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Transform

beignet.rotations.apply_transform 

apply_transform(input, transform)

Return affine transformed position.

Parameters:

Name Type Description Default
input Tensor

Position, must have shape (..., dimension).

 required
transform Tensor

Affine transformation matrix, must have shape (dimension, dimension).

 required

Returns:

Type Description
Tensor

Affine transformed position of shape (..., dimension).
Source code in src/beignet/rotations/_apply_transform.py 
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def apply_transform(input: Tensor, transform: Tensor) -> Tensor:
    """
    Return affine transformed position.

    Parameters
    ----------
    input : Tensor
        Position, must have shape `(..., dimension)`.

    transform : Tensor
        Affine transformation matrix, must have shape
        `(dimension, dimension)`.

    Returns
    -------
    Tensor
        Affine transformed position of shape `(..., dimension)`.
    """
    return _ApplyTransform.apply(transform, input)

beignet.rotations.invert_transform 

invert_transform(transform)

Calculates the inverse of an affine transformation matrix.

Parameters:

Name Type Description Default
transform Tensor

The affine transformation matrix to be inverted.

 required

Returns:

Type Description
Tensor

The inverse of the given affine transformation matrix.
Source code in src/beignet/rotations/_invert_transform.py 
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def invert_transform(transform: Tensor) -> Tensor:
    """
    Calculates the inverse of an affine transformation matrix.

    Parameters
    ----------
    transform : Tensor
        The affine transformation matrix to be inverted.

    Returns
    -------
    Tensor
        The inverse of the given affine transformation matrix.
    """
    if transform.ndim in {0, 1}:
        return 1.0 / transform

    if transform.ndim == 2:
        return torch.linalg.inv(transform)

    raise ValueError