def test_rad2deg(self):
# generate input data
x_rad = tgm.pi * torch.rand(2, 3, 4)
# 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)
self.assertAlmostEqual(error.item(), 0.0, places=4)
# functional
self.assertTrue(torch.allclose(x_deg, tgm.RadToDeg()(x_rad)))
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)