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浩然/OpenHGNN_clone_from_github

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main.py 1.61 KB
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loadingyy 提交于 2024-07-11 12:03 . SeHGNN (#198)
# @Time : 2021/1/28
# @Author : Tianyu Zhao
# @Email : tyzhao@bupt.edu.cn
import argparse
from openhgnn.experiment import Experiment
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--model', '-m', default='GTN', type=str, help='name of models')
parser.add_argument('--task', '-t', default='node_classification', type=str, help='name of task')
# link_prediction / node_classification
parser.add_argument('--dataset', '-d', default='acm4GTN', type=str, help='name of datasets')
parser.add_argument('--gpu', '-g', default='0', type=int, help='-1 means cpu')
parser.add_argument('--use_distributed', action='store_true', help='will use distributed training')
parser.add_argument('--use_best_config', action='store_true', help='will load utils.best_config')
parser.add_argument('--load_from_pretrained', action='store_true', help='load model from the checkpoint')
parser.add_argument('--use_database', action='store_true',help = 'use database')
parser.add_argument('--mini_batch_flag', action='store_true', help='will train in mini_batch mode')
parser.add_argument('--graphbolt',action='store_true',help = 'use graphbolt to access dataset')
args = parser.parse_args()
experiment = Experiment(model=args.model, dataset=args.dataset, task=args.task, gpu=args.gpu,
use_best_config=args.use_best_config, load_from_pretrained=args.load_from_pretrained,
mini_batch_flag=args.mini_batch_flag, use_distributed = args.use_distributed,
graphbolt = args.graphbolt)
experiment.run()
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