dmlc--dgl
Zihao Ye
117 行
3.6 KiB
Python
117 行
3.6 KiB
Python
"""
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This code was modified from the GCN implementation in DGL examples.
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Simplifying Graph Convolutional Networks
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Paper: https://arxiv.org/abs/1902.07153
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Code: https://github.com/Tiiiger/SGC
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SGC implementation in DGL.
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"""
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import argparse, time, math
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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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import dgl.function as fn
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from dgl import DGLGraph
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from dgl.data import register_data_args, load_data
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from dgl.nn.pytorch.conv import SGConv
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def normalize(h):
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return (h-h.mean(0))/h.std(0)
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def evaluate(model, features, graph, labels, mask):
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model.eval()
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with torch.no_grad():
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logits = model(graph, features)[mask] # only compute the evaluation set
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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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args.dataset = "reddit-self-loop"
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data = load_data(args)
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features = torch.FloatTensor(data.features)
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labels = torch.LongTensor(data.labels)
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if hasattr(torch, 'BoolTensor'):
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train_mask = torch.BoolTensor(data.train_mask)
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val_mask = torch.BoolTensor(data.val_mask)
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test_mask = torch.BoolTensor(data.test_mask)
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else:
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train_mask = torch.ByteTensor(data.train_mask)
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val_mask = torch.ByteTensor(data.val_mask)
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test_mask = torch.ByteTensor(data.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 = data.graph.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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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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torch.cuda.set_device(args.gpu)
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features = features.cuda()
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labels = labels.cuda()
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train_mask = train_mask.cuda()
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val_mask = val_mask.cuda()
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test_mask = test_mask.cuda()
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# graph preprocess and calculate normalization factor
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g = DGLGraph(data.graph)
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n_edges = g.number_of_edges()
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# normalization
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degs = g.in_degrees().float()
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norm = torch.pow(degs, -0.5)
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norm[torch.isinf(norm)] = 0
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if cuda: norm = norm.cuda()
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g.ndata['norm'] = norm.unsqueeze(1)
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# create SGC model
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model = SGConv(in_feats, n_classes, k=2, cached=True, bias=True, norm=normalize)
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if args.gpu >= 0:
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model = model.cuda()
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# use optimizer
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optimizer = torch.optim.LBFGS(model.parameters())
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# define loss closure
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def closure():
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optimizer.zero_grad()
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output = model(g, features)[train_mask]
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loss_train = F.cross_entropy(output, labels[train_mask])
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loss_train.backward()
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return loss_train
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# initialize graph
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for epoch in range(args.n_epochs):
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model.train()
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optimizer.step(closure)
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acc = evaluate(model, features, g, 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='SGC')
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register_data_args(parser)
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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("--bias", action='store_true', default=False,
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help="flag to use bias")
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parser.add_argument("--n-epochs", type=int, default=2,
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help="number of training epochs")
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args = parser.parse_args()
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print(args)
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main(args)
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