(args)
| 5 | random.seed(8026728) |
| 6 | |
| 7 | def load_data(args): |
| 8 | available_data = os.listdir(args.data_path) |
| 9 | |
| 10 | available_data_temp = [] |
| 11 | for i in available_data: |
| 12 | if not 'zero' in i: |
| 13 | available_data_temp.append(i) |
| 14 | train_data_keys = random.sample(available_data_temp, args.train_data_number) |
| 15 | test_data_keys = [ i for i in available_data if i not in train_data_keys ] |
| 16 | test_data_keys = random.sample(test_data_keys, args.test_data_number) |
| 17 | data = {} |
| 18 | for k in available_data: |
| 19 | g = dgl.load_graphs('graph/{}'.format(k))[0][0].to('cuda') |
| 20 | g.edges['net_out'].data['net_delays_log'] = (torch.log(0.0001 + g.edges['net_out'].data['net_delay']) + 9.211) # log(0.0001) ≈ -9.2103 |
| 21 | data[k] = g |
| 22 | data_train = {k: t for k, t in data.items() if k in train_data_keys} |
| 23 | data_test = {k: t for k, t in data.items() if k in test_data_keys} |
| 24 | return data_train, data_test |
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