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from typing import Union
import torch
from torch import Tensor
from torch_geometric.data import Data, HeteroData
from torch_geometric.data.datapipes import functional_transform
from torch_geometric.transforms import BaseTransform
from torch_geometric.utils import to_undirected
@functional_transform('to_undirected')
class ToUndirected(BaseTransform):
r"""Converts a homogeneous or heterogeneous graph to an undirected graph
such that :math:`(j,i) \in \mathcal{E}` for every edge
:math:`(i,j) \in \mathcal{E}` (functional name: :obj:`to_undirected`).
In heterogeneous graphs, will add "reverse" connections for *all* existing
edge types.
Args:
reduce (str, optional): The reduce operation to use for merging edge
features (:obj:`"add"`, :obj:`"mean"`, :obj:`"min"`, :obj:`"max"`,
:obj:`"mul"`). (default: :obj:`"add"`)
merge (bool, optional): If set to :obj:`False`, will create reverse
edge types for connections pointing to the same source and target
node type.
If set to :obj:`True`, reverse edges will be merged into the
original relation.
This option only has effects in
:class:`~torch_geometric.data.HeteroData` graph data.
(default: :obj:`True`)
"""
def __init__(self, reduce: str = "add", merge: bool = True):
self.reduce = reduce
self.merge = merge
def forward(
self,
data: Union[Data, HeteroData],
) -> Union[Data, HeteroData]:
for store in data.edge_stores:
if 'edge_index' not in store:
continue
nnz = store.edge_index.size(1)
if isinstance(data, HeteroData) and (store.is_bipartite()
or not self.merge):
src, rel, dst = store._key
# Just reverse the connectivity and add edge attributes:
row, col = store.edge_index
rev_edge_index = torch.stack([col, row], dim=0)
inv_store = data[dst, f'rev_{rel}', src]
inv_store.edge_index = rev_edge_index
for key, value in store.items():
if key == 'edge_index':
continue
if isinstance(value, Tensor) and value.size(0) == nnz:
inv_store[key] = value
else:
keys, values = [], []
for key, value in store.items():
if key == 'edge_index':
continue
if store.is_edge_attr(key):
keys.append(key)
values.append(value)
store.edge_index, values = to_undirected(
store.edge_index, values, reduce=self.reduce)
for key, value in zip(keys, values):
store[key] = value
return data
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