def test_orthographic_kwargs(self):
cameras = FoVOrthographicCameras(znear=5.0, zfar=100.0)
far = 10.0
P = cameras.get_projection_transform(znear=1.0, zfar=far)
vertices = torch.tensor([1, 2, far], dtype=torch.float32)
projected_verts = torch.tensor([1, 2, 1], dtype=torch.float32)
vertices = vertices[None, None, :]
v1 = P.transform_points(vertices)
self.assertClose(v1.squeeze(), projected_verts)
def test_orthographic_scaled(self):
vertices = torch.tensor([1, 2, 0.5], dtype=torch.float32)
vertices = vertices[None, None, :]
scale = torch.tensor([[2.0, 0.5, 20]])
# applying the scale puts the z coordinate at the far clipping plane
# so the z is mapped to 1.0
projected_verts = torch.tensor([2, 1, 1], dtype=torch.float32)
cameras = FoVOrthographicCameras(znear=1.0, zfar=10.0, scale_xyz=scale)
P = cameras.get_projection_transform()
v1 = P.transform_points(vertices)
v2 = orthographic_project_naive(vertices, scale)
self.assertClose(v1[..., :2], v2[..., :2])
self.assertClose(v1, projected_verts[None, None])
def test_orthographic_mixed_inputs_broadcast(self):
far = torch.tensor([10.0, 20.0])
near = 1.0
cameras = FoVOrthographicCameras(znear=near, zfar=far)
P = cameras.get_projection_transform()
vertices = torch.tensor([1.0, 2.0, 10.0], dtype=torch.float32)
z2 = 1.0 / (20.0 - 1.0) * 10.0 + -1.0 / (20.0 - 1.0)
projected_verts = torch.tensor([[1.0, 2.0, 1.0], [1.0, 2.0, z2]],
dtype=torch.float32)
vertices = vertices[None, None, :]
v1 = P.transform_points(vertices)
v2 = orthographic_project_naive(vertices)
self.assertClose(v1[..., :2], torch.cat([v2, v2])[..., :2])
self.assertClose(v1.squeeze(), projected_verts)
def test_orthographic_mixed_inputs_grad(self):
far = torch.tensor([10.0])
near = 1.0
scale = torch.tensor([[1.0, 1.0, 1.0]], requires_grad=True)
cameras = FoVOrthographicCameras(znear=near, zfar=far, scale_xyz=scale)
P = cameras.get_projection_transform()
vertices = torch.tensor([1.0, 2.0, 10.0], dtype=torch.float32)
vertices_batch = vertices[None, None, :]
v1 = P.transform_points(vertices_batch)
v1.sum().backward()
self.assertTrue(hasattr(scale, "grad"))
scale_grad = scale.grad.clone()
grad_scale = torch.tensor([[
vertices[0] * P._matrix[:, 0, 0],
vertices[1] * P._matrix[:, 1, 1],
vertices[2] * P._matrix[:, 2, 2],
]])
self.assertClose(scale_grad, grad_scale)
def test_orthographic(self):
far = 10.0
near = 1.0
cameras = FoVOrthographicCameras(znear=near, zfar=far)
P = cameras.get_projection_transform()
vertices = torch.tensor([1, 2, far], dtype=torch.float32)
projected_verts = torch.tensor([1, 2, 1], dtype=torch.float32)
vertices = vertices[None, None, :]
v1 = P.transform_points(vertices)
v2 = orthographic_project_naive(vertices)
self.assertClose(v1[..., :2], v2[..., :2])
self.assertClose(v1.squeeze(), projected_verts)
vertices[..., 2] = near
projected_verts[2] = 0.0
v1 = P.transform_points(vertices)
v2 = orthographic_project_naive(vertices)
self.assertClose(v1[..., :2], v2[..., :2])
self.assertClose(v1.squeeze(), projected_verts)