def test_scale_double(self, device, dtype):
scale_factor = torch.tensor(0.5)
camera_matrix = torch.tensor(
[[[100.0, 0.0, 50.0], [0.0, 100.0, 50.0], [0.0, 0.0, 1.0]]],
device=device,
dtype=dtype)
camera_matrix_expected = torch.tensor(
[[[50.0, 0.0, 25.0], [0.0, 50.0, 25.0], [0.0, 0.0, 1.0]]],
device=device,
dtype=dtype)
camera_matrix_scale = epi.scale_intrinsics(camera_matrix, scale_factor)
assert_allclose(camera_matrix_scale,
camera_matrix_expected,
atol=1e-4,
rtol=1e-4)
def test_shape(self, batch_size, device, dtype):
B: int = batch_size
scale_factor = torch.rand(B, device=device, dtype=dtype)
camera_matrix = torch.rand(B, 3, 3, device=device, dtype=dtype)
camera_matrix_scale = epi.scale_intrinsics(camera_matrix, scale_factor)
assert camera_matrix_scale.shape == (B, 3, 3)
def test_smoke_tensor(self, device, dtype):
scale_factor = torch.tensor(1.0)
camera_matrix = torch.rand(1, 3, 3, device=device, dtype=dtype)
camera_matrix_scale = epi.scale_intrinsics(camera_matrix, scale_factor)
assert camera_matrix_scale.shape == (1, 3, 3)