Source code for torch_geometric.utils.undirected

import torch
from torch_sparse import coalesce, transpose

from .num_nodes import maybe_num_nodes

[docs]def is_undirected(edge_index, edge_attr=None, num_nodes=None): 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, num_nodes=None): r"""Converts the graph given by :attr:`edge_index` to an undirected graph, so that :math:`(j,i) \in \mathcal{E}` for every edge :math:`(i,j) \in \mathcal{E}`. Args: edge_index (LongTensor): The edge indices. num_nodes (int, optional): The number of nodes, *i.e.* :obj:`max_val + 1` of :attr:`edge_index`. (default: :obj:`None`) :rtype: :class:`LongTensor` """ num_nodes = maybe_num_nodes(edge_index, num_nodes) row, col = edge_index row, col =[row, col], dim=0),[col, row], dim=0) edge_index = torch.stack([row, col], dim=0) edge_index, _ = coalesce(edge_index, None, num_nodes, num_nodes) return edge_index