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from typing import Union
import torch
from torch_geometric.data import Data
from torch_geometric.data.datapipes import functional_transform
from torch_geometric.transforms import BaseTransform, LinearTransformation
@functional_transform('random_shear')
class RandomShear(BaseTransform):
r"""Shears node positions by randomly sampled factors :math:`s` within a
given interval, *e.g.*, resulting in the transformation matrix
(functional name: :obj:`random_shear`).
.. math::
\begin{bmatrix}
1 & s_{xy} & s_{xz} \\
s_{yx} & 1 & s_{yz} \\
s_{zx} & z_{zy} & 1 \\
\end{bmatrix}
for three-dimensional positions.
Args:
shear (float or int): maximum shearing factor defining the range
:math:`(-\mathrm{shear}, +\mathrm{shear})` to sample from.
"""
def __init__(self, shear: Union[float, int]) -> None:
self.shear = abs(shear)
def forward(self, data: Data) -> Data:
assert data.pos is not None
dim = data.pos.size(-1)
matrix = data.pos.new_empty(dim, dim).uniform_(-self.shear, self.shear)
eye = torch.arange(dim, dtype=torch.long)
matrix[eye, eye] = 1
return LinearTransformation(matrix)(data)
def __repr__(self) -> str:
return f'{self.__class__.__name__}({self.shear})'
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