67 lines
2.7 KiB
Python
67 lines
2.7 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 gnnrec.hge.hgt.model import HGTFull
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from gnnrec.hge.utils import set_random_seed, get_device, load_data, add_node_feat, evaluate_full, \
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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, _ = \
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load_data(args.dataset, device)
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add_node_feat(g, 'one-hot')
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model = HGTFull(
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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
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).to(device)
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optimizer = optim.AdamW(model.parameters(), eps=1e-6)
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scheduler = optim.lr_scheduler.OneCycleLR(
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optimizer, args.max_lr, epochs=args.epochs, steps_per_epoch=1,
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pct_start=0.05, anneal_strategy='linear', final_div_factor=10.0
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)
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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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logits = model(g, g.ndata['feat'])
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loss = F.cross_entropy(logits[train_idx], labels[train_idx])
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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scheduler.step()
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torch.cuda.empty_cache()
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print(('Epoch {:d} | Loss {:.4f} | ' + METRICS_STR).format(
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epoch, loss.item(), *evaluate_full(model, g, labels, train_idx, val_idx, test_idx)
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))
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if args.save_path:
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torch.save(model.cpu().state_dict(), args.save_path)
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print('模型已保存到', args.save_path)
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def main():
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parser = argparse.ArgumentParser(description='训练HGT模型(full-batch)')
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parser.add_argument('--seed', type=int, default=1, 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=['acm', 'dblp'], default='acm', help='数据集')
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parser.add_argument('--num-hidden', type=int, default=512, 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('--dropout', type=float, default=0.5, help='Dropout概率')
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parser.add_argument('--epochs', type=int, default=10, help='训练epoch数')
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parser.add_argument('--max-lr', type=float, default=5e-4, help='学习率上界')
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parser.add_argument('--save-path', help='模型保存路径')
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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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