dmlc--dgl
xnouhz
756fdd8e90
* [example] deepergcn * update * update * update * update * update Co-authored-by: Mufei Li <mufeili1996@gmail.com>
144 行
4.6 KiB
Python
144 行
4.6 KiB
Python
import argparse
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import torch
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import torch.nn as nn
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import torch.optim as optim
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import copy
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import time
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from ogb.graphproppred import DglGraphPropPredDataset, collate_dgl
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from torch.utils.data import DataLoader
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from ogb.graphproppred import Evaluator
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from models import DeeperGCN
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def train(model, device, data_loader, opt, loss_fn):
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model.train()
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train_loss = []
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for g, labels in data_loader:
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g = g.to(device)
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labels = labels.to(torch.float32).to(device)
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logits = model(g, g.edata['feat'], g.ndata['feat'])
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loss = loss_fn(logits, labels)
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train_loss.append(loss.item())
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opt.zero_grad()
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loss.backward()
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opt.step()
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return sum(train_loss) / len(train_loss)
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@torch.no_grad()
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def test(model, device, data_loader, evaluator):
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model.eval()
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y_true, y_pred = [], []
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for g, labels in data_loader:
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g = g.to(device)
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logits = model(g, g.edata['feat'], g.ndata['feat'])
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y_true.append(labels.detach().cpu())
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y_pred.append(logits.detach().cpu())
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y_true = torch.cat(y_true, dim=0).numpy()
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y_pred = torch.cat(y_pred, dim=0).numpy()
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return evaluator.eval({
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'y_true': y_true,
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'y_pred': y_pred
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})['rocauc']
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def main():
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# check cuda
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device = f'cuda:{args.gpu}' if args.gpu >= 0 and torch.cuda.is_available() else 'cpu'
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# load ogb dataset & evaluator
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dataset = DglGraphPropPredDataset(name='ogbg-molhiv')
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evaluator = Evaluator(name='ogbg-molhiv')
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g, _ = dataset[0]
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node_feat_dim = g.ndata['feat'].size()[-1]
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edge_feat_dim = g.edata['feat'].size()[-1]
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n_classes = dataset.num_tasks
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split_idx = dataset.get_idx_split()
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train_loader = DataLoader(dataset[split_idx["train"]],
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batch_size=args.batch_size,
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shuffle=True,
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collate_fn=collate_dgl)
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valid_loader = DataLoader(dataset[split_idx["valid"]],
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batch_size=args.batch_size,
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shuffle=False,
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collate_fn=collate_dgl)
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test_loader = DataLoader(dataset[split_idx["test"]],
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batch_size=args.batch_size,
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shuffle=False,
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collate_fn=collate_dgl)
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# load model
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model = DeeperGCN(node_feat_dim=node_feat_dim,
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edge_feat_dim=edge_feat_dim,
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hid_dim=args.hid_dim,
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out_dim=n_classes,
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num_layers=args.num_layers,
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dropout=args.dropout,
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learn_beta=args.learn_beta).to(device)
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print(model)
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opt = optim.Adam(model.parameters(), lr=args.lr)
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loss_fn = nn.BCEWithLogitsLoss()
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# training & validation & testing
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best_auc = 0
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best_model = copy.deepcopy(model)
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times = []
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print('---------- Training ----------')
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for i in range(args.epochs):
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t1 = time.time()
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train_loss = train(model, device, train_loader, opt, loss_fn)
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t2 = time.time()
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if i >= 5:
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times.append(t2 - t1)
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train_auc = test(model, device, train_loader, evaluator)
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valid_auc = test(model, device, valid_loader, evaluator)
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print(f'Epoch {i} | Train Loss: {train_loss:.4f} | Train Auc: {train_auc:.4f} | Valid Auc: {valid_auc:.4f}')
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if valid_auc > best_auc:
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best_auc = valid_auc
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best_model = copy.deepcopy(model)
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print('---------- Testing ----------')
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test_auc = test(best_model, device, test_loader, evaluator)
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print(f'Test Auc: {test_auc}')
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if len(times) > 0:
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print('Times/epoch: ', sum(times) / len(times))
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if __name__ == '__main__':
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"""
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DeeperGCN Hyperparameters
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"""
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parser = argparse.ArgumentParser(description='DeeperGCN')
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# training
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parser.add_argument('--gpu', type=int, default=-1, help='GPU index, -1 for CPU.')
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parser.add_argument('--epochs', type=int, default=300, help='Number of epochs to train.')
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parser.add_argument('--lr', type=float, default=0.01, help='Learning rate.')
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parser.add_argument('--dropout', type=float, default=0.2, help='Dropout rate.')
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parser.add_argument('--batch-size', type=int, default=2048, help='Batch size.')
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# model
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parser.add_argument('--num-layers', type=int, default=7, help='Number of GNN layers.')
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parser.add_argument('--hid-dim', type=int, default=256, help='Hidden channel size.')
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# learnable parameters in aggr
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parser.add_argument('--learn-beta', action='store_true')
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args = parser.parse_args()
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print(args)
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main()
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