Make some matrix conversion jittable (#898)

una-dinosauria · facebook-github-bot · commit bee31c48d3d3 · 2021-10-26T14:31:46.000-07:00
Summary: Make sure the functions from `rotation_conversion` are jittable, and add some type hints. Add tests to verify this is the case. Pull Request resolved: #898 Reviewed By: patricklabatut Differential Revision: D31926103 Pulled By: bottler fbshipit-source-id: bff6013c5ca2d452e37e631bd902f0674d5ca091
diff --git a/pytorch3d/transforms/rotation_conversions.py b/pytorch3d/transforms/rotation_conversions.py
@@ -4,7 +4,6 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
-import functools
 from typing import Optional
 
 import torch
@@ -39,7 +38,7 @@
 """
 
 
-def quaternion_to_matrix(quaternions):
+def quaternion_to_matrix(quaternions: torch.Tensor) -> torch.Tensor:
     """
     Convert rotations given as quaternions to rotation matrices.
 
@@ -70,7 +69,7 @@ def quaternion_to_matrix(quaternions):
     return o.reshape(quaternions.shape[:-1] + (3, 3))
 
 
-def _copysign(a, b):
+def _copysign(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
     """
     Return a tensor where each element has the absolute value taken from the,
     corresponding element of a, with sign taken from the corresponding
@@ -114,7 +113,7 @@ def matrix_to_quaternion(matrix: torch.Tensor) -> torch.Tensor:
 
     batch_dim = matrix.shape[:-2]
     m00, m01, m02, m10, m11, m12, m20, m21, m22 = torch.unbind(
-        matrix.reshape(*batch_dim, 9), dim=-1
+        matrix.reshape(batch_dim + (9,)), dim=-1
     )
 
     q_abs = _sqrt_positive_part(
@@ -142,17 +141,18 @@ def matrix_to_quaternion(matrix: torch.Tensor) -> torch.Tensor:
 
     # We floor here at 0.1 but the exact level is not important; if q_abs is small,
     # the candidate won't be picked.
-    quat_candidates = quat_by_rijk / (2.0 * q_abs[..., None].max(q_abs.new_tensor(0.1)))
+    flr = torch.tensor(0.1).to(dtype=q_abs.dtype, device=q_abs.device)
+    quat_candidates = quat_by_rijk / (2.0 * q_abs[..., None].max(flr))
 
     # if not for numerical problems, quat_candidates[i] should be same (up to a sign),
     # forall i; we pick the best-conditioned one (with the largest denominator)
 
     return quat_candidates[
         F.one_hot(q_abs.argmax(dim=-1), num_classes=4) > 0.5, :  # pyre-ignore[16]
-    ].reshape(*batch_dim, 4)
+    ].reshape(batch_dim + (4,))
 
 
-def _axis_angle_rotation(axis: str, angle):
+def _axis_angle_rotation(axis: str, angle: torch.Tensor) -> torch.Tensor:
     """
     Return the rotation matrices for one of the rotations about an axis
     of which Euler angles describe, for each value of the angle given.
@@ -172,15 +172,17 @@ def _axis_angle_rotation(axis: str, angle):
 
     if axis == "X":
         R_flat = (one, zero, zero, zero, cos, -sin, zero, sin, cos)
-    if axis == "Y":
+    elif axis == "Y":
         R_flat = (cos, zero, sin, zero, one, zero, -sin, zero, cos)
-    if axis == "Z":
+    elif axis == "Z":
         R_flat = (cos, -sin, zero, sin, cos, zero, zero, zero, one)
+    else:
+        raise ValueError("letter must be either X, Y or Z.")
 
     return torch.stack(R_flat, -1).reshape(angle.shape + (3, 3))
 
 
-def euler_angles_to_matrix(euler_angles, convention: str):
+def euler_angles_to_matrix(euler_angles: torch.Tensor, convention: str) -> torch.Tensor:
     """
     Convert rotations given as Euler angles in radians to rotation matrices.
 
@@ -201,13 +203,17 @@ def euler_angles_to_matrix(euler_angles, convention: str):
     for letter in convention:
         if letter not in ("X", "Y", "Z"):
             raise ValueError(f"Invalid letter {letter} in convention string.")
-    matrices = map(_axis_angle_rotation, convention, torch.unbind(euler_angles, -1))
-    return functools.reduce(torch.matmul, matrices)
+    matrices = [
+        _axis_angle_rotation(c, e)
+        for c, e in zip(convention, torch.unbind(euler_angles, -1))
+    ]
+    # return functools.reduce(torch.matmul, matrices)
+    return torch.matmul(torch.matmul(matrices[0], matrices[1]), matrices[2])
 
 
 def _angle_from_tan(
     axis: str, other_axis: str, data, horizontal: bool, tait_bryan: bool
-):
+) -> torch.Tensor:
     """
     Extract the first or third Euler angle from the two members of
     the matrix which are positive constant times its sine and cosine.
@@ -238,16 +244,17 @@ def _angle_from_tan(
     return torch.atan2(data[..., i2], -data[..., i1])
 
 
-def _index_from_letter(letter: str):
+def _index_from_letter(letter: str) -> int:
     if letter == "X":
         return 0
     if letter == "Y":
         return 1
     if letter == "Z":
         return 2
+    raise ValueError("letter must be either X, Y or Z.")
 
 
-def matrix_to_euler_angles(matrix, convention: str):
+def matrix_to_euler_angles(matrix: torch.Tensor, convention: str) -> torch.Tensor:
     """
     Convert rotations given as rotation matrices to Euler angles in radians.
 
@@ -291,7 +298,7 @@ def matrix_to_euler_angles(matrix, convention: str):
 
 def random_quaternions(
     n: int, dtype: Optional[torch.dtype] = None, device: Optional[Device] = None
-):
+) -> torch.Tensor:
     """
     Generate random quaternions representing rotations,
     i.e. versors with nonnegative real part.
@@ -305,6 +312,8 @@ def random_quaternions(
     Returns:
         Quaternions as tensor of shape (N, 4).
     """
+    if isinstance(device, str):
+        device = torch.device(device)
     o = torch.randn((n, 4), dtype=dtype, device=device)
     s = (o * o).sum(1)
     o = o / _copysign(torch.sqrt(s), o[:, 0])[:, None]
@@ -313,7 +322,7 @@ def random_quaternions(
 
 def random_rotations(
     n: int, dtype: Optional[torch.dtype] = None, device: Optional[Device] = None
-):
+) -> torch.Tensor:
     """
     Generate random rotations as 3x3 rotation matrices.
 
@@ -332,7 +341,7 @@ def random_rotations(
 
 def random_rotation(
     dtype: Optional[torch.dtype] = None, device: Optional[Device] = None
-):
+) -> torch.Tensor:
     """
     Generate a single random 3x3 rotation matrix.
 
@@ -347,7 +356,7 @@ def random_rotation(
     return random_rotations(1, dtype, device)[0]
 
 
-def standardize_quaternion(quaternions):
+def standardize_quaternion(quaternions: torch.Tensor) -> torch.Tensor:
     """
     Convert a unit quaternion to a standard form: one in which the real
     part is non negative.
@@ -362,7 +371,7 @@ def standardize_quaternion(quaternions):
     return torch.where(quaternions[..., 0:1] < 0, -quaternions, quaternions)
 
 
-def quaternion_raw_multiply(a, b):
+def quaternion_raw_multiply(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
     """
     Multiply two quaternions.
     Usual torch rules for broadcasting apply.
@@ -383,7 +392,7 @@ def quaternion_raw_multiply(a, b):
     return torch.stack((ow, ox, oy, oz), -1)
 
 
-def quaternion_multiply(a, b):
+def quaternion_multiply(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
     """
     Multiply two quaternions representing rotations, returning the quaternion
     representing their composition, i.e. the versor with nonnegative real part.
@@ -400,7 +409,7 @@ def quaternion_multiply(a, b):
     return standardize_quaternion(ab)
 
 
-def quaternion_invert(quaternion):
+def quaternion_invert(quaternion: torch.Tensor) -> torch.Tensor:
     """
     Given a quaternion representing rotation, get the quaternion representing
     its inverse.
@@ -413,10 +422,11 @@ def quaternion_invert(quaternion):
         The inverse, a tensor of quaternions of shape (..., 4).
     """
 
-    return quaternion * quaternion.new_tensor([1, -1, -1, -1])
+    scaling = torch.tensor([1, -1, -1, -1], device=quaternion.device)
+    return quaternion * scaling
 
 
-def quaternion_apply(quaternion, point):
+def quaternion_apply(quaternion: torch.Tensor, point: torch.Tensor) -> torch.Tensor:
     """
     Apply the rotation given by a quaternion to a 3D point.
     Usual torch rules for broadcasting apply.
@@ -439,7 +449,7 @@ def quaternion_apply(quaternion, point):
     return out[..., 1:]
 
 
-def axis_angle_to_matrix(axis_angle):
+def axis_angle_to_matrix(axis_angle: torch.Tensor) -> torch.Tensor:
     """
     Convert rotations given as axis/angle to rotation matrices.
 
@@ -455,7 +465,7 @@ def axis_angle_to_matrix(axis_angle):
     return quaternion_to_matrix(axis_angle_to_quaternion(axis_angle))
 
 
-def matrix_to_axis_angle(matrix):
+def matrix_to_axis_angle(matrix: torch.Tensor) -> torch.Tensor:
     """
     Convert rotations given as rotation matrices to axis/angle.
 
@@ -471,7 +481,7 @@ def matrix_to_axis_angle(matrix):
     return quaternion_to_axis_angle(matrix_to_quaternion(matrix))
 
 
-def axis_angle_to_quaternion(axis_angle):
+def axis_angle_to_quaternion(axis_angle: torch.Tensor) -> torch.Tensor:
     """
     Convert rotations given as axis/angle to quaternions.
 
@@ -485,7 +495,7 @@ def axis_angle_to_quaternion(axis_angle):
         quaternions with real part first, as tensor of shape (..., 4).
     """
     angles = torch.norm(axis_angle, p=2, dim=-1, keepdim=True)
-    half_angles = 0.5 * angles
+    half_angles = angles * 0.5
     eps = 1e-6
     small_angles = angles.abs() < eps
     sin_half_angles_over_angles = torch.empty_like(angles)
@@ -503,7 +513,7 @@ def axis_angle_to_quaternion(axis_angle):
     return quaternions
 
 
-def quaternion_to_axis_angle(quaternions):
+def quaternion_to_axis_angle(quaternions: torch.Tensor) -> torch.Tensor:
     """
     Convert rotations given as quaternions to axis/angle.
 
@@ -573,4 +583,5 @@ def matrix_to_rotation_6d(matrix: torch.Tensor) -> torch.Tensor:
     IEEE Conference on Computer Vision and Pattern Recognition, 2019.
     Retrieved from https://arxiv.org/abs/1812.07035
     """
-    return matrix[..., :2, :].clone().reshape(*matrix.size()[:-2], 6)
+    batch_dim = matrix.size()[:-2]
+    return matrix[..., :2, :].clone().reshape(batch_dim + (6,))
diff --git a/tests/test_rotation_conversions.py b/tests/test_rotation_conversions.py
@@ -8,6 +8,7 @@
 import itertools
 import math
 import unittest
+from distutils.version import LooseVersion
 from typing import Optional, Union
 
 import numpy as np
@@ -264,6 +265,25 @@ def test_6d(self):
             torch.matmul(r, r.permute(0, 2, 1)), torch.eye(3).expand_as(r), atol=1e-6
         )
 
+    @unittest.skipIf(LooseVersion(torch.__version__) < "1.9", "recent torchscript only")
+    def test_scriptable(self):
+        torch.jit.script(axis_angle_to_matrix)
+        torch.jit.script(axis_angle_to_quaternion)
+        torch.jit.script(euler_angles_to_matrix)
+        torch.jit.script(matrix_to_axis_angle)
+        torch.jit.script(matrix_to_euler_angles)
+        torch.jit.script(matrix_to_quaternion)
+        torch.jit.script(matrix_to_rotation_6d)
+        torch.jit.script(quaternion_apply)
+        torch.jit.script(quaternion_multiply)
+        torch.jit.script(quaternion_to_matrix)
+        torch.jit.script(quaternion_to_axis_angle)
+        torch.jit.script(random_quaternions)
+        torch.jit.script(random_rotation)
+        torch.jit.script(random_rotations)
+        torch.jit.script(random_quaternions)
+        torch.jit.script(rotation_6d_to_matrix)
+
     def _assert_quaternions_close(
         self,
         input: Union[torch.Tensor, np.ndarray],
