wehub-resource-sync
166 lines
4.2 KiB
Python
166 lines
4.2 KiB
Python
import argparse
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import time
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import dgl
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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 appnp import APPNP
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from dgl.data import (
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CiteseerGraphDataset,
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CoraGraphDataset,
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PubmedGraphDataset,
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register_data_args,
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)
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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_classes
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n_edges = g.num_edges()
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print(
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"""----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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% (
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n_edges,
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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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)
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)
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n_edges = g.num_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(
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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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)
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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(
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model.parameters(), lr=args.lr, weight_decay=args.weight_decay
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)
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# initialize graph
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mean = 0
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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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mean = (mean * (epoch - 3) + (time.time() - t0)) / (epoch - 2)
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acc = evaluate(model, features, labels, val_mask)
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print(
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"Epoch {:05d} | Time(s) {:.4f} | Loss {:.4f} | Accuracy {:.4f} | "
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"ETputs(KTEPS) {:.2f}".format(
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epoch,
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mean,
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loss.item(),
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acc,
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n_edges / mean / 1000,
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)
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)
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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(
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"--in-drop", type=float, default=0.5, help="input feature dropout"
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)
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parser.add_argument(
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"--edge-drop", type=float, default=0.5, help="edge propagation dropout"
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)
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parser.add_argument("--gpu", type=int, default=-1, help="gpu")
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parser.add_argument("--lr", type=float, default=1e-2, help="learning rate")
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parser.add_argument(
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"--n-epochs", type=int, default=200, help="number of training epochs"
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)
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parser.add_argument(
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"--hidden_sizes",
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type=int,
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nargs="+",
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default=[64],
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help="hidden unit sizes for appnp",
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)
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parser.add_argument(
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"--k", type=int, default=10, help="Number of propagation steps"
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)
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parser.add_argument(
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"--alpha", type=float, default=0.1, help="Teleport Probability"
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)
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parser.add_argument(
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"--weight-decay", type=float, default=5e-4, help="Weight for L2 loss"
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)
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args = parser.parse_args()
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print(args)
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main(args)
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