fde4f581d219e0d25148ffb0f8c44d6848032b53,examples/pytorch/gcn/gcn.py,,main,#Any#,63

Before Change

    // features of each samples
    features = {}
    labels = []
    train_nodes = []
    for n in data.graph.nodes():
        features[n] = torch.FloatTensor(data.features[n, :])
        if data.train_mask[n] == 1:
            train_nodes.append(n)
            labels.append(data.labels[n])
    labels = torch.LongTensor(labels)
    in_feats = data.features.shape[1]
    n_classes = data.num_labels
    n_edges = data.graph.number_of_edges()

After Change

    features = torch.FloatTensor(data.features)
    labels = torch.LongTensor(data.labels)
    mask = torch.ByteTensor(data.train_mask)
    in_feats = features.shape[1]
    n_classes = data.num_labels
    n_edges = data.graph.number_of_edges()

    if args.gpu < 0:
        cuda = False
    else:
        cuda = True
        torch.cuda.set_device(args.gpu)
        features = features.cuda()
        labels = labels.cuda()
        mask = mask.cuda()

    // create GCN model
    g = DGLGraph(data.graph)
    model = GCN(g,

Instances