Source code for torch_geometric.transforms.polar

from math import pi as PI
from typing import Optional

import torch

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

[docs]@functional_transform('polar') class Polar(BaseTransform): r"""Saves the polar coordinates of linked nodes in its edge attributes (functional name: :obj:`polar`). Args: norm (bool, optional): If set to :obj:`False`, the output will not be normalized to the interval :math:`{[0, 1]}^2`. (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: bool = True, max_value: Optional[float] = None, cat: bool = True, ) -> None: self.norm = norm self.max = max_value = cat 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 assert pos.dim() == 2 and pos.size(1) == 2 cart = pos[col] - pos[row] rho = torch.norm(cart, p=2, dim=-1).view(-1, 1) theta = torch.atan2(cart[..., 1], cart[..., 0]).view(-1, 1) theta = theta + (theta < 0).type_as(theta) * (2 * PI) if self.norm: rho = rho / (rho.max() if self.max is None else self.max) theta = theta / (2 * PI) polar =[rho, theta], dim=-1) if pseudo is not None and pseudo = pseudo.view(-1, 1) if pseudo.dim() == 1 else pseudo data.edge_attr =[pseudo, polar.type_as(pos)], dim=-1) else: data.edge_attr = polar return data def __repr__(self) -> str: return (f'{self.__class__.__name__}(norm={self.norm}, ' f'max_value={self.max})')