def test_rotate_x_torch_tensor(self):
angle = torch.tensor([0, 45.0, 90.0]) # (N)
t = RotateAxisAngle(angle=angle, axis="X")
r2_i = 1 / math.sqrt(2)
r2_2 = math.sqrt(2) / 2
# fmt: off
matrix = torch.tensor(
[
[
[1.0, 0.0, 0.0, 0.0],
[0.0, 1.0, 0.0, 0.0],
[0.0, 0.0, 1.0, 0.0],
[0.0, 0.0, 0.0, 1.0],
],
[
[1.0, 0.0, 0.0, 0.0], # noqa: E241, E201
[0.0, r2_2, -r2_i, 0.0], # noqa: E241, E201
[0.0, r2_i, r2_2, 0.0], # noqa: E241, E201
[0.0, 0.0, 0.0, 1.0], # noqa: E241, E201
],
[
[1.0, 0.0, 0.0, 0.0], # noqa: E241, E201
[0.0, 0.0, -1.0, 0.0], # noqa: E241, E201
[0.0, 1.0, 0.0, 0.0], # noqa: E241, E201
[0.0, 0.0, 0.0, 1.0], # noqa: E241, E201
]
],
dtype=torch.float32,
)
# fmt: on
self.assertTrue(torch.allclose(t._matrix, matrix, atol=1e-7))
angle = angle[..., None] # (N, 1)
t = RotateAxisAngle(angle=angle, axis="X")
self.assertTrue(torch.allclose(t._matrix, matrix, atol=1e-7))
def test_rotate_y_torch_scalar(self):
"""
Test rotation about Y axis. With a right hand coordinate system this
should result in a vector pointing along the x-axis being rotated to
point along the negative z axis.
"""
angle = torch.tensor(90.0)
t = RotateAxisAngle(angle=angle, axis="Y")
# fmt: off
matrix = torch.tensor(
[[
[0.0, 0.0, -1.0, 0.0], # noqa: E241, E201
[0.0, 1.0, 0.0, 0.0], # noqa: E241, E201
[1.0, 0.0, 0.0, 0.0], # noqa: E241, E201
[0.0, 0.0, 0.0, 1.0], # noqa: E241, E201
]],
dtype=torch.float32,
)
# fmt: on
points = torch.tensor([1.0, 0.0, 0.0])[None, None, :] # (1, 1, 3)
transformed_points = t.transform_points(points)
expected_points = torch.tensor([0.0, 0.0, -1.0])
self.assertTrue(
torch.allclose(transformed_points.squeeze(),
expected_points,
atol=1e-7))
self.assertTrue(torch.allclose(t._matrix, matrix, atol=1e-7))
def test_rotate_angle_fail(self):
angle = torch.tensor([[0, 45.0, 90.0], [0, 45.0, 90.0]])
with self.assertRaises(ValueError):
RotateAxisAngle(angle=angle, axis="X")
# Helpful comments below.# Helpful comments below.# Helpful comments below.# Helpful comments below.# Helpful comments below.# Helpful comments below.
def test_rotate_compose_x_y_z(self):
angle = torch.tensor(90.0)
t1 = RotateAxisAngle(angle=angle, axis="X")
t2 = RotateAxisAngle(angle=angle, axis="Y")
t3 = RotateAxisAngle(angle=angle, axis="Z")
t = t1.compose(t2, t3)
# fmt: off
matrix1 = torch.tensor(
[
[
[1.0, 0.0, 0.0, 0.0], # noqa: E241, E201
[0.0, 0.0, 1.0, 0.0], # noqa: E241, E201
[0.0, -1.0, 0.0, 0.0], # noqa: E241, E201
[0.0, 0.0, 0.0, 1.0], # noqa: E241, E201
]
],
dtype=torch.float32,
)
matrix2 = torch.tensor(
[
[
[0.0, 0.0, -1.0, 0.0], # noqa: E241, E201
[0.0, 1.0, 0.0, 0.0], # noqa: E241, E201
[1.0, 0.0, 0.0, 0.0], # noqa: E241, E201
[0.0, 0.0, 0.0, 1.0], # noqa: E241, E201
]
],
dtype=torch.float32,
)
matrix3 = torch.tensor(
[
[
[ 0.0, 1.0, 0.0, 0.0], # noqa: E241, E201
[-1.0, 0.0, 0.0, 0.0], # noqa: E241, E201
[ 0.0, 0.0, 1.0, 0.0], # noqa: E241, E201
[ 0.0, 0.0, 0.0, 1.0], # noqa: E241, E201
]
],
dtype=torch.float32,
)
# fmt: on
# order of transforms is t1 -> t2
matrix = torch.matmul(matrix1, torch.matmul(matrix2, matrix3))
composed_matrix = t.get_matrix()
self.assertTrue(torch.allclose(composed_matrix, matrix, atol=1e-7))
def test_rotate_z_python_scalar(self):
t = RotateAxisAngle(angle=90, axis="Z")
# fmt: off
matrix = torch.tensor(
[[
[0.0, 1.0, 0.0, 0.0], # noqa: E241, E201
[-1.0, 0.0, 0.0, 0.0], # noqa: E241, E201
[0.0, 0.0, 1.0, 0.0], # noqa: E241, E201
[0.0, 0.0, 0.0, 1.0], # noqa: E241, E201
]],
dtype=torch.float32,
)
# fmt: on
points = torch.tensor([1.0, 0.0, 0.0])[None, None, :] # (1, 1, 3)
transformed_points = t.transform_points(points)
expected_points = torch.tensor([0.0, 1.0, 0.0])
self.assertTrue(
torch.allclose(transformed_points.squeeze(),
expected_points,
atol=1e-7))
self.assertTrue(torch.allclose(t._matrix, matrix, atol=1e-7))
def test_lower_case_axis(self):
t = RotateAxisAngle(angle=90.0, axis="z")
# fmt: off
matrix = torch.tensor(
[[
[0.0, -1.0, 0.0, 0.0], # noqa: E241, E201
[1.0, 0.0, 0.0, 0.0], # noqa: E241, E201
[0.0, 0.0, 1.0, 0.0], # noqa: E241, E201
[0.0, 0.0, 0.0, 1.0], # noqa: E241, E201
]],
dtype=torch.float32,
)
# fmt: on
self.assertTrue(torch.allclose(t._matrix, matrix, atol=1e-7))
def test_rotate_angle_radians(self):
t = RotateAxisAngle(angle=math.pi / 2, degrees=False, axis="Z")
# fmt: off
matrix = torch.tensor(
[[
[0.0, -1.0, 0.0, 0.0], # noqa: E241, E201
[1.0, 0.0, 0.0, 0.0], # noqa: E241, E201
[0.0, 0.0, 1.0, 0.0], # noqa: E241, E201
[0.0, 0.0, 0.0, 1.0], # noqa: E241, E201
]],
dtype=torch.float32,
)
# fmt: on
self.assertTrue(torch.allclose(t._matrix, matrix, atol=1e-7))
def test_rotate_z_python_scalar(self):
t = RotateAxisAngle(angle=90, axis="Z")
# fmt: off
matrix = torch.tensor(
[[
[0.0, -1.0, 0.0, 0.0], # noqa: E241, E201
[1.0, 0.0, 0.0, 0.0], # noqa: E241, E201
[0.0, 0.0, 1.0, 0.0], # noqa: E241, E201
[0.0, 0.0, 0.0, 1.0], # noqa: E241, E201
]],
dtype=torch.float32,
)
# fmt: on
self.assertTrue(torch.allclose(t._matrix, matrix, atol=1e-7))
def test_rotate_y_torch_scalar(self):
angle = torch.tensor(90.0)
t = RotateAxisAngle(angle=angle, axis="Y")
# fmt: off
matrix = torch.tensor(
[[
[0.0, 0.0, 1.0, 0.0], # noqa: E241, E201
[0.0, 1.0, 0.0, 0.0], # noqa: E241, E201
[-1.0, 0.0, 0.0, 0.0], # noqa: E241, E201
[0.0, 0.0, 0.0, 1.0], # noqa: E241, E201
]],
dtype=torch.float32,
)
# fmt: on
self.assertTrue(torch.allclose(t._matrix, matrix, atol=1e-7))
def test_axis_fail(self):
with self.assertRaises(ValueError):
RotateAxisAngle(angle=90.0, axis="P")
def test_rotate_angle_fail(self):
angle = torch.tensor([[0, 45.0, 90.0], [0, 45.0, 90.0]])
with self.assertRaises(ValueError):
RotateAxisAngle(angle=angle, axis='X')
