Python torch_geometric.transforms.Compose() Examples

The following are 5 code examples of torch_geometric.transforms.Compose(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module torch_geometric.transforms , or try the search function .
Example #1
Source File: test_compose.py    From pytorch_geometric with MIT License 6 votes vote down vote up 
def test_compose():
    transform = T.Compose([T.Center(), T.AddSelfLoops()])
    assert transform.__repr__() == ('Compose([\n'
                                    '    Center(),\n'
                                    '    AddSelfLoops(),\n'
                                    '])')

    pos = torch.Tensor([[0, 0], [2, 0], [4, 0]])
    edge_index = torch.tensor([[0, 1, 1, 2], [1, 0, 2, 1]])

    data = Data(edge_index=edge_index, pos=pos)
    data = transform(data)
    assert len(data) == 2
    assert data.pos.tolist() == [[-2, 0], [0, 0], [2, 0]]
    assert data.edge_index.tolist() == [[0, 0, 1, 1, 1, 2, 2],
                                        [0, 1, 0, 1, 2, 1, 2]]
Example #2
Source File: datasets.py    From pytorch_geometric with MIT License 5 votes vote down vote up 
def get_planetoid_dataset(name, normalize_features=False, transform=None):
    path = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data', name)
    dataset = Planetoid(path, name)

    if transform is not None and normalize_features:
        dataset.transform = T.Compose([T.NormalizeFeatures(), transform])
    elif normalize_features:
        dataset.transform = T.NormalizeFeatures()
    elif transform is not None:
        dataset.transform = transform

    return dataset
Example #3
Source File: pyg.py    From cogdl with MIT License 5 votes vote down vote up 
def __init__(self):
        dataset = "QM9"
        path = osp.join(osp.dirname(osp.realpath(__file__)), "../..", "data", dataset)

        target=0
        class MyTransform(object):
            def __call__(self, data):
                # Specify target.
                data.y = data.y[:, target]
                return data

        class Complete(object):
            def __call__(self, data):
                device = data.edge_index.device
                row = torch.arange(data.num_nodes, dtype=torch.long, device=device)
                col = torch.arange(data.num_nodes, dtype=torch.long, device=device)
                row = row.view(-1, 1).repeat(1, data.num_nodes).view(-1)
                col = col.repeat(data.num_nodes)
                edge_index = torch.stack([row, col], dim=0)
                edge_attr = None
                if data.edge_attr is not None:
                    idx = data.edge_index[0] * data.num_nodes + data.edge_index[1]
                    size = list(data.edge_attr.size())
                    size[0] = data.num_nodes * data.num_nodes
                    edge_attr = data.edge_attr.new_zeros(size)
                    edge_attr[idx] = data.edge_attr
                edge_index, edge_attr = remove_self_loops(edge_index, edge_attr)
                data.edge_attr = edge_attr
                data.edge_index = edge_index
                return data

        transform = T.Compose([MyTransform(), Complete(), T.Distance(norm=False)])
        if not osp.exists(path):
            QM9(path)
        super(QM9Dataset, self).__init__(path)
Example #4
Source File: __init__.py    From GraphNAS with Apache License 2.0 5 votes vote down vote up 
def get_planetoid_dataset(name, normalize_features=False, transform=None):
    path = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data', name)
    dataset = Planetoid(path, name)

    if transform is not None and normalize_features:
        dataset.transform = T.Compose([T.NormalizeFeatures(), transform])
    elif normalize_features:
        dataset.transform = T.NormalizeFeatures()
    elif transform is not None:
        dataset.transform = transform

    return dataset
Example #5
Source File: datasets.py    From pytorch_geometric with MIT License 4 votes vote down vote up 
def get_dataset(name, sparse=True, cleaned=False):
    path = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data', name)
    dataset = TUDataset(path, name, cleaned=cleaned)
    dataset.data.edge_attr = None

    if dataset.data.x is None:
        max_degree = 0
        degs = []
        for data in dataset:
            degs += [degree(data.edge_index[0], dtype=torch.long)]
            max_degree = max(max_degree, degs[-1].max().item())

        if max_degree < 1000:
            dataset.transform = T.OneHotDegree(max_degree)
        else:
            deg = torch.cat(degs, dim=0).to(torch.float)
            mean, std = deg.mean().item(), deg.std().item()
            dataset.transform = NormalizedDegree(mean, std)

    if not sparse:
        num_nodes = max_num_nodes = 0
        for data in dataset:
            num_nodes += data.num_nodes
            max_num_nodes = max(data.num_nodes, max_num_nodes)

        # Filter out a few really large graphs in order to apply DiffPool.
        if name == 'REDDIT-BINARY':
            num_nodes = min(int(num_nodes / len(dataset) * 1.5), max_num_nodes)
        else:
            num_nodes = min(int(num_nodes / len(dataset) * 5), max_num_nodes)

        indices = []
        for i, data in enumerate(dataset):
            if data.num_nodes <= num_nodes:
                indices.append(i)
        dataset = dataset[torch.tensor(indices)]

        if dataset.transform is None:
            dataset.transform = T.ToDense(num_nodes)
        else:
            dataset.transform = T.Compose(
                [dataset.transform, T.ToDense(num_nodes)])

    return dataset