def get_transformation_matrix(angles, translation):
"""
Create a rotation matrix defined by the rotation angles around the x, y and z axes
:param angles:
:param translation:
:return:
"""
rtvec = torch.cat((tgm.deg2rad(angles), translation), 0)
rtvec = torch.unsqueeze(rtvec, 0)
transformation_matrix = tgm.rtvec_to_pose(rtvec)
return transformation_matrix
def test_deg2rad(self):
# generate input data
x_deg = 180. * torch.rand(2, 3, 4)
# convert radians/degrees
x_rad = tgm.deg2rad(x_deg)
x_rad_to_deg = tgm.rad2deg(x_rad)
# compute error
error = utils.compute_mse(x_deg, x_rad_to_deg)
self.assertAlmostEqual(error.item(), 0.0, places=4)
# functional
self.assertTrue(torch.allclose(x_rad, tgm.DegToRad()(x_deg)))
def test_deg2rad(batch_shape, device_type):
# generate input data
x_deg = 180. * torch.rand(batch_shape)
x_deg = x_deg.to(torch.device(device_type))
# convert radians/degrees
x_rad = tgm.deg2rad(x_deg)
x_rad_to_deg = tgm.rad2deg(x_rad)
# compute error
error = utils.compute_mse(x_deg, x_rad_to_deg)
assert pytest.approx(error.item(), 0.0)
# functional
assert torch.allclose(x_rad, tgm.DegToRad()(x_deg))
assert gradcheck(tgm.deg2rad, (utils.tensor_to_gradcheck_var(x_deg), ),
raise_exception=True)
def test_rad2deg(batch_shape, device_type):
# generate input data
x_rad = tgm.pi * torch.rand(batch_shape)
x_rad = x_rad.to(torch.device(device_type))
# convert radians/degrees
x_deg = tgm.rad2deg(x_rad)
x_deg_to_rad = tgm.deg2rad(x_deg)
# compute error
error = utils.compute_mse(x_rad, x_deg_to_rad)
assert pytest.approx(error.item(), 0.0)
# functional
assert torch.allclose(x_deg, tgm.RadToDeg()(x_rad))
# evaluate function gradient
assert gradcheck(tgm.rad2deg, (utils.tensor_to_gradcheck_var(x_rad), ),
raise_exception=True)