本文整理汇总了Python中torch_geometric.transforms.Compose方法的典型用法代码示例。如果您正苦于以下问题：Python transforms.Compose方法的具体用法？Python transforms.Compose怎么用？Python transforms.Compose使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类torch_geometric.transforms的用法示例。
在下文中一共展示了transforms.Compose方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_compose
# 需要导入模块: from torch_geometric import transforms [as 别名]
# 或者: from torch_geometric.transforms import Compose [as 别名]
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]] 示例2: get_planetoid_dataset
# 需要导入模块: from torch_geometric import transforms [as 别名]
# 或者: from torch_geometric.transforms import Compose [as 别名]
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 示例3: __init__
# 需要导入模块: from torch_geometric import transforms [as 别名]
# 或者: from torch_geometric.transforms import Compose [as 别名]
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) 示例4: get_dataset
# 需要导入模块: from torch_geometric import transforms [as 别名]
# 或者: from torch_geometric.transforms import Compose [as 别名]
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