args.py

import argparse

def parse_args():
    parser = argparse.ArgumentParser()

    parser.add_argument('--device', type=str, default='cuda:0', help='device')
    parser.add_argument('--seed', type=int, default=100, help='seed')
    parser.add_argument('--dataset', type=str, choices=['Cora', 'CiteSeer', 'PubMed', 'Amazon-Photo', 'Amazon-Computers', 'Coauthor-CS'], default='Cora', help='dataset name')
    parser.add_argument('--data_path', type=str, default='dataset/', help='data path')
    parser.add_argument('--imb_ratio', type=float, default=100, help='imbalance ratio')
    parser.add_argument('--net', type=str, choices=['GCN', 'GAT', 'SAGE'], default='GCN', help='GNN bachbone')
    parser.add_argument('--n_layer', type=int, default=2, help='the number of layers')
    parser.add_argument('--feat_dim', type=int, default=64, help='feature dimension')
    parser.add_argument('--warmup', type=int, default=5, help='warmup epoch')
    parser.add_argument('--epoch', type=int, default=900, help='epoch')
    parser.add_argument('--lr', type=float, default=0.01, help='learning rate')
    parser.add_argument('--tau', type=int, default=2, help='temperature in the softmax function when calculating confidence-based node hardness')
    parser.add_argument('--max', action="store_true", help='synthesizing to max or mean num of training set. default is mean') 
    parser.add_argument('--no_mask', action="store_true", help='whether to mask the self class in sampling neighbor classes. default is mask')
    parser.add_argument('--gdc', type=str, choices=['ppr', 'hk', 'none'], default='ppr', help='how to convert to weighted graph')
    args = parser.parse_args()

    return args