from typing import List, Optional, Tuple, Union
import torch
from torch import Tensor
from torch_scatter import scatter
from .num_nodes import maybe_num_nodes
@torch.jit._overload
def coalesce(edge_index, edge_attr=None, num_nodes=None, reduce="add",
is_sorted=False, sort_by_row=True):
# type: (Tensor, Optional[bool], Optional[int], str, bool, bool) -> Tensor # noqa
pass
@torch.jit._overload
def coalesce(edge_index, edge_attr=None, num_nodes=None, reduce="add",
is_sorted=False, sort_by_row=True):
# type: (Tensor, Tensor, Optional[int], str, bool, bool) -> Tuple[Tensor, Tensor] # noqa
pass
@torch.jit._overload
def coalesce(edge_index, edge_attr=None, num_nodes=None, reduce="add",
is_sorted=False, sort_by_row=True):
# type: (Tensor, List[Tensor], Optional[int], str, bool, bool) -> Tuple[Tensor, List[Tensor]] # noqa
pass
[docs]def coalesce(
edge_index: Tensor,
edge_attr: Union[Optional[Tensor], List[Tensor]] = None,
num_nodes: Optional[int] = None,
reduce: str = "add",
is_sorted: bool = False,
sort_by_row: bool = True,
) -> Union[Tensor, Tuple[Tensor, Tensor], Tuple[Tensor, List[Tensor]]]:
"""Row-wise sorts :obj:`edge_index` and removes its duplicated entries.
Duplicate entries in :obj:`edge_attr` are merged by scattering them
together according to the given :obj:`reduce` option.
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`)
reduce (string, optional): The reduce operation to use for merging edge
features (:obj:`"add"`, :obj:`"mean"`, :obj:`"min"`, :obj:`"max"`,
:obj:`"mul"`). (default: :obj:`"add"`)
is_sorted (bool, optional): If set to :obj:`True`, will expect
:obj:`edge_index` to be already sorted row-wise.
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]]`)
Example:
>>> edge_index = torch.tensor([[1, 1, 2, 3],
... [3, 3, 1, 2]])
>>> edge_attr = torch.tensor([1., 1., 1., 1.])
>>> coalesce(edge_index)
tensor([[1, 2, 3],
[3, 1, 2]])
>>> # Sort `edge_index` column-wise
>>> coalesce(edge_index, sort_by_row=False)
tensor([[2, 3, 1],
[1, 2, 3]])
>>> coalesce(edge_index, edge_attr)
(tensor([[1, 2, 3],
[3, 1, 2]]),
tensor([2., 1., 1.]))
>>> # Use 'mean' operation to merge edge features
>>> coalesce(edge_index, edge_attr, reduce='mean')
(tensor([[1, 2, 3],
[3, 1, 2]]),
tensor([1., 1., 1.]))
"""
nnz = edge_index.size(1)
num_nodes = maybe_num_nodes(edge_index, num_nodes)
idx = edge_index.new_empty(nnz + 1)
idx[0] = -1
idx[1:] = edge_index[1 - int(sort_by_row)]
idx[1:].mul_(num_nodes).add_(edge_index[int(sort_by_row)])
if not is_sorted:
idx[1:], perm = idx[1:].sort()
edge_index = edge_index[:, perm]
if isinstance(edge_attr, Tensor):
edge_attr = edge_attr[perm]
elif isinstance(edge_attr, (list, tuple)):
edge_attr = [e[perm] for e in edge_attr]
mask = idx[1:] > idx[:-1]
# Only perform expensive merging in case there exists duplicates:
if mask.all():
if isinstance(edge_attr, (Tensor, list, tuple)):
return edge_index, edge_attr
return edge_index
edge_index = edge_index[:, mask]
if edge_attr is None:
return edge_index
dim_size = edge_index.size(1)
idx = torch.arange(0, nnz, device=edge_index.device)
idx.sub_(mask.logical_not_().cumsum(dim=0))
if isinstance(edge_attr, Tensor):
edge_attr = scatter(edge_attr, idx, 0, None, dim_size, reduce)
return edge_index, edge_attr
elif isinstance(edge_attr, (list, tuple)):
edge_attr = [
scatter(e, idx, 0, None, dim_size, reduce) for e in edge_attr
]
return edge_index, edge_attr
return edge_index