How to use the dgl.data.load_data function in dgl

To help you get started, we’ve selected a few dgl examples, based on popular ways it is used in public projects.

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dmlc / dgl / examples / pytorch / dgi / train.py View on Github

def main(args):
    # load and preprocess dataset
    data = load_data(args)
    features = torch.FloatTensor(data.features)
    labels = torch.LongTensor(data.labels)
    if hasattr(torch, 'BoolTensor'):
        train_mask = torch.BoolTensor(data.train_mask)
        val_mask = torch.BoolTensor(data.val_mask)
        test_mask = torch.BoolTensor(data.test_mask)
    else:
        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()

    if args.gpu &lt; 0:
        cuda = False

dmlc / dgl / examples / pytorch / cluster_gcn / utils.py View on Github

def load_data(args):
    '''Wraps the dgl's load_data utility to handle ppi special case'''
    if args.dataset != 'ppi':
        return _load_data(args)
    train_dataset = PPIDataset('train')
    val_dataset = PPIDataset('valid')
    test_dataset = PPIDataset('test')
    PPIDataType = namedtuple('PPIDataset', ['train_mask', 'test_mask',
                                            'val_mask', 'features', 'labels', 'num_labels', 'graph'])
    G = dgl.BatchedDGLGraph(
        [train_dataset.graph, val_dataset.graph, test_dataset.graph], edge_attrs=None, node_attrs=None)
    G = G.to_networkx()
    # hack to dodge the potential bugs of to_networkx
    for (n1, n2, d) in G.edges(data=True):
        d.clear()
    train_nodes_num = train_dataset.graph.number_of_nodes()
    test_nodes_num = test_dataset.graph.number_of_nodes()
    val_nodes_num = val_dataset.graph.number_of_nodes()
    nodes_num = G.number_of_nodes()
    assert(nodes_num == (train_nodes_num + test_nodes_num + val_nodes_num))

dmlc / dgl / examples / pytorch / gat / train.py View on Github

def main(args):
    # load and preprocess dataset
    data = load_data(args)
    features = torch.FloatTensor(data.features)
    labels = torch.LongTensor(data.labels)
    if hasattr(torch, 'BoolTensor'):
        train_mask = torch.BoolTensor(data.train_mask)
        val_mask = torch.BoolTensor(data.val_mask)
        test_mask = torch.BoolTensor(data.test_mask)
    else:
        train_mask = torch.ByteTensor(data.train_mask)
        val_mask = torch.ByteTensor(data.val_mask)
        test_mask = torch.ByteTensor(data.test_mask)
    num_feats = features.shape[1]
    n_classes = data.num_labels
    n_edges = data.graph.number_of_edges()
    print("""----Data statistics------'
      #Edges %d
      #Classes %d

dmlc / dgl / examples / pytorch / gcn / gcn_mp.py View on Github

def main(args):
    # load and preprocess dataset
    data = load_data(args)
    features = torch.FloatTensor(data.features)
    labels = torch.LongTensor(data.labels)
    if hasattr(torch, 'BoolTensor'):
        train_mask = torch.BoolTensor(data.train_mask)
        val_mask = torch.BoolTensor(data.val_mask)
        test_mask = torch.BoolTensor(data.test_mask)
    else:
        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

dmlc / dgl / examples / pytorch / sampling / gcn_cv_sc.py View on Github

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

    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 = np.nonzero(data.train_mask)[0].astype(np.int64)
    test_nid = np.nonzero(data.test_mask)[0].astype(np.int64)

    features = torch.FloatTensor(data.features)
    labels = torch.LongTensor(data.labels)
    if hasattr(torch, 'BoolTensor'):
        train_mask = torch.BoolTensor(data.train_mask)
        val_mask = torch.BoolTensor(data.val_mask)
        test_mask = torch.BoolTensor(data.test_mask)
    else:
        train_mask = torch.ByteTensor(data.train_mask)
        val_mask = torch.ByteTensor(data.val_mask)

dmlc / dgl / examples / pytorch / appnp / train.py View on Github

def main(args):
    # load and preprocess dataset
    data = load_data(args)
    features = torch.FloatTensor(data.features)
    labels = torch.LongTensor(data.labels)
    if hasattr(torch, 'BoolTensor'):
        train_mask = torch.BoolTensor(data.train_mask)
        val_mask = torch.BoolTensor(data.val_mask)
        test_mask = torch.BoolTensor(data.test_mask)
    else:
        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

dmlc / dgl / examples / pytorch / model_zoo / citation_network / run.py View on Github

def main(args):
    # load and preprocess dataset
    data = load_data(args)
    features = torch.FloatTensor(data.features)
    labels = torch.LongTensor(data.labels)
    if hasattr(torch, 'BoolTensor'):
        train_mask = torch.BoolTensor(data.train_mask)
        val_mask = torch.BoolTensor(data.val_mask)
        test_mask = torch.BoolTensor(data.test_mask)
    else:
        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

dmlc / dgl / examples / pytorch / gcn / train.py View on Github

def main(args):
    # load and preprocess dataset
    data = load_data(args)
    features = torch.FloatTensor(data.features)
    labels = torch.LongTensor(data.labels)
    if hasattr(torch, 'BoolTensor'):
        train_mask = torch.BoolTensor(data.train_mask)
        val_mask = torch.BoolTensor(data.val_mask)
        test_mask = torch.BoolTensor(data.test_mask)
    else:
        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

dmlc / dgl / examples / mxnet / gat / gat_batch.py View on Github

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

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

    if args.gpu &lt; 0:
        ctx = mx.cpu()
    else:
        ctx = mx.gpu(args.gpu)
        features = features.as_in_context(ctx)
        labels = labels.as_in_context(ctx)

How to use the dgl.data.load_data function in dgl

To help you get started, we’ve selected a few dgl examples, based on popular ways it is used in public projects.

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