gcn_cv_sc.py

import argparse, time, math
import numpy as np
import mxnet as mx
from mxnet import gluon
import dgl
import dgl.function as fn
from dgl import DGLGraph
from dgl.data import register_data_args, load_data


class NodeUpdate(gluon.Block):
    def __init__(self, layer_id, in_feats, out_feats, dropout, activation=None, test=False, concat=False):
        super(NodeUpdate, self).__init__()
        self.layer_id = layer_id
        self.dropout = dropout
        self.test = test
        self.concat = concat
        with self.name_scope():
            self.dense = gluon.nn.Dense(out_feats, in_units=in_feats)
            self.activation = activation

    def forward(self, node):
        h = node.data['h']
        if self.test:
            norm = node.data['norm']
            h = h * norm
        else:
            agg_history_str = 'agg_h_{}'.format(self.layer_id-1)
            agg_history = node.data[agg_history_str]
            # control variate
            h = h + agg_history
            if self.dropout:
                h = mx.nd.Dropout(h, p=self.dropout)
        h = self.dense(h)
        if self.concat:
            h = mx.nd.concat(h, self.activation(h))
        elif self.activation:
            h = self.activation(h)
        return {'activation': h}



class GCNSampling(gluon.Block):
    def __init__(self,
                 in_feats,
                 n_hidden,
                 n_classes,
                 n_layers,
                 activation,
                 dropout,
                 **kwargs):
        super(GCNSampling, self).__init__(**kwargs)
        self.dropout = dropout
        self.n_layers = n_layers
        with self.name_scope():
            self.layers = gluon.nn.Sequential()
            # input layer
            self.dense = gluon.nn.Dense(n_hidden, in_units=in_feats)
            self.activation = activation
            # hidden layers
            for i in range(1, n_layers):
                skip_start = (i == self.n_layers-1)
                self.layers.add(NodeUpdate(i, n_hidden, n_hidden, dropout, activation, concat=skip_start))
            # output layer
            self.layers.add(NodeUpdate(n_layers, 2*n_hidden, n_classes, dropout))

    def forward(self, nf):
        h = nf.layers[0].data['preprocess']
        if self.dropout:
            h = mx.nd.Dropout(h, p=self.dropout)
        h = self.dense(h)

        skip_start = (0 == self.n_layers-1)
        if skip_start:
            h = mx.nd.concat(h, self.activation(h))
        else:
            h = self.activation(h)

        for i, layer in enumerate(self.layers):
            new_history = h.copy().detach()
            history_str = 'h_{}'.format(i)
            history = nf.layers[i].data[history_str]
            h = h - history

            nf.layers[i].data['h'] = h
            nf.block_compute(i,
                             fn.copy_src(src='h', out='m'),
                             lambda node : {'h': node.mailbox['m'].mean(axis=1)},
                             layer)
            h = nf.layers[i+1].data.pop('activation')
            # update history
            if i < nf.num_layers-1:
                nf.layers[i].data[history_str] = new_history

        return h


class GCNInfer(gluon.Block):
    def __init__(self,
                 in_feats,
                 n_hidden,
                 n_classes,
                 n_layers,
                 activation,
                 **kwargs):
        super(GCNInfer, self).__init__(**kwargs)
        self.n_layers = n_layers
        with self.name_scope():
            self.layers = gluon.nn.Sequential()
            # input layer
            self.dense = gluon.nn.Dense(n_hidden, in_units=in_feats)
            self.activation = activation
            # hidden layers
            for i in range(1, n_layers):
                skip_start = (i == self.n_layers-1)
                self.layers.add(NodeUpdate(i, n_hidden, n_hidden, 0, activation, True, concat=skip_start))
            # output layer
            self.layers.add(NodeUpdate(n_layers, 2*n_hidden, n_classes, 0, None, True))


    def forward(self, nf):
        h = nf.layers[0].data['preprocess']
        h = self.dense(h)

        skip_start = (0 == self.n_layers-1)
        if skip_start:
            h = mx.nd.concat(h, self.activation(h))
        else:
            h = self.activation(h)

        for i, layer in enumerate(self.layers):
            nf.layers[i].data['h'] = h
            nf.block_compute(i,
                             fn.copy_src(src='h', out='m'),
                             fn.sum(msg='m', out='h'),
                             layer)
            h = nf.layers[i+1].data.pop('activation')

        return h


def main(args):
    # load and preprocess dataset
    data = load_data(args)

    if args.gpu >= 0:
        ctx = mx.gpu(args.gpu)
    else:
        ctx = mx.cpu()

    if args.self_loop and not args.dataset.startswith('reddit'):
        data.graph.add_edges_from([(i,i) for i in range(len(data.graph))])

    train_nid = mx.nd.array(np.nonzero(data.train_mask)[0]).astype(np.int64)
    test_nid = mx.nd.array(np.nonzero(data.test_mask)[0]).astype(np.int64)

    num_neighbors = args.num_neighbors
    n_layers = args.n_layers

    features = mx.nd.array(data.features).as_in_context(ctx)
    labels = mx.nd.array(data.labels).as_in_context(ctx)
    train_mask = mx.nd.array(data.train_mask).as_in_context(ctx)
    val_mask = mx.nd.array(data.val_mask).as_in_context(ctx)
    test_mask = mx.nd.array(data.test_mask).as_in_context(ctx)
    in_feats = features.shape[1]
    n_classes = data.num_labels
    n_edges = data.graph.number_of_edges()

    n_train_samples = train_mask.sum().asscalar()
    n_test_samples = test_mask.sum().asscalar()
    n_val_samples = val_mask.sum().asscalar()

    print("""----Data statistics------'
      #Edges %d
      #Classes %d
      #Train samples %d
      #Val samples %d
      #Test samples %d""" %
          (n_edges, n_classes,
              n_train_samples,
              n_val_samples,
              n_test_samples))

    # create GCN model
    g = DGLGraph(data.graph, readonly=True)

    g.ndata['features'] = features

    norm = mx.nd.expand_dims(1./g.in_degrees().astype('float32'), 1)
    g.ndata['norm'] = norm.as_in_context(ctx)

    g.update_all(fn.copy_src(src='features', out='m'),
                 fn.sum(msg='m', out='preprocess'),
                 lambda node : {'preprocess': node.data['preprocess'] * node.data['norm']})

    for i in range(n_layers):
        g.ndata['h_{}'.format(i)] = mx.nd.zeros((features.shape[0], args.n_hidden), ctx=ctx)

    g.ndata['h_{}'.format(n_layers-1)] = mx.nd.zeros((features.shape[0], 2*args.n_hidden), ctx=ctx)

    model = GCNSampling(in_feats,
                        args.n_hidden,
                        n_classes,
                        n_layers,
                        mx.nd.relu,
                        args.dropout,
                        prefix='GCN')

    model.initialize(ctx=ctx)

    loss_fcn = gluon.loss.SoftmaxCELoss()

    infer_model = GCNInfer(in_feats,
                           args.n_hidden,
                           n_classes,
                           n_layers,
                           mx.nd.relu,
                           prefix='GCN')

    infer_model.initialize(ctx=ctx)

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

    # initialize graph
    dur = []
    for epoch in range(args.n_epochs):
        for nf in dgl.contrib.sampling.NeighborSampler(g, args.batch_size,
                                                       num_neighbors,
                                                       neighbor_type='in',
                                                       shuffle=True,
                                                       num_hops=n_layers,
                                                       seed_nodes=train_nid):
            for i in range(n_layers):
                agg_history_str = 'agg_h_{}'.format(i)
                g.pull(nf.layer_parent_nid(i+1), fn.copy_src(src='h_{}'.format(i), out='m'),
                       fn.sum(msg='m', out=agg_history_str),
                       lambda node : {agg_history_str: node.data[agg_history_str] * node.data['norm']})

            node_embed_names = [['preprocess', 'h_0']]
            for i in range(1, n_layers):
                node_embed_names.append(['h_{}'.format(i), 'agg_h_{}'.format(i-1)])
            node_embed_names.append(['agg_h_{}'.format(n_layers-1)])

            nf.copy_from_parent(node_embed_names=node_embed_names)
            # forward
            with mx.autograd.record():
                pred = model(nf)
                batch_nids = nf.layer_parent_nid(-1).as_in_context(ctx)
                batch_labels = labels[batch_nids]
                loss = loss_fcn(pred, batch_labels)
                loss = loss.sum() / len(batch_nids)

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

            node_embed_names = [['h_{}'.format(i)] for i in range(n_layers)]
            node_embed_names.append([])

            nf.copy_to_parent(node_embed_names=node_embed_names)

        infer_params = infer_model.collect_params()

        for key in infer_params:
            idx = trainer._param2idx[key]
            trainer._kvstore.pull(idx, out=infer_params[key].data())

        num_acc = 0.

        for nf in dgl.contrib.sampling.NeighborSampler(g, args.test_batch_size,
                                                       g.number_of_nodes(),
                                                       neighbor_type='in',
                                                       num_hops=n_layers,
                                                       seed_nodes=test_nid):
            node_embed_names = [['preprocess']]
            for i in range(n_layers):
                node_embed_names.append(['norm'])

            nf.copy_from_parent(node_embed_names=node_embed_names)
            pred = infer_model(nf)
            batch_nids = nf.layer_parent_nid(-1).as_in_context(ctx)
            batch_labels = labels[batch_nids]
            num_acc += (pred.argmax(axis=1) == batch_labels).sum().asscalar()

        print("Test Accuracy {:.4f}". format(num_acc/n_test_samples))


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=3e-2,
            help="learning rate")
    parser.add_argument("--n-epochs", type=int, default=200,
            help="number of training epochs")
    parser.add_argument("--batch-size", type=int, default=1000,
            help="train batch size")
    parser.add_argument("--test-batch-size", type=int, default=1000,
            help="test batch size")
    parser.add_argument("--num-neighbors", type=int, default=2,
            help="number of neighbors to be sampled")
    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("--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)