时空多图卷积神经网络的代码

时空多图卷积神经网络（ST-GCN）是一种用于处理时空数据的深度学习模型。它在图卷积神经网络（GCN）的基础上进行了扩展，可以有效地处理时序数据和空间关系。

以下是一个简单的时空多图卷积神经网络的代码示例：

import torch
import torch.nn as nn
from torch_geometric.nn import ChebConv

class STGCN(nn.Module):
    def __init__(self, num_nodes, in_channels, num_classes):
        super(STGCN, self).__init__()
        self.num_nodes = num_nodes
        self.in_channels = in_channels
        self.num_classes = num_classes
        
        self.temporal_conv1 = ChebConv(in_channels, 64, K=2)
        self.temporal_conv2 = ChebConv(64, 64, K=2)
        self.spatial_conv1 = ChebConv(64, 64, K=2)
        self.spatial_conv2 = ChebConv(64, num_classes, K=2)
        
    def forward(self, x, edge_index):
        # x: [batch_size, in_channels, num_nodes, num_timesteps]
        # edge_index: [2, num_edges]
        
        x = x.permute(0, 3, 1, 2)  # [batch_size, num_timesteps, in_channels, num_nodes]
        batch_size, num_timesteps, _, _ = x.size()
        
        x = x.reshape(batch_size * num_timesteps, self.in_channels, self.num_nodes)
        edge_index = edge_index.unsqueeze(0).repeat(batch_size * num_timesteps, 1, 1)
        
        x = self.temporal_conv1(x, edge_index)
        x = self.temporal_conv2(x, edge_index)
        
        x = x.reshape(batch_size, num_timesteps, 64, self.num_nodes)
        x = x.permute(0, 3, 2, 1)  # [batch_size, num_nodes, 64, num_timesteps]
        
        x = x.reshape(batch_size * self.num_nodes, 64, num_timesteps)
        edge_index = edge_index.squeeze(0)
        
        x = self.spatial_conv1(x, edge_index)
        x = self.spatial_conv2(x, edge_index)
        
        x = x.reshape(batch_size, self.num_nodes, self.num_classes, num_timesteps)
        x = x.permute(0, 3, 2, 1)  # [batch_size, num_timesteps, num_classes, num_nodes]
        
        return x

这段代码实现了一个简单的ST-GCN模型，包括两个时间卷积层和两个空间卷积层。你可以根据自己的数据和需求进行修改和扩展。