dmlc--dgl
Chang Liu
137 行
4.2 KiB
Python
137 行
4.2 KiB
Python
import argparse, time
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from dgl.data import register_data_args
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from dgl.data import CoraGraphDataset, CiteseerGraphDataset, PubmedGraphDataset
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import dgl
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from appnp import APPNP
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def evaluate(model, features, labels, mask):
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model.eval()
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with torch.no_grad():
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logits = model(features)
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logits = logits[mask]
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labels = labels[mask]
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_, indices = torch.max(logits, dim=1)
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correct = torch.sum(indices == labels)
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return correct.item() * 1.0 / len(labels)
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def main(args):
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# load and preprocess dataset
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if args.dataset == 'cora':
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data = CoraGraphDataset()
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elif args.dataset == 'citeseer':
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data = CiteseerGraphDataset()
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elif args.dataset == 'pubmed':
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data = PubmedGraphDataset()
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else:
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raise ValueError('Unknown dataset: {}'.format(args.dataset))
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g = data[0]
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if args.gpu < 0:
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cuda = False
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else:
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cuda = True
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g = g.to(args.gpu)
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features = g.ndata['feat']
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labels = g.ndata['label']
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train_mask = g.ndata['train_mask']
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val_mask = g.ndata['val_mask']
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test_mask = g.ndata['test_mask']
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in_feats = features.shape[1]
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n_classes = data.num_labels
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n_edges = g.number_of_edges()
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print("""----Data statistics------'
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#Edges %d
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#Classes %d
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#Train samples %d
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#Val samples %d
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#Test samples %d""" %
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(n_edges, n_classes,
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train_mask.int().sum().item(),
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val_mask.int().sum().item(),
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test_mask.int().sum().item()))
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n_edges = g.number_of_edges()
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# add self loop
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g = dgl.remove_self_loop(g)
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g = dgl.add_self_loop(g)
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# create APPNP model
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model = APPNP(g,
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in_feats,
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args.hidden_sizes,
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n_classes,
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F.relu,
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args.in_drop,
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args.edge_drop,
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args.alpha,
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args.k)
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if cuda:
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model.cuda()
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loss_fcn = torch.nn.CrossEntropyLoss()
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# use optimizer
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optimizer = torch.optim.Adam(model.parameters(),
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lr=args.lr,
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weight_decay=args.weight_decay)
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# initialize graph
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dur = []
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for epoch in range(args.n_epochs):
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model.train()
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if epoch >= 3:
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t0 = time.time()
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# forward
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logits = model(features)
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loss = loss_fcn(logits[train_mask], labels[train_mask])
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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if epoch >= 3:
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dur.append(time.time() - t0)
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acc = evaluate(model, features, labels, val_mask)
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print("Epoch {:05d} | Time(s) {:.4f} | Loss {:.4f} | Accuracy {:.4f} | "
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"ETputs(KTEPS) {:.2f}".format(epoch, np.mean(dur), loss.item(),
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acc, n_edges / np.mean(dur) / 1000))
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print()
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acc = evaluate(model, features, labels, test_mask)
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print("Test Accuracy {:.4f}".format(acc))
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='APPNP')
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register_data_args(parser)
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parser.add_argument("--in-drop", type=float, default=0.5,
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help="input feature dropout")
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parser.add_argument("--edge-drop", type=float, default=0.5,
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help="edge propagation dropout")
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parser.add_argument("--gpu", type=int, default=-1,
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help="gpu")
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parser.add_argument("--lr", type=float, default=1e-2,
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help="learning rate")
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parser.add_argument("--n-epochs", type=int, default=200,
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help="number of training epochs")
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parser.add_argument("--hidden_sizes", type=int, nargs='+', default=[64],
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help="hidden unit sizes for appnp")
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parser.add_argument("--k", type=int, default=10,
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help="Number of propagation steps")
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parser.add_argument("--alpha", type=float, default=0.1,
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help="Teleport Probability")
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parser.add_argument("--weight-decay", type=float, default=5e-4,
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help="Weight for L2 loss")
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args = parser.parse_args()
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print(args)
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main(args)
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