import typing
from typing import List, Optional, Tuple, Union
import torch
from torch import Tensor
import torch_geometric.typing
from torch_geometric import EdgeIndex
from torch_geometric.edge_index import SortOrder
from torch_geometric.typing import OptTensor
from torch_geometric.utils import index_sort, lexsort
from torch_geometric.utils.num_nodes import maybe_num_nodes
if typing.TYPE_CHECKING:
from typing import overload
else:
from torch.jit import _overload as overload
MISSING = '???'
@overload
def sort_edge_index(
edge_index: Tensor,
edge_attr: str = MISSING,
num_nodes: Optional[int] = None,
sort_by_row: bool = True,
) -> Tensor:
pass
@overload
def sort_edge_index( # noqa: F811
edge_index: Tensor,
edge_attr: Tensor,
num_nodes: Optional[int] = None,
sort_by_row: bool = True,
) -> Tuple[Tensor, Tensor]:
pass
@overload
def sort_edge_index( # noqa: F811
edge_index: Tensor,
edge_attr: OptTensor,
num_nodes: Optional[int] = None,
sort_by_row: bool = True,
) -> Tuple[Tensor, OptTensor]:
pass
@overload
def sort_edge_index( # noqa: F811
edge_index: Tensor,
edge_attr: List[Tensor],
num_nodes: Optional[int] = None,
sort_by_row: bool = True,
) -> Tuple[Tensor, List[Tensor]]:
pass
[docs]def sort_edge_index( # noqa: F811
edge_index: Tensor,
edge_attr: Union[OptTensor, List[Tensor], str] = MISSING,
num_nodes: Optional[int] = None,
sort_by_row: bool = True,
) -> Union[Tensor, Tuple[Tensor, OptTensor], Tuple[Tensor, List[Tensor]]]:
"""Row-wise sorts :obj:`edge_index`.
Args:
edge_index (torch.Tensor): The edge indices.
edge_attr (torch.Tensor or List[torch.Tensor], optional): Edge weights
or multi-dimensional edge features.
If given as a list, will re-shuffle and remove duplicates for all
its entries. (default: :obj:`None`)
num_nodes (int, optional): The number of nodes, *i.e.*
:obj:`max_val + 1` of :attr:`edge_index`. (default: :obj:`None`)
sort_by_row (bool, optional): If set to :obj:`False`, will sort
:obj:`edge_index` column-wise/by destination node.
(default: :obj:`True`)
:rtype: :class:`LongTensor` if :attr:`edge_attr` is not passed, else
(:class:`LongTensor`, :obj:`Optional[Tensor]` or :obj:`List[Tensor]]`)
.. warning::
From :pyg:`PyG >= 2.3.0` onwards, this function will always return a
tuple whenever :obj:`edge_attr` is passed as an argument (even in case
it is set to :obj:`None`).
Examples:
>>> edge_index = torch.tensor([[2, 1, 1, 0],
[1, 2, 0, 1]])
>>> edge_attr = torch.tensor([[1], [2], [3], [4]])
>>> sort_edge_index(edge_index)
tensor([[0, 1, 1, 2],
[1, 0, 2, 1]])
>>> sort_edge_index(edge_index, edge_attr)
(tensor([[0, 1, 1, 2],
[1, 0, 2, 1]]),
tensor([[4],
[3],
[2],
[1]]))
"""
num_nodes = maybe_num_nodes(edge_index, num_nodes)
if num_nodes * num_nodes > torch_geometric.typing.MAX_INT64:
if not torch_geometric.typing.WITH_PT113:
raise ValueError("'sort_edge_index' will result in an overflow")
perm = lexsort(keys=[
edge_index[int(sort_by_row)],
edge_index[1 - int(sort_by_row)],
])
else:
idx = edge_index[1 - int(sort_by_row)] * num_nodes
idx += edge_index[int(sort_by_row)]
_, perm = index_sort(idx, max_value=num_nodes * num_nodes)
if isinstance(edge_index, Tensor):
is_undirected = False
if not torch.jit.is_scripting() and isinstance(edge_index, EdgeIndex):
is_undirected = edge_index.is_undirected
edge_index = edge_index[:, perm]
if not torch.jit.is_scripting() and isinstance(edge_index, EdgeIndex):
edge_index._sort_order = SortOrder('row' if sort_by_row else 'col')
edge_index._is_undirected = is_undirected
elif isinstance(edge_index, tuple):
edge_index = (edge_index[0][perm], edge_index[1][perm])
else:
raise NotImplementedError
if edge_attr is None:
return edge_index, None
if isinstance(edge_attr, Tensor):
return edge_index, edge_attr[perm]
if isinstance(edge_attr, (list, tuple)):
return edge_index, [e[perm] for e in edge_attr]
return edge_index