多模态动态图卷积神经网络及Transformer多头注意力机制设计

多模态动态图卷积神经网络及Transformer多头注意力机制设计

下面是一个基于PyTorch实现的融合动态脑功能网络、EEG和fNIRS三种模态数据的深度学习模型。该模型结合了动态图卷积网络、Transformer多头注意力和多模态融合技术。

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.nn import GCNConv, DynamicEdgeConv
from torch_geometric.data import Data
from einops import rearrange, repeatclass DynamicGCN(nn.Module):"""动态图卷积网络模块，处理脑功能网络数据"""def __init__(self, node_features, hidden_dim, time_steps):super().__init__()self.time_steps = time_stepsself.conv_layers = nn.ModuleList([GCNConv(node_features if i==0 else hidden_dim, hidden_dim)for i in range(2)])self.edge_conv = DynamicEdgeConv(nn=nn.Sequential(nn.Linear(2 * hidden_dim, hidden_dim),nn.ReLU(),nn.Linear(hidden_dim, hidden_dim)), k=5)self.temporal_attn = nn.MultiheadAttention(hidden_dim, 4, batch_first=True)self.ln = nn.LayerNorm(hidden_dim)def forward(self, x, edge_index):# x: [batch, time_steps, nodes, features]batch_size, time_steps, num_nodes, _ = x.shapex = rearrange(x, 'b t n f -> (b t n) f')edge_index = repeat(edge_index, 'n e -> (b t) n e', b=batch_size, t=time_steps)for conv in self.conv_layers:x = conv(x, edge_index)x = F.relu(x)x = rearrange(x, '(b t n) f -> b t n f', b=batch_size, t=time_steps, n=num_nodes)temporal_features = []for t in range(time_steps):node_features = x[:, t]dynamic_edge_index = self.edge_conv(node_features)dynamic_features = self.edge_conv.nn(torch.cat([node_features[dynamic_edge_index[0]], node_features[dynamic_edge_index[1]]], dim=-1))temporal_features.append(dynamic_features.mean(dim=1))temporal_features = torch.stack(temporal_features, dim=1)attn_out, _ = self.temporal_attn