Source code for torch_geometric.transforms.random_rotate

import math
import random
from typing import Tuple, Union

import torch

from torch_geometric.data import Data
from torch_geometric.data.datapipes import functional_transform
from torch_geometric.transforms import BaseTransform, LinearTransformation

[docs]@functional_transform('random_rotate')
class RandomRotate(BaseTransform):
r"""Rotates node positions around a specific axis by a randomly sampled
factor within a given interval (functional name: :obj:random_rotate).

Args:
degrees (tuple or float): Rotation interval from which the rotation
angle is sampled. If :obj:degrees is a number instead of a
tuple, the interval is given by :math:[-\mathrm{degrees},
\mathrm{degrees}].
axis (int, optional): The rotation axis. (default: :obj:0)
"""
def __init__(
self,
degrees: Union[Tuple[float, float], float],
axis: int = 0,
) -> None:
if isinstance(degrees, (int, float)):
degrees = (-abs(degrees), abs(degrees))
assert isinstance(degrees, (tuple, list)) and len(degrees) == 2
self.degrees = degrees
self.axis = axis

def forward(self, data: Data) -> Data:
assert data.pos is not None

degree = math.pi * random.uniform(*self.degrees) / 180.0
sin, cos = math.sin(degree), math.cos(degree)

if data.pos.size(-1) == 2:
matrix = [[cos, sin], [-sin, cos]]
else:
if self.axis == 0:
matrix = [[1, 0, 0], [0, cos, sin], [0, -sin, cos]]
elif self.axis == 1:
matrix = [[cos, 0, -sin], [0, 1, 0], [sin, 0, cos]]
else:
matrix = [[cos, sin, 0], [-sin, cos, 0], [0, 0, 1]]

return LinearTransformation(torch.tensor(matrix))(data)

def __repr__(self) -> str:
return (f'{self.__class__.__name__}({self.degrees}, '
f'axis={self.axis})')