Source code for torch_geometric.transforms.distance

from typing import Optional, Tuple

import torch

from import Data
from 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 = 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 pseudo = pseudo.view(-1, 1) if pseudo.dim() == 1 else pseudo data.edge_attr =[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})')