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from math import pi as PI
from typing import Optional
import torch
from torch_geometric.data import Data
from torch_geometric.data.datapipes import functional_transform
from torch_geometric.transforms import BaseTransform
@functional_transform('spherical')
class Spherical(BaseTransform):
r"""Saves the spherical coordinates of linked nodes in its edge attributes
(functional name: :obj:`spherical`).
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: bool = True,
max_value: Optional[float] = None,
cat: bool = True,
):
self.norm = norm
self.max = max_value
self.cat = 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) == 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 = torch.cat([rho, theta, phi], dim=-1)
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, spher.type_as(pos)], dim=-1)
else:
data.edge_attr = spher
return data
def __repr__(self) -> str:
return (f'{self.__class__.__name__}(norm={self.norm}, '
f'max_value={self.max})')
|
