def test_camera_position_from_angles_vectors_bad_broadcast(self):
# Batch dim for broadcast must be N or 1
dist = torch.tensor([2.0, 3.0, 5.0])
elev = torch.tensor([0.0, 90.0])
azim = torch.tensor([90.0])
with self.assertRaises(ValueError):
camera_position_from_spherical_angles(dist, elev, azim)
def test_camera_position_from_angles_vectors_mixed_broadcast_grads(self):
dist = torch.tensor([2.0, 3.0, 5.0], requires_grad=True)
elev = torch.tensor(45.0, requires_grad=True)
azim = 45.0
position = camera_position_from_spherical_angles(dist, elev, azim)
position.sum().backward()
self.assertTrue(hasattr(elev, 'grad'))
self.assertTrue(hasattr(dist, 'grad'))
elev_grad = elev.grad.clone()
dist_grad = dist.grad.clone()
azim = torch.tensor(azim)
elev = math.pi / 180.0 * elev.detach()
azim = math.pi / 180.0 * azim
grad_dist = (
torch.cos(elev) * torch.sin(azim)
+ torch.sin(elev)
+ torch.cos(elev) * torch.cos(azim)
)
grad_elev = (
-(torch.sin(elev)) * torch.sin(azim)
+ torch.cos(elev)
- torch.sin(elev) * torch.cos(azim)
)
grad_elev = (dist * (math.pi / 180.0) * grad_elev).sum()
self.assertClose(elev_grad, grad_elev)
self.assertClose(dist_grad, torch.full([3], grad_dist))
def test_camera_position_from_angles_torch_scalar_grads(self):
dist = torch.tensor(2.7, requires_grad=True)
elev = torch.tensor(45.0, requires_grad=True)
azim = torch.tensor(45.0)
position = camera_position_from_spherical_angles(dist, elev, azim)
position.sum().backward()
self.assertTrue(hasattr(elev, 'grad'))
self.assertTrue(hasattr(dist, 'grad'))
elev_grad = elev.grad.clone()
dist_grad = dist.grad.clone()
elev = math.pi / 180.0 * elev.detach()
azim = math.pi / 180.0 * azim
grad_dist = (
torch.cos(elev) * torch.sin(azim)
+ torch.sin(elev)
+ torch.cos(elev) * torch.cos(azim)
)
grad_elev = (
-(torch.sin(elev)) * torch.sin(azim)
+ torch.cos(elev)
- torch.sin(elev) * torch.cos(azim)
)
grad_elev = dist * (math.pi / 180.0) * grad_elev
self.assertClose(elev_grad, grad_elev)
self.assertClose(dist_grad, grad_dist)
def test_camera_position_from_angles_vectors(self):
dist = torch.tensor([2.0, 2.0])
elev = torch.tensor([0.0, 90.0])
azim = torch.tensor([90.0, 0.0])
expected_position = torch.tensor([[2.0, 0.0, 0.0], [0.0, 2.0, 0.0]],
dtype=torch.float32)
position = camera_position_from_spherical_angles(dist, elev, azim)
self.assertClose(position, expected_position, atol=2e-7)
def test_camera_position_from_angles_mixed_scalars(self):
dist = 2.7
elev = torch.tensor(0.0)
azim = 90.0
expected_position = torch.tensor([2.7, 0.0, 0.0],
dtype=torch.float32).view(1, 3)
position = camera_position_from_spherical_angles(dist, elev, azim)
self.assertClose(position, expected_position, atol=2e-7)
def test_camera_position_from_angles_torch_scalars(self):
dist = torch.tensor(2.7)
elev = torch.tensor(0.0)
azim = torch.tensor(90.0)
expected_position = torch.tensor([2.7, 0.0, 0.0],
dtype=torch.float32).view(1, 3)
position = camera_position_from_spherical_angles(dist, elev, azim)
self.assertTrue(torch.allclose(position, expected_position, atol=2e-7))
def test_camera_position_from_angles_python_scalar(self):
dist = 2.7
elev = 90.0
azim = 0.0
expected_position = torch.tensor([0.0, 2.7, 0.0],
dtype=torch.float32).view(1, 3)
position = camera_position_from_spherical_angles(dist, elev, azim)
self.assertTrue(torch.allclose(position, expected_position, atol=2e-7))
def test_camera_position_from_angles_vectors_mixed_broadcast(self):
dist = torch.tensor([2.0, 3.0, 5.0])
elev = 0.0
azim = torch.tensor(90.0)
expected_position = torch.tensor(
[[2.0, 0.0, 0.0], [3.0, 0.0, 0.0], [5.0, 0.0, 0.0]],
dtype=torch.float32)
position = camera_position_from_spherical_angles(dist, elev, azim)
self.assertClose(position, expected_position, atol=3e-7)
def test_camera_position_from_angles_python_scalar_radians(self):
dist = 2.7
elev = math.pi / 2
azim = 0.0
expected_position = torch.tensor([0.0, 2.7, 0.0], dtype=torch.float32)
expected_position = expected_position.view(1, 3)
position = camera_position_from_spherical_angles(
dist, elev, azim, degrees=False
)
self.assertClose(position, expected_position, atol=2e-7)
def test_look_at_view_transform_corner_case(self):
dist = 2.7
elev = 90
azim = 90
expected_position = torch.tensor([0.0, 2.7, 0.0],
dtype=torch.float32).view(1, 3)
position = camera_position_from_spherical_angles(dist, elev, azim)
self.assertClose(position, expected_position, atol=2e-7)
R, _ = look_at_view_transform(eye=position)
x_axis = R[:, :, 0]
expected_x_axis = torch.tensor([0.0, 0.0, -1.0],
dtype=torch.float32).view(1, 3)
self.assertClose(x_axis, expected_x_axis, atol=5e-3)
