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cffa403 Dec 16, 2018
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@jermainewang @ZiyueHuang @mufeili @ylfdq1118
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"""
Semi-Supervised Classification with Graph Convolutional Networks
Paper: https://arxiv.org/abs/1609.02907
Code: https://github.com/tkipf/gcn
GCN with SPMV specialization.
"""
import argparse, time, math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from dgl import DGLGraph
from dgl.data import register_data_args, load_data
def gcn_msg(edge):
msg = edge.src['h'] * edge.src['norm']
return {'m': msg}
def gcn_reduce(node):
accum = torch.sum(node.mailbox['m'], 1) * node.data['norm']
return {'h': accum}
class NodeApplyModule(nn.Module):
def __init__(self, out_feats, activation=None, bias=True):
super(NodeApplyModule, self).__init__()
if bias:
self.bias = nn.Parameter(torch.Tensor(out_feats))
else:
self.bias = None
self.activation = activation
self.reset_parameters()
def reset_parameters(self):
if self.bias is not None:
stdv = 1. / math.sqrt(self.bias.size(0))
self.bias.data.uniform_(-stdv, stdv)
def forward(self, nodes):
h = nodes.data['h']
if self.bias is not None:
h = h + self.bias
if self.activation:
h = self.activation(h)
return {'h': h}
class GCNLayer(nn.Module):
def __init__(self,
g,
in_feats,
out_feats,
activation,
dropout,
bias=True):
super(GCNLayer, self).__init__()
self.g = g
self.weight = nn.Parameter(torch.Tensor(in_feats, out_feats))
if dropout:
self.dropout = nn.Dropout(p=dropout)
else:
self.dropout = 0.
self.node_update = NodeApplyModule(out_feats, activation, bias)
self.reset_parameters()
def reset_parameters(self):
stdv = 1. / math.sqrt(self.weight.size(1))
self.weight.data.uniform_(-stdv, stdv)
def forward(self, h):
if self.dropout:
h = self.dropout(h)
self.g.ndata['h'] = torch.mm(h, self.weight)
self.g.update_all(gcn_msg, gcn_reduce, self.node_update)
h = self.g.ndata.pop('h')
return h
class GCN(nn.Module):
def __init__(self,
g,
in_feats,
n_hidden,
n_classes,
n_layers,
activation,
dropout):
super(GCN, self).__init__()
self.layers = nn.ModuleList()
# input layer
self.layers.append(GCNLayer(g, in_feats, n_hidden, activation, dropout))
# hidden layers
for i in range(n_layers - 1):
self.layers.append(GCNLayer(g, n_hidden, n_hidden, activation, dropout))
# output layer
self.layers.append(GCNLayer(g, n_hidden, n_classes, None, dropout))
def forward(self, features):
h = features
for layer in self.layers:
h = layer(h)
return h
def evaluate(model, features, labels, mask):
model.eval()
with torch.no_grad():
logits = model(features)
logits = logits[mask]
labels = labels[mask]
_, indices = torch.max(logits, dim=1)
correct = torch.sum(indices == labels)
return correct.item() * 1.0 / len(labels)
def main(args):
# load and preprocess dataset
data = load_data(args)
features = torch.FloatTensor(data.features)
labels = torch.LongTensor(data.labels)
train_mask = torch.ByteTensor(data.train_mask)
val_mask = torch.ByteTensor(data.val_mask)
test_mask = torch.ByteTensor(data.test_mask)
in_feats = features.shape[1]
n_classes = data.num_labels
n_edges = data.graph.number_of_edges()
print("""----Data statistics------'
#Edges %d
#Classes %d
#Train samples %d
#Val samples %d
#Test samples %d""" %
(n_edges, n_classes,
train_mask.sum().item(),
val_mask.sum().item(),
test_mask.sum().item()))
if args.gpu < 0:
cuda = False
else:
cuda = True
torch.cuda.set_device(args.gpu)
features = features.cuda()
labels = labels.cuda()
train_mask = train_mask.cuda()
val_mask = val_mask.cuda()
test_mask = test_mask.cuda()
# graph preprocess and calculate normalization factor
g = DGLGraph(data.graph)
n_edges = g.number_of_edges()
# add self loop
g.add_edges(g.nodes(), g.nodes())
# normalization
degs = g.in_degrees().float()
norm = torch.pow(degs, -0.5)
norm[torch.isinf(norm)] = 0
if cuda:
norm = norm.cuda()
g.ndata['norm'] = norm.unsqueeze(1)
# create GCN model
model = GCN(g,
in_feats,
args.n_hidden,
n_classes,
args.n_layers,
F.relu,
args.dropout)
if cuda:
model.cuda()
loss_fcn = torch.nn.CrossEntropyLoss()
# use optimizer
optimizer = torch.optim.Adam(model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
# initialize graph
dur = []
for epoch in range(args.n_epochs):
model.train()
if epoch >= 3:
t0 = time.time()
# forward
logits = model(features)
loss = loss_fcn(logits[train_mask], labels[train_mask])
optimizer.zero_grad()
loss.backward()
optimizer.step()
if epoch >= 3:
dur.append(time.time() - t0)
acc = evaluate(model, features, labels, val_mask)
print("Epoch {:05d} | Time(s) {:.4f} | Loss {:.4f} | Accuracy {:.4f} | "
"ETputs(KTEPS) {:.2f}". format(epoch, np.mean(dur), loss.item(),
acc, n_edges / np.mean(dur) / 1000))
print()
acc = evaluate(model, features, labels, test_mask)
print("Test Accuracy {:.4f}".format(acc))
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='GCN')
register_data_args(parser)
parser.add_argument("--dropout", type=float, default=0.5,
help="dropout probability")
parser.add_argument("--gpu", type=int, default=-1,
help="gpu")
parser.add_argument("--lr", type=float, default=1e-2,
help="learning rate")
parser.add_argument("--n-epochs", type=int, default=200,
help="number of training epochs")
parser.add_argument("--n-hidden", type=int, default=16,
help="number of hidden gcn units")
parser.add_argument("--n-layers", type=int, default=1,
help="number of hidden gcn layers")
parser.add_argument("--weight-decay", type=float, default=5e-4,
help="Weight for L2 loss")
args = parser.parse_args()
print(args)
main(args)