.num_layers

        seq_len = input.size(1) if self.batch_first else input.size(0)
        cur_layer_input = input
        new_hidden_state = []

        for layer_idx in range(self.num_layers):
            h, c = hidden_state[layer_idx]

            layer_output = []
            for t in range(seq_len):
                if self.batch_first:
                    x_t = cur_layer_input[:, t, :, :, :]
                else:
                    x_t = cur_layer_input[t, :, :, :, :]

                h, c = self.cell_list[layer_idx](input=x_t, hx=(h, c))
                layer_output.append(h)

            cur_layer_input = torch.stack(layer_output, dim=1) if self.batch_first else torch.stack(layer_output, dim=0)
            new_hidden_state.append((h, c))

        return cur_layer_input, new_hidden_state

class ConvLSTMForecast(nn.Module):
    def __init__(self, input_channels, hidden_channels, kernel_size, num_layers):
        super(ConvLSTMForecast, self).__init__()

        self.conv_lstm = ConvLSTM(input_channels=input_channels,
                                  hidden_channels=hidden_channels,
                                  kernel_size=kernel_size,
                                  num_layers=num_layers,
                                  batch_first=True)

        self.conv1 = nn.Conv2d(hidden_channels[-1], 16, kernel_size=(1, 1))
        self.bn1 = nn.BatchNorm2d(16)
        self.relu1 = nn.ReLU(inplace=True)

        self.conv2 = nn.Conv2d(16, 8, kernel_size=(1, 1))
        self.bn2 = nn.BatchNorm2d(8)
        self.relu2 = nn.ReLU(inplace=True)

        self.conv3 = nn.Conv2d(8, 1, kernel_size=(1, 1))

    def forward(self, input):
        output, _ = self.conv_lstm(input)
        output = self.conv1(output[:, -1, :, :, :])
        output = self.bn1(output)
        output = self.relu1(output)
        output = self.conv2(output)
        output = self.bn2(output)
        output = self.relu2(output)
        output = self.conv3(output)

        return output

这里的ConvLSTM模型由两个部分组成：ConvLSTM层和预测层。ConvLSTM层接收一个3D张量作为输入，每个时间步长的张量都有一个时间维度。预测层将ConvLSTM层的输出转换为预测结果。

在这个例子中，模型输入是一个形状为(batch_size, seq_len, channels, height, width)的5D张量，其中seq_len表示时间步长，channels表示输入特征的数量，height和width表示空间维度。模型输出是一个形状为(batch_size, 1, height, width)的4D张量，其中1表示预测的特征的数量。

该模型的训练和测试代码将根据具体问题而异。