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parser.add_argument('--scheduler', action='store_true', default=False,
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help='use a scheduler to reduce learning rate')
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# Log settings.
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parser.add_argument('--save_appendix', type=str, default='',
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help='appendix to save results')
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parser.add_argument('--log_steps', type=int, default=10,
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help='save model checkpoint every x epochs')
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parser.add_argument('--continue_from', type=int, default=None,
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help="from which epoch's checkpoint to continue training")
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parser.add_argument('--run_from', type=int, default=1,
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help="from which run (of multiple repeated experiments) to start")
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# Visualization settings.
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parser.add_argument('--visualize_all', action='store_true', default=False,
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help='visualize all graphs in dataset sequentially')
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parser.add_argument('--visualize_test', action='store_true', default=False,
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help='visualize test graphs by loss')
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parser.add_argument('--pre_visualize', action='store_true', default=False)
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args = parser.parse_args()
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device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
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# Save directory.
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if args.save_appendix == '':
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args.save_appendix = '_' + time.strftime("%Y%m%d%H%M%S")
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args.res_dir = 'results/{}{}'.format(args.dataset, args.save_appendix)
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print('Results will be saved in ' + args.res_dir)
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if not os.path.exists(args.res_dir):
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os.makedirs(args.res_dir)
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# Backup python files.
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copy('run_ogb_mol.py', args.res_dir)
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copy('ogb_mol_gnn.py', args.res_dir)
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copy('utils.py', args.res_dir)
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log_file = os.path.join(args.res_dir, 'log.txt')
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# Save command line input.
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cmd_input = 'python ' + ' '.join(sys.argv) + '\n'
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with open(os.path.join(args.res_dir, 'cmd_input.txt'), 'a') as f:
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f.write(cmd_input)
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print('Command line input: ' + cmd_input + ' is saved.')
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with open(log_file, 'a') as f:
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f.write('\n' + cmd_input)
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# Rooted subgraph extraction for NGNN.
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path = 'data/'
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pre_transform = None
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if args.h is not None:
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if type(args.h) == int:
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path += '/ngnn_h' + str(args.h)
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path += '_' + args.node_label
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if args.use_rd:
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path += '_rd'
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def pre_transform(g):
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return create_subgraphs(g, args.h, node_label=args.node_label,
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use_rd=args.use_rd)
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if args.use_rp is not None:
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path += f'_rp{args.use_rp}'
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if pre_transform is None:
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pre_transform = return_prob(args.use_rp)
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else:
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pre_transform = Compose([return_prob(args.use_rp), pre_transform])
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transform = None
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if args.dataset == 'ogbg-ppa': # ppa is too slow to process currently for NGNN
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def add_zeros(data):
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data.x = torch.zeros(data.num_nodes, dtype=torch.long)
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return data
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transform = add_zeros
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dataset = PygGraphPropPredDataset(
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name=args.dataset, root=path, transform=transform, pre_transform=pre_transform,
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skip_collate=False)
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split_idx = dataset.get_idx_split()
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evaluator = Evaluator(args.dataset)
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train_loader = DataLoader(dataset[split_idx["train"]], batch_size=args.batch_size,
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shuffle=True, num_workers = args.num_workers)
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valid_loader = DataLoader(dataset[split_idx["valid"]], batch_size=args.batch_size,
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shuffle=False, num_workers = args.num_workers)
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test_loader = DataLoader(dataset[split_idx["test"]], batch_size=args.batch_size,
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shuffle=False, num_workers = args.num_workers)
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if args.pre_visualize:
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visualize(dataset, args.res_dir)
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kwargs = {
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'num_layer': args.num_layer,
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'residual': args.residual,
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'use_rd': args.use_rd,
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'use_rp': args.use_rp,
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'adj_dropout': args.adj_dropout,
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'subgraph_pooling': args.subgraph_pooling,
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'graph_pooling': args.graph_pooling,
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}
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