from typing import Optional, Tuple
import torch
from torch_geometric.data import Data
from torch_geometric.data.datapipes import functional_transform
from torch_geometric.transforms import BaseTransform
[docs]@functional_transform('distance')
class Distance(BaseTransform):
r"""Saves the Euclidean distance of linked nodes in its edge attributes
(functional name: :obj:`distance`). Each distance gets globally normalized
to a specified interval (:math:`[0, 1]` by default).
Args:
norm (bool, optional): If set to :obj:`False`, the output will not be
normalized. (default: :obj:`True`)
max_value (float, optional): If set and :obj:`norm=True`, normalization
will be performed based on this value instead of the maximum value
found in the data. (default: :obj:`None`)
cat (bool, optional): If set to :obj:`False`, all existing edge
attributes will be replaced. (default: :obj:`True`)
interval ((float, float), optional): A tuple specifying the lower and
upper bound for normalization. (default: :obj:`(0.0, 1.0)`)
"""
def __init__(
self,
norm: bool = True,
max_value: Optional[float] = None,
cat: bool = True,
interval: Tuple[float, float] = (0.0, 1.0),
):
self.norm = norm
self.max = max_value
self.cat = cat
self.interval = interval
def forward(self, data: Data) -> Data:
assert data.pos is not None
assert data.edge_index is not None
(row, col), pos, pseudo = data.edge_index, data.pos, data.edge_attr
dist = torch.norm(pos[col] - pos[row], p=2, dim=-1).view(-1, 1)
if self.norm and dist.numel() > 0:
max_val = float(dist.max()) if self.max is None else self.max
length = self.interval[1] - self.interval[0]
dist = length * (dist / max_val) + self.interval[0]
if pseudo is not None and self.cat:
pseudo = pseudo.view(-1, 1) if pseudo.dim() == 1 else pseudo
data.edge_attr = torch.cat([pseudo, dist.type_as(pseudo)], dim=-1)
else:
data.edge_attr = dist
return data
def __repr__(self) -> str:
return (f'{self.__class__.__name__}(norm={self.norm}, '
f'max_value={self.max})')