Python转为C语言并编译生成二进制文件的教程详解

pip3 install torch torchvision torchaudio
pip install dgl -f https://data.dgl.ai/wheels/cu117/repo.html
pip install dglgo -f https://data.dgl.ai/wheels-test/repo.html

# cython: language_level=3
 
import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl
from dgl.data import CoraGraphDataset
from dgl.nn import GraphConv
 
# 定义 GCN 模型
class GCN(nn.Module):
    def __init__(self, in_feats, h_feats, num_classes):
        super(GCN, self).__init__()
        self.conv1 = GraphConv(in_feats, h_feats)
        self.conv2 = GraphConv(h_feats, num_classes)
 
    def forward(self, g, in_feat):
        h = self.conv1(g, in_feat)
        h = F.relu(h)
        h = self.conv2(g, h)
        return h
 
if __name__ == "__main__":
    # 加载数据集
    dataset = CoraGraphDataset()
    g = dataset[0]
 
    # 创建模型实例
    model = GCN(g.ndata['feat'].shape[1], 16, dataset.num_classes)
 
    # 定义损失函数和优化器
    optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
    criterion = nn.CrossEntropyLoss()
 
    # 训练模型
    for epoch in range(200):
        logits = model(g, g.ndata['feat'])
        loss = criterion(logits[g.ndata['train_mask']], g.ndata['label'][g.ndata['train_mask']])
        
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
 
        if epoch % 10 == 0:
            print(f'Epoch {epoch}, Loss: {loss.item()}')
 
    # 测试模型
    model.eval()
    with torch.no_grad():
        logits = model(g, g.ndata['feat'])
        _, predicted = torch.max(logits[g.ndata['test_mask']], 1)
        correct = (predicted == g.ndata['label'][g.ndata['test_mask']]).sum().item()
        acc = correct / len(predicted)
        print(f'Accuracy: {acc:.4f}')

cython gcn.py --embed

gcc -c gcn.c `python3-config --includes` `python3-config --ldflags` -o gcn.o

gcc gcn.o -L/home/sxf/anaconda3/envs/dgl/lib  -lpython3.9 -o gcn

./gcn

export LD_LIBRARY_PATH=/home/sxf/anaconda3/envs/dgl/lib/:$LD_LIBRARY_PATH