【机器学习系列（7）】图神经网络与实时推理：构建高阶智能系统

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【机器学习系列（7）】图神经网络与实时推理：构建高阶智能系统

一、图神经网络(GNN)基础理论

1. 图卷积网络(GCN)公式

$H(l+1)=σ(D^−1/2A^D^−1/2H(l)W(l))H^{(l+1)} = \sigma\left(\hat{D}^{-1/2}\hat{A}\hat{D}^{-1/2}H^{(l)}W^{(l)}\right)$

2. Cora数据集节点分类实战

import torch_geometric
from torch_geometric.datasets import Planetoid
from torch_geometric.nn import GCNConv

dataset = Planetoid(root='/tmp/Cora', name='Cora')

class GNN(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = GCNConv(dataset.num_features, 16)
        self.conv2 = GCNConv(16, dataset.num_classes)

    def forward(self, data):
        x, edge_index = data.x, data.edge_index
        x = torch.nn.functional.relu(self.conv1(x, edge_index))
        x = torch.nn.functional.dropout(x, training=self.training)
        return self.conv2(x, edge_index)

model = GNN()
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)

# 训练循环（简化版）
for epoch in range(200):
    model.train()
    optimizer.zero_grad()
    out = model(dataset.data)
    loss = torch.nn.functional.cross_entropy(out[dataset.data.train_mask], 
                                            dataset.data.y[dataset.data.train_mask])
    loss.backward()
    optimizer.step()

二、异构图表示学习

1. 元路径建模范式

import dgl
import dgl.nn as dglnn

# 构建学术网络异构图
hetero_graph = dgl.heterograph({
    ('author', 'writes', 'paper'): (authors, papers),
    ('paper', 'cites', 'paper'): (citing_papers, cited_papers),
    ('paper', 'has_topic', 'field'): (papers, fields)
})

class HAN(nn.Module):
    def __init__(self, meta_paths, in_dim, hidden_dim, out_dim):
        super().__init__()
        self.layers = nn.ModuleList()
        self.layers.append(dglnn.HeteroGraphConv({
            mp: dglnn.GraphConv(in_dim, hidden_dim)
            for mp in meta_paths}))
        self.layers.append(dglnn.HeteroGraphConv({
            mp: dglnn.GraphConv(hidden_dim, out_dim) 
            for mp in meta_paths}))

    def forward(self, g, inputs):
        for layer in self.layers:
            inputs = layer(g, inputs)
        return inputs

# 定义元路径集合
meta_paths = [
    [('author', 'writes', 'paper'), ('paper', 'cites', 'paper')],
    [('paper', 'has_topic', 'field'), ('field', 'has_paper', 'paper')]
]

model = HAN(meta_paths, in_dim=128, hidden_dim=64, out_dim=16)

三、实时推理优化策略

1. TensorRT模型加速

import tensorrt as trt

# 将PyTorch模型转换为ONNX
torch.onnx.export(model, 
                 dummy_input, 
                 "gnn.onnx",
                 input_names=["node_features", "edge_index"],
                 dynamic_axes={'node_features': {0: 'num_nodes'}})

# 转换到TensorRT
TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
with trt.Builder(TRT_LOGGER) as builder:
    network = builder.create_network(1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
    parser = trt.OnnxParser(network, TRT_LOGGER)
    with open("gnn.onnx", "rb") as f:
        parser.parse(f.read())
  
    config = builder.create_builder_config()
    config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, 1 << 30)
    serialized_engine = builder.build_serialized_network(network, config)
  
    with open("gnn.engine", "wb") as f:
        f.write(serialized_engine)