from typing import Optional, Union, List, Tuple
from torch import Tensor
from .num_nodes import maybe_num_nodes
[docs]def sort_edge_index(
edge_index: Tensor,
edge_attr: Optional[Union[Tensor, List[Tensor]]] = None,
num_nodes: Optional[int] = None,
sort_by_row: bool = True,
) -> Union[Tensor, Tuple[Tensor, Tensor], 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 :obj:`None`, else
(:class:`LongTensor`, :obj:`Tensor` or :obj:`List[Tensor]]`)
"""
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 = idx.argsort()
edge_index = edge_index[:, perm]
if edge_attr is None:
return edge_index
elif isinstance(edge_attr, Tensor):
return edge_index, edge_attr[perm]
else:
return edge_index, [e[perm] for e in edge_attr]