Source code for torch_geometric.transforms.distance

import torch

from torch_geometric.transforms import BaseTransform


[docs]class Distance(BaseTransform): r"""Saves the Euclidean distance of linked nodes in its edge attributes. Args: norm (bool, optional): If set to :obj:`False`, the output will not be normalized to the interval :math:`[0, 1]`. (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`) """ def __init__(self, norm=True, max_value=None, cat=True): self.norm = norm self.max = max_value self.cat = cat def __call__(self, data): (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: dist = dist / (dist.max() if self.max is None else self.max) 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})')