Source code for torch_geometric.transforms.spherical

from math import pi as PI

import torch

[docs]class Spherical(object): r"""Saves the spherical coordinates 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]}^3`. (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 = cat def __call__(self, data): (row, col), pos, pseudo = data.edge_index, data.pos, data.edge_attr assert pos.dim() == 2 and pos.size(1) == 3 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) phi = torch.acos(cart[..., 2] / rho.view(-1)).view(-1, 1) if self.norm: rho = rho / (rho.max() if self.max is None else self.max) theta = theta / (2 * PI) phi = phi / PI spher =[rho, theta, phi], dim=-1) if pseudo is not None and pseudo = pseudo.view(-1, 1) if pseudo.dim() == 1 else pseudo data.edge_attr =[pseudo, spher.type_as(pos)], dim=-1) else: data.edge_attr = spher return data def __repr__(self): return '{}(norm={}, max_value={})'.format(self.__class__.__name__, self.norm, self.max)