from typing import Any, Optional, Tuple, Union
import torch
from torch import Tensor
from torch_sparse import SparseTensor
[docs]def dense_to_sparse(adj: Tensor) -> Tuple[Tensor, Tensor]:
r"""Converts a dense adjacency matrix to a sparse adjacency matrix defined
by edge indices and edge attributes.
Args:
adj (Tensor): The dense adjacency matrix.
:rtype: (:class:`LongTensor`, :class:`Tensor`)
Examples:
>>> # Forr a single adjacency matrix
>>> adj = torch.tensor([[3, 1],
... [2, 0]])
>>> dense_to_sparse(adj)
(tensor([[0, 0, 1],
[0, 1, 0]]),
tensor([3, 1, 2]))
>>> # For two adjacency matrixes
>>> adj = torch.tensor([[[3, 1],
... [2, 0]],
... [[0, 1],
... [0, 2]]])
>>> dense_to_sparse(adj)
(tensor([[0, 0, 1, 2, 3],
[0, 1, 0, 3, 3]]),
tensor([3, 1, 2, 1, 2]))
"""
assert adj.dim() >= 2 and adj.dim() <= 3
assert adj.size(-1) == adj.size(-2)
edge_index = adj.nonzero().t()
if edge_index.size(0) == 2:
edge_attr = adj[edge_index[0], edge_index[1]]
return edge_index, edge_attr
else:
edge_attr = adj[edge_index[0], edge_index[1], edge_index[2]]
batch = edge_index[0] * adj.size(-1)
row = batch + edge_index[1]
col = batch + edge_index[2]
return torch.stack([row, col], dim=0), edge_attr
[docs]def is_torch_sparse_tensor(src: Any) -> bool:
"""Returns :obj:`True` if the input :obj:`src` is a
:class:`torch.sparse.Tensor` (in any sparse layout).
Args:
src (Any): The input object to be checked.
"""
return isinstance(src, Tensor) and src.is_sparse
[docs]def is_sparse(src: Any) -> bool:
"""Returns :obj:`True` if the input :obj:`src` is of type
:class:`torch.sparse.Tensor` (in any sparse layout) or of type
:class:`torch_sparse.SparseTensor`.
Args:
src (Any): The input object to be checked.
"""
return is_torch_sparse_tensor(src) or isinstance(src, SparseTensor)
[docs]def to_torch_coo_tensor(
edge_index: Tensor,
edge_attr: Optional[Tensor] = None,
size: Optional[Union[int, Tuple[int, int]]] = None,
) -> Tensor:
"""Converts a sparse adjacency matrix defined by edge indices and edge
attributes to a :class:`torch.sparse.Tensor`.
Args:
edge_index (LongTensor): The edge indices.
edge_attr (Tensor, optional): The edge attributes.
(default: :obj:`None`)
size (int or (int, int), optional): The size of the sparse matrix.
If given as an integer, will create a quadratic sparse matrix.
If set to :obj:`None`, will infer a quadratic sparse matrix based
on :obj:`edge_index.max() + 1`. (default: :obj:`None`)
:rtype: :class:`torch.sparse.FloatTensor`
Example:
>>> edge_index = torch.tensor([[0, 1, 1, 2, 2, 3],
... [1, 0, 2, 1, 3, 2]])
>>> to_torch_coo_tensor(edge_index)
tensor(indices=tensor([[0, 1, 1, 2, 2, 3],
[1, 0, 2, 1, 3, 2]]),
values=tensor([1., 1., 1., 1., 1., 1.]),
size=(4, 4), nnz=6, layout=torch.sparse_coo)
"""
if size is None:
size = int(edge_index.max()) + 1
if not isinstance(size, (tuple, list)):
size = (size, size)
if edge_attr is None:
edge_attr = torch.ones(edge_index.size(1), device=edge_index.device)
size = tuple(size) + edge_attr.size()[1:]
out = torch.sparse_coo_tensor(edge_index, edge_attr, size,
device=edge_index.device)
out = out.coalesce()
return out