gcn_concat.py

"""
Semi-Supervised Classification with Graph Convolutional Networks
Paper: https://arxiv.org/abs/1609.02907
Code: https://github.com/tkipf/gcn
GCN with batch processing
"""
import argparse
import numpy as np
import time
import mxnet as mx
from mxnet import gluon
import dgl
import dgl.function as fn
from dgl.data import register_data_args
from dgl.data import CoraGraphDataset, CiteseerGraphDataset, PubmedGraphDataset


class GCNLayer(gluon.Block):
    def __init__(self,
                 g,
                 out_feats,
                 activation,
                 dropout):
        super(GCNLayer, self).__init__()
        self.g = g
        self.dense = gluon.nn.Dense(out_feats, activation)
        self.dropout = dropout

    def forward(self, h):
        self.g.ndata['h'] = h * self.g.ndata['out_norm']
        self.g.update_all(fn.copy_src(src='h', out='m'),
                          fn.sum(msg='m', out='accum'))
        accum = self.g.ndata.pop('accum')
        accum = self.dense(accum * self.g.ndata['in_norm'])
        if self.dropout:
            accum = mx.nd.Dropout(accum, p=self.dropout)
        h = self.g.ndata.pop('h')
        h = mx.nd.concat(h / self.g.ndata['out_norm'], accum, dim=1)
        return h


class GCN(gluon.Block):
    def __init__(self,
                 g,
                 n_hidden,
                 n_classes,
                 n_layers,
                 activation,
                 dropout):
        super(GCN, self).__init__()
        self.inp_layer = gluon.nn.Dense(n_hidden, activation)
        self.dropout = dropout
        self.layers = gluon.nn.Sequential()
        for i in range(n_layers):
            self.layers.add(GCNLayer(g, n_hidden, activation, dropout))
        self.out_layer = gluon.nn.Dense(n_classes)


    def forward(self, features):
        emb_inp = [features, self.inp_layer(features)]
        if self.dropout:
            emb_inp[-1] = mx.nd.Dropout(emb_inp[-1], p=self.dropout)
        h = mx.nd.concat(*emb_inp, dim=1)
        for layer in self.layers:
            h = layer(h)
        h = self.out_layer(h)
        return h


def evaluate(model, features, labels, mask):
    pred = model(features).argmax(axis=1)
    accuracy = ((pred == labels) * mask).sum() / mask.sum().asscalar()
    return accuracy.asscalar()


def main(args):
    # load and preprocess dataset
    if args.dataset == 'cora':
        data = CoraGraphDataset()
    elif args.dataset == 'citeseer':
        data = CiteseerGraphDataset()
    elif args.dataset == 'pubmed':
        data = PubmedGraphDataset()
    else:
        raise ValueError('Unknown dataset: {}'.format(args.dataset))

    g = data[0]
    if args.gpu < 0:
        cuda = False
        ctx = mx.cpu(0)
    else:
        cuda = True
        ctx = mx.gpu(args.gpu)
        g = g.to(ctx)

    features = g.ndata['feat']
    labels = mx.nd.array(g.ndata['label'], dtype="float32", ctx=ctx)
    train_mask = g.ndata['train_mask']
    val_mask = g.ndata['val_mask']
    test_mask = g.ndata['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().asscalar(),
              val_mask.sum().asscalar(),
              test_mask.sum().asscalar()))

    # add self loop
    if args.self_loop:
        g = dgl.remove_self_loop(g)
        g = dgl.add_self_loop(g)
    # normalization
    in_degs = g.in_degrees().astype('float32')
    out_degs = g.out_degrees().astype('float32')
    in_norm = mx.nd.power(in_degs, -0.5)
    out_norm = mx.nd.power(out_degs, -0.5)
    if cuda:
        in_norm = in_norm.as_in_context(ctx)
        out_norm = out_norm.as_in_context(ctx)
    g.ndata['in_norm'] = mx.nd.expand_dims(in_norm, 1)
    g.ndata['out_norm'] = mx.nd.expand_dims(out_norm, 1)

    model = GCN(g,
                args.n_hidden,
                n_classes,
                args.n_layers,
                'relu',
                args.dropout,
                )
    model.initialize(ctx=ctx)
    n_train_samples = train_mask.sum().asscalar()
    loss_fcn = gluon.loss.SoftmaxCELoss()

    # use optimizer
    print(model.collect_params())
    trainer = gluon.Trainer(model.collect_params(), 'adam',
            {'learning_rate': args.lr, 'wd': args.weight_decay})

    # initialize graph
    dur = []
    for epoch in range(args.n_epochs):
        if epoch >= 3:
            t0 = time.time()
        # forward
        with mx.autograd.record():
            pred = model(features)
            loss = loss_fcn(pred, labels, mx.nd.expand_dims(train_mask, 1))
            loss = loss.sum() / n_train_samples

        loss.backward()
        trainer.step(batch_size=1)

        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.asscalar(), acc, n_edges / np.mean(dur) / 1000))

    # test set accuracy
    acc = evaluate(model, features, labels, test_mask)
    print("Test accuracy {:.2%}".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("--normalization",
            choices=['sym','left'], default=None,
            help="graph normalization types (default=None)")
    parser.add_argument("--self-loop", action='store_true',
            help="graph self-loop (default=False)")
    parser.add_argument("--weight-decay", type=float, default=5e-4,
            help="Weight for L2 loss")
    args = parser.parse_args()

    print(args)

    main(args)