Source code for torch_geometric.transforms.cartesian

from typing import Optional, Tuple

import torch

from import Data
from import functional_transform
from torch_geometric.transforms import BaseTransform

[docs]@functional_transform('cartesian') class Cartesian(BaseTransform): r"""Saves the relative Cartesian coordinates of linked nodes in its edge attributes (functional name: :obj:`cartesian`). Each coordinate 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: (row, col), pos, pseudo = data.edge_index, data.pos, data.edge_attr cart = pos[row] - pos[col] cart = cart.view(-1, 1) if cart.dim() == 1 else cart if self.norm and cart.numel() > 0: max_value = cart.abs().max() if self.max is None else self.max length = self.interval[1] - self.interval[0] center = (self.interval[0] + self.interval[1]) / 2 cart = length * cart / (2 * max_value) + center if pseudo is not None and pseudo = pseudo.view(-1, 1) if pseudo.dim() == 1 else pseudo data.edge_attr =[pseudo, cart.type_as(pseudo)], dim=-1) else: data.edge_attr = cart return data def __repr__(self) -> str: return (f'{self.__class__.__name__}(norm={self.norm}, ' f'max_value={self.max})')