Source code for torch_geometric.utils.sort_edge_index

from typing import List, Optional, Tuple, Union

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]]`) 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 = 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]