from typing import List, Optional, Tuple, Union
import torch
from torch import Tensor
from torch_geometric.typing import OptTensor
from torch_geometric.utils import index_sort
from torch_geometric.utils.num_nodes import maybe_num_nodes
MISSING = '???'
@torch.jit._overload
def sort_edge_index(edge_index, edge_attr, num_nodes, sort_by_row):
# type: (Tensor, str, Optional[int], bool) -> Tensor # noqa
pass
@torch.jit._overload
def sort_edge_index(edge_index, edge_attr, num_nodes, sort_by_row):
# type: (Tensor, Optional[Tensor], Optional[int], bool) -> Tuple[Tensor, Optional[Tensor]] # noqa
pass
@torch.jit._overload
def sort_edge_index(edge_index, edge_attr, num_nodes, sort_by_row):
# type: (Tensor, List[Tensor], Optional[int], bool) -> Tuple[Tensor, List[Tensor]] # noqa
pass
[docs]def sort_edge_index(
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 (LongTensor): The edge indices.
edge_attr (Tensor or List[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.
: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)
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)
edge_index = edge_index[:, perm]
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