from typing import Optional, Union, Tuple
import torch
from torch import Tensor
from torch_sparse import coalesce, transpose
from .num_nodes import maybe_num_nodes
[docs]def is_undirected(edge_index: Tensor, edge_attr: Optional[Tensor] = None,
num_nodes: Optional[int] = None) -> bool:
r"""Returns :obj:`True` if the graph given by :attr:`edge_index` is
undirected.
Args:
edge_index (LongTensor): The edge indices.
edge_attr (Tensor, optional): Edge weights or multi-dimensional
edge features. (default: :obj:`None`)
num_nodes (int, optional): The number of nodes, *i.e.*
:obj:`max_val + 1` of :attr:`edge_index`. (default: :obj:`None`)
:rtype: bool
"""
num_nodes = maybe_num_nodes(edge_index, num_nodes)
edge_index, edge_attr = coalesce(edge_index, edge_attr, num_nodes,
num_nodes)
if edge_attr is None:
undirected_edge_index = to_undirected(edge_index, num_nodes=num_nodes)
return edge_index.size(1) == undirected_edge_index.size(1)
else:
edge_index_t, edge_attr_t = transpose(edge_index, edge_attr, num_nodes,
num_nodes, coalesced=True)
index_symmetric = torch.all(edge_index == edge_index_t)
attr_symmetric = torch.all(edge_attr == edge_attr_t)
return index_symmetric and attr_symmetric
[docs]def to_undirected(edge_index: Tensor, edge_attr: Optional[Tensor] = None,
num_nodes: Optional[int] = None,
reduce: str = "add") -> Union[Tensor, Tuple[Tensor, Tensor]]:
r"""Converts the graph given by :attr:`edge_index` to an undirected graph
such that :math:`(j,i) \in \mathcal{E}` for every edge :math:`(i,j) \in
\mathcal{E}`.
Args:
edge_index (LongTensor): The edge indices.
edge_attr (Tensor, optional): Edge weights or multi-dimensional
edge features. (default: :obj:`None`)
num_nodes (int, optional): The number of nodes, *i.e.*
:obj:`max_val + 1` of :attr:`edge_index`. (default: :obj:`None`)
reduce (string, optional): The reduce operation to use for merging edge
features. (default: :obj:`"add"`)
:rtype: :class:`LongTensor` if :attr:`edge_attr` is :obj:`None`, else
(:class:`LongTensor`, :class:`Tensor`)
"""
# Maintain backward compatibility to `to_undirected(edge_index, num_nodes)`
if isinstance(edge_attr, int):
edge_attr = None
num_nodes = edge_attr
num_nodes = maybe_num_nodes(edge_index, num_nodes)
row, col = edge_index
row, col = torch.cat([row, col], dim=0), torch.cat([col, row], dim=0)
edge_index = torch.stack([row, col], dim=0)
if edge_attr is not None:
edge_attr = torch.cat([edge_attr, edge_attr], dim=0)
edge_index, edge_attr = coalesce(edge_index, edge_attr, num_nodes,
num_nodes, reduce)
if edge_attr is None:
return edge_index
else:
return edge_index, edge_attr