# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
# pyre-unsafe
import warnings
from typing import Tuple
import torch
from pytorch3d.transforms import rotation_conversions
from ..transforms import acos_linear_extrapolation
[docs]
def so3_relative_angle(
R1: torch.Tensor,
R2: torch.Tensor,
cos_angle: bool = False,
cos_bound: float = 1e-4,
eps: float = 1e-4,
) -> torch.Tensor:
"""
Calculates the relative angle (in radians) between pairs of
rotation matrices `R1` and `R2` with `angle = acos(0.5 * (Trace(R1 R2^T)-1))`
.. note::
This corresponds to a geodesic distance on the 3D manifold of rotation
matrices.
Args:
R1: Batch of rotation matrices of shape `(minibatch, 3, 3)`.
R2: Batch of rotation matrices of shape `(minibatch, 3, 3)`.
cos_angle: If==True return cosine of the relative angle rather than
the angle itself. This can avoid the unstable calculation of `acos`.
cos_bound: Clamps the cosine of the relative rotation angle to
[-1 + cos_bound, 1 - cos_bound] to avoid non-finite outputs/gradients
of the `acos` call. Note that the non-finite outputs/gradients
are returned when the angle is requested (i.e. `cos_angle==False`)
and the rotation angle is close to 0 or π.
eps: Tolerance for the valid trace check of the relative rotation matrix
in `so3_rotation_angle`.
Returns:
Corresponding rotation angles of shape `(minibatch,)`.
If `cos_angle==True`, returns the cosine of the angles.
Raises:
ValueError if `R1` or `R2` is of incorrect shape.
ValueError if `R1` or `R2` has an unexpected trace.
"""
R12 = torch.bmm(R1, R2.permute(0, 2, 1))
return so3_rotation_angle(R12, cos_angle=cos_angle, cos_bound=cos_bound, eps=eps)
[docs]
def so3_rotation_angle(
R: torch.Tensor,
eps: float = 1e-4,
cos_angle: bool = False,
cos_bound: float = 1e-4,
) -> torch.Tensor:
"""
Calculates angles (in radians) of a batch of rotation matrices `R` with
`angle = acos(0.5 * (Trace(R)-1))`. The trace of the
input matrices is checked to be in the valid range `[-1-eps,3+eps]`.
The `eps` argument is a small constant that allows for small errors
caused by limited machine precision.
Args:
R: Batch of rotation matrices of shape `(minibatch, 3, 3)`.
eps: Tolerance for the valid trace check.
cos_angle: If==True return cosine of the rotation angles rather than
the angle itself. This can avoid the unstable
calculation of `acos`.
cos_bound: Clamps the cosine of the rotation angle to
[-1 + cos_bound, 1 - cos_bound] to avoid non-finite outputs/gradients
of the `acos` call. Note that the non-finite outputs/gradients
are returned when the angle is requested (i.e. `cos_angle==False`)
and the rotation angle is close to 0 or π.
Returns:
Corresponding rotation angles of shape `(minibatch,)`.
If `cos_angle==True`, returns the cosine of the angles.
Raises:
ValueError if `R` is of incorrect shape.
ValueError if `R` has an unexpected trace.
"""
N, dim1, dim2 = R.shape
if dim1 != 3 or dim2 != 3:
raise ValueError("Input has to be a batch of 3x3 Tensors.")
rot_trace = R[:, 0, 0] + R[:, 1, 1] + R[:, 2, 2]
if ((rot_trace < -1.0 - eps) + (rot_trace > 3.0 + eps)).any():
raise ValueError("A matrix has trace outside valid range [-1-eps,3+eps].")
# phi ... rotation angle
phi_cos = (rot_trace - 1.0) * 0.5
if cos_angle:
return phi_cos
else:
if cos_bound > 0.0:
bound = 1.0 - cos_bound
return acos_linear_extrapolation(phi_cos, (-bound, bound))
else:
return torch.acos(phi_cos)
[docs]
def so3_exp_map(log_rot: torch.Tensor, eps: float = 0.0001) -> torch.Tensor:
"""
Convert a batch of logarithmic representations of rotation matrices `log_rot`
to a batch of 3x3 rotation matrices using Rodrigues formula [1].
In the logarithmic representation, each rotation matrix is represented as
a 3-dimensional vector (`log_rot`) who's l2-norm and direction correspond
to the magnitude of the rotation angle and the axis of rotation respectively.
The conversion has a singularity around `log(R) = 0`
which is handled by clamping controlled with the `eps` argument.
Args:
log_rot: Batch of vectors of shape `(minibatch, 3)`.
eps: A float constant handling the conversion singularity.
Returns:
Batch of rotation matrices of shape `(minibatch, 3, 3)`.
Raises:
ValueError if `log_rot` is of incorrect shape.
[1] https://en.wikipedia.org/wiki/Rodrigues%27_rotation_formula
"""
return _so3_exp_map(log_rot, eps=eps)[0]
[docs]
def so3_exponential_map(log_rot: torch.Tensor, eps: float = 0.0001) -> torch.Tensor:
warnings.warn(
"""so3_exponential_map is deprecated,
Use so3_exp_map instead.
so3_exponential_map will be removed in future releases.""",
PendingDeprecationWarning,
)
return so3_exp_map(log_rot, eps)
def _so3_exp_map(
log_rot: torch.Tensor, eps: float = 0.0001
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
"""
A helper function that computes the so3 exponential map and,
apart from the rotation matrix, also returns intermediate variables
that can be re-used in other functions.
"""
_, dim = log_rot.shape
if dim != 3:
raise ValueError("Input tensor shape has to be Nx3.")
nrms = (log_rot * log_rot).sum(1)
# phis ... rotation angles
rot_angles = torch.clamp(nrms, eps).sqrt()
skews = hat(log_rot)
skews_square = torch.bmm(skews, skews)
R = rotation_conversions.axis_angle_to_matrix(log_rot)
return R, rot_angles, skews, skews_square
[docs]
def so3_log_map(
R: torch.Tensor, eps: float = 0.0001, cos_bound: float = 1e-4
) -> torch.Tensor:
"""
Convert a batch of 3x3 rotation matrices `R`
to a batch of 3-dimensional matrix logarithms of rotation matrices
The conversion has a singularity around `(R=I)`.
Args:
R: batch of rotation matrices of shape `(minibatch, 3, 3)`.
eps: (unused, for backward compatibility)
cos_bound: (unused, for backward compatibility)
Returns:
Batch of logarithms of input rotation matrices
of shape `(minibatch, 3)`.
"""
N, dim1, dim2 = R.shape
if dim1 != 3 or dim2 != 3:
raise ValueError("Input has to be a batch of 3x3 Tensors.")
return rotation_conversions.matrix_to_axis_angle(R)
def hat_inv(h: torch.Tensor) -> torch.Tensor:
"""
Compute the inverse Hat operator [1] of a batch of 3x3 matrices.
Args:
h: Batch of skew-symmetric matrices of shape `(minibatch, 3, 3)`.
Returns:
Batch of 3d vectors of shape `(minibatch, 3, 3)`.
Raises:
ValueError if `h` is of incorrect shape.
ValueError if `h` not skew-symmetric.
[1] https://en.wikipedia.org/wiki/Hat_operator
"""
N, dim1, dim2 = h.shape
if dim1 != 3 or dim2 != 3:
raise ValueError("Input has to be a batch of 3x3 Tensors.")
ss_diff = torch.abs(h + h.permute(0, 2, 1)).max()
HAT_INV_SKEW_SYMMETRIC_TOL = 1e-5
if float(ss_diff) > HAT_INV_SKEW_SYMMETRIC_TOL:
raise ValueError("One of input matrices is not skew-symmetric.")
x = h[:, 2, 1]
y = h[:, 0, 2]
z = h[:, 1, 0]
v = torch.stack((x, y, z), dim=1)
return v
def hat(v: torch.Tensor) -> torch.Tensor:
"""
Compute the Hat operator [1] of a batch of 3D vectors.
Args:
v: Batch of vectors of shape `(minibatch , 3)`.
Returns:
Batch of skew-symmetric matrices of shape
`(minibatch, 3 , 3)` where each matrix is of the form:
`[ 0 -v_z v_y ]
[ v_z 0 -v_x ]
[ -v_y v_x 0 ]`
Raises:
ValueError if `v` is of incorrect shape.
[1] https://en.wikipedia.org/wiki/Hat_operator
"""
N, dim = v.shape
if dim != 3:
raise ValueError("Input vectors have to be 3-dimensional.")
h = torch.zeros((N, 3, 3), dtype=v.dtype, device=v.device)
x, y, z = v.unbind(1)
h[:, 0, 1] = -z
h[:, 0, 2] = y
h[:, 1, 0] = z
h[:, 1, 2] = -x
h[:, 2, 0] = -y
h[:, 2, 1] = x
return h