84 lines
4.0 KiB
Python
84 lines
4.0 KiB
Python
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import argparse
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import warnings
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import torch
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import torch.nn.functional as F
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import torch.optim as optim
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from dgl.dataloading import MultiLayerNeighborSampler, NodeDataLoader
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from tqdm import tqdm
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from gnnrec.hge.hgconv.model import HGConv
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from gnnrec.hge.utils import set_random_seed, get_device, load_data, add_node_feat, evaluate, \
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calc_metrics, METRICS_STR
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def train(args):
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set_random_seed(args.seed)
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device = get_device(args.device)
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data, g, _, labels, predict_ntype, train_idx, val_idx, test_idx, evaluator = \
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load_data(args.dataset, device)
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add_node_feat(g, args.node_feat, args.node_embed_path)
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sampler = MultiLayerNeighborSampler([args.neighbor_size] * args.num_layers)
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train_loader = NodeDataLoader(g, {predict_ntype: train_idx}, sampler, device=device, batch_size=args.batch_size)
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loader = NodeDataLoader(g, {predict_ntype: g.nodes(predict_ntype)}, sampler, device=device, batch_size=args.batch_size)
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model = HGConv(
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{ntype: g.nodes[ntype].data['feat'].shape[1] for ntype in g.ntypes},
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args.num_hidden, data.num_classes, args.num_heads, g.ntypes, g.canonical_etypes,
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predict_ntype, args.num_layers, args.dropout, args.residual
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).to(device)
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optimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
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warnings.filterwarnings('ignore', 'Setting attributes on ParameterDict is not supported')
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for epoch in range(args.epochs):
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model.train()
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losses = []
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for input_nodes, output_nodes, blocks in tqdm(train_loader):
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batch_logits = model(blocks, blocks[0].srcdata['feat'])
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batch_labels = labels[output_nodes[predict_ntype]]
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loss = F.cross_entropy(batch_logits, batch_labels)
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losses.append(loss.item())
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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torch.cuda.empty_cache()
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print('Epoch {:d} | Loss {:.4f}'.format(epoch, sum(losses) / len(losses)))
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if epoch % args.eval_every == 0 or epoch == args.epochs - 1:
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print(METRICS_STR.format(*evaluate(
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model, loader, g, labels, data.num_classes, predict_ntype,
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train_idx, val_idx, test_idx, evaluator
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)))
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embeds = model.inference(g, g.ndata['feat'], device, args.batch_size)
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print(METRICS_STR.format(*calc_metrics(embeds, labels, train_idx, val_idx, test_idx, evaluator)))
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def main():
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parser = argparse.ArgumentParser(description='训练HGConv模型')
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parser.add_argument('--seed', type=int, default=8, help='随机数种子')
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parser.add_argument('--device', type=int, default=0, help='GPU设备')
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parser.add_argument('--dataset', choices=['ogbn-mag', 'oag-venue'], default='ogbn-mag', help='数据集')
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parser.add_argument(
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'--node-feat', choices=['average', 'pretrained'], default='pretrained',
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help='如何获取无特征顶点的输入特征'
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)
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parser.add_argument('--node-embed-path', help='预训练顶点嵌入路径')
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parser.add_argument('--num-hidden', type=int, default=32, help='隐藏层维数')
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parser.add_argument('--num-heads', type=int, default=8, help='注意力头数')
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parser.add_argument('--num-layers', type=int, default=2, help='层数')
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parser.add_argument('--no-residual', action='store_false', help='不使用残差连接', dest='residual')
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parser.add_argument('--dropout', type=float, default=0.5, help='Dropout概率')
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parser.add_argument('--epochs', type=int, default=100, help='训练epoch数')
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parser.add_argument('--batch-size', type=int, default=4096, help='批大小')
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parser.add_argument('--neighbor-size', type=int, default=10, help='邻居采样数')
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parser.add_argument('--lr', type=float, default=0.001, help='学习率')
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parser.add_argument('--weight-decay', type=float, default=0.0, help='权重衰减')
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parser.add_argument('--eval-every', type=int, default=10, help='每多少个epoch计算一次准确率')
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args = parser.parse_args()
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print(args)
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train(args)
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if __name__ == '__main__':
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main()
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