构建一个ST-GCN（Spatio-Temporal Graph Convolutional Network）模型需要结合图卷积网络（GCN）的思想，以处理时空数据。以下是一个简单的例子，演示如何使用PyTorch构建ST-GCN模型： 

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

class STGraphConvolution(nn.Module):
    def __init__(self, in_channels, out_channels, graph_matrix):
        super(STGraphConvolution, self).__init__()
        self.graph_matrix = graph_matrix
        self.weight = nn.Parameter(torch.rand(in_channels, out_channels))
        self.bias = nn.Parameter(torch.zeros(out_channels))

    def forward(self, x):
        batch_size, num_nodes, num_frames, num_features = x.size()
        x = x.view(batch_size, num_nodes * num_frames, num_features)  # Reshape for graph convolution

        adjacency_matrix = self.graph_matrix.view(num_nodes, num_nodes).to(x.device)
        adjacency_matrix = F.normalize(adjacency_matrix, p=1, dim=1)  # Normalize adjacency matrix

        x = torch.matmul(x, self.weight)
        x = torch.matmul(adjacency_matrix, x)
        x = x.view(batch_size, num_nodes, num_frames, -1) + self.bias.view(1, -1, 1, 1)

        return x

class STGCN(nn.Module):
    def __init__(self, in_channels, spatial_channels, temporal_channels, graph_matrix):
        super(STGCN, self).__init__()
        self.graph_conv1 = STGraphConvolution(in_channels, spatial_channels, graph_matrix)
        self.graph_conv2 = STGraphConvolution(spatial_channels, temporal_channels, graph_matrix)

    def forward(self, x):
        x = self.graph_conv1(x)
        x = F.relu(x)
        x = self.graph_conv2(x)
        x = F.relu(x)
        return x

# 示例用法
num_nodes = 10  # 假设有10个节点
in_channels = 3  # 输入通道数，根据你的数据而定
spatial_channels = 64  # 空间通道数，根据你的数据而定
temporal_channels = 32  # 时间通道数，根据你的数据而定

# 生成一个随机的邻接矩阵作为示例
graph_matrix = torch.randn((num_nodes, num_nodes))

model = STGCN(in_channels, spatial_channels, temporal_channels, graph_matrix)

# 随机生成输入数据
input_data = torch.randn((2, num_nodes, 5, in_channels))

# 输出结果
output = model(input_data)
print("Input shape:", input_data.shape)
print("Output shape:", output.shape)

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