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if args.gnn.startswith('gin'):
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gnn_type = 'gin'
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elif args.gnn.startswith('gcn'):
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gnn_type = 'gcn'
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num_classes = dataset.num_tasks if args.dataset.startswith('ogbg-mol') else dataset.num_classes
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valid_perfs, test_perfs = [], []
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start_run = args.run_from - 1
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runs = args.runs - args.run_from + 1
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for run in range(start_run, start_run + runs):
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if args.gnn == 'ppgn':
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model = PPGN(num_classes).to(device)
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elif args.gnn == 'gine+':
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model = ClassifierNetwork(hidden=args.emb_dim,
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out_dim=num_classes,
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layers=args.num_layer,
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dropout=args.drop_ratio,
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virtual_node=args.virtual_node,
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k=3,
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conv_type='gin+',
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nested=args.h is not None).to(device)
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torch.cuda.set_device(0)
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else:
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# the GNN class can automatically switch between GNN and NGNN depending on
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# whether the input data contain 'node_to_subgraph' and 'subgraph_to_graph'
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model = GNN(args.dataset, num_classes, gnn_type=gnn_type, emb_dim=args.emb_dim,
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drop_ratio=args.drop_ratio, virtual_node=args.virtual_node,
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RNI=args.RNI, **kwargs).to(device)
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optimizer = optim.Adam(model.parameters(), lr=args.lr)
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if args.scheduler:
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scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=20,
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gamma=args.lr_decay_factor)
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start_epoch = 1
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epochs = args.epochs
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if args.continue_from is not None:
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model.load_state_dict(
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torch.load(os.path.join(args.res_dir,
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'run{}_model_checkpoint{}.pth'.format(run+1, args.continue_from)))
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)
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optimizer.load_state_dict(
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torch.load(os.path.join(args.res_dir,
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'run{}_optimizer_checkpoint{}.pth'.format(run+1, args.continue_from)))
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)
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start_epoch = args.continue_from + 1
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epochs = epochs - args.continue_from
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if args.visualize_all: # visualize all graphs
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model.load_state_dict(torch.load(os.path.join(args.res_dir, 'best_model.pth')))
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dataset = dataset[:100]
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loader = DataLoader(dataset, batch_size=32, shuffle=False)
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all_losses = eval(model, device, loader, evaluator, True,
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dataset.task_type).flatten()
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visualize(dataset, args.res_dir, 'all_vis', loss=all_losses, sort=False)
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if args.visualize_test:
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model.load_state_dict(torch.load(os.path.join(args.res_dir, 'best_model.pth')))
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test_losses = eval(model, device, test_loader, evaluator, True,
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dataset.task_type).flatten()
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visualize(dataset[split_idx["test"]], args.res_dir, 'test_vis', loss=test_losses)
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# Training begins.
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eval_metric = dataset.eval_metric
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best_valid_perf = -1E6 if 'classification' in dataset.task_type else 1E6
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best_test_perf = None
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for epoch in range(start_epoch, start_epoch + epochs):
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print(f"=====Run {run+1}, epoch {epoch}, {args.save_appendix}")
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print('Training...')
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loss = train(model, device, train_loader, optimizer, dataset.task_type)
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print('Evaluating...')
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valid_perf = eval(model, device, valid_loader, evaluator, False,
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dataset.task_type)[eval_metric]
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if 'classification' in dataset.task_type:
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if valid_perf > best_valid_perf:
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best_valid_perf = valid_perf
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best_test_perf = eval(model, device, test_loader, evaluator, False,
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dataset.task_type)[eval_metric]
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torch.save(model.state_dict(),
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os.path.join(args.res_dir, f'run{run+1}_best_model.pth'))
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else:
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if valid_perf < best_valid_perf:
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best_valid_perf = valid_perf
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best_test_perf = eval(model, device, test_loader, evaluator, False,
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dataset.task_type)[eval_metric]
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torch.save(model.state_dict(),
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os.path.join(args.res_dir, f'run{run+1}_best_model.pth'))
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if args.scheduler:
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scheduler.step()
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res = {'Epoch': epoch, 'Loss': loss, 'Cur Val': valid_perf,
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'Best Val': best_valid_perf, 'Best Test': best_test_perf}
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print(res)
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with open(log_file, 'a') as f:
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print(res, file=f)
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if epoch % args.log_steps == 0:
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