| import argparse |
| import random |
| import time |
| import torch |
| import numpy as np |
| from network import GNet |
| from trainer import Trainer |
| from utils.data_loader import FileLoader |
|
|
|
|
| def get_args(): |
| parser = argparse.ArgumentParser(description='Args for graph predition') |
| parser.add_argument('-seed', type=int, default=1, help='seed') |
| parser.add_argument('-data', default='DD', help='data folder name') |
| parser.add_argument('-fold', type=int, default=1, help='fold (1..10)') |
| parser.add_argument('-num_epochs', type=int, default=2, help='epochs') |
| parser.add_argument('-batch', type=int, default=8, help='batch size') |
| parser.add_argument('-lr', type=float, default=0.001, help='learning rate') |
| parser.add_argument('-deg_as_tag', type=int, default=0, help='1 or degree') |
| parser.add_argument('-l_num', type=int, default=3, help='layer num') |
| parser.add_argument('-h_dim', type=int, default=512, help='hidden dim') |
| parser.add_argument('-l_dim', type=int, default=48, help='layer dim') |
| parser.add_argument('-drop_n', type=float, default=0.3, help='drop net') |
| parser.add_argument('-drop_c', type=float, default=0.2, help='drop output') |
| parser.add_argument('-act_n', type=str, default='ELU', help='network act') |
| parser.add_argument('-act_c', type=str, default='ELU', help='output act') |
| parser.add_argument('-ks', nargs='+', type=float, default='0.9 0.8 0.7') |
| parser.add_argument('-acc_file', type=str, default='re', help='acc file') |
| args, _ = parser.parse_known_args() |
| return args |
|
|
|
|
| def set_random(seed): |
| random.seed(seed) |
| np.random.seed(seed) |
| torch.manual_seed(seed) |
|
|
|
|
| def app_run(args, G_data, fold_idx): |
| G_data.use_fold_data(fold_idx) |
| net = GNet(G_data.feat_dim, G_data.num_class, args) |
| trainer = Trainer(args, net, G_data) |
| trainer.train() |
|
|
|
|
| def main(): |
| args = get_args() |
| print(args) |
| set_random(args.seed) |
| start = time.time() |
| G_data = FileLoader(args).load_data() |
| print('load data using ------>', time.time()-start) |
| if args.fold == 0: |
| for fold_idx in range(10): |
| print('start training ------> fold', fold_idx+1) |
| app_run(args, G_data, fold_idx) |
| else: |
| print('start training ------> fold', args.fold) |
| app_run(args, G_data, args.fold-1) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
