Method test_spacing

tests/transforms/test_spacing.py:230–256

(
        self,
        init_param: dict,
        img: torch.Tensor,
        affine: torch.Tensor,
        data_param: dict,
        expected_output: torch.Tensor,
        device: torch.device,
    )

class TestSpacingCase(unittest.TestCase):
    @parameterized.expand(TESTS)
    def test_spacing(
        self,
        init_param: dict,
        img: torch.Tensor,
        affine: torch.Tensor,
        data_param: dict,
        expected_output: torch.Tensor,
        device: torch.device,
    ):
        img = MetaTensor(img, affine=affine).to(device)
        tr = Spacing(**init_param)
        call_param = data_param.copy()
        call_param["data_array"] = img
        res: MetaTensor = tr(**call_param)  # type: ignore
        self.assertEqual(img.device, res.device)

        test_resampler_lazy(tr, res, init_param=init_param, call_param=call_param)

        if callable(expected_output):
            expected_output = expected_output(device)
        assert_allclose(res, expected_output, atol=1e-1, rtol=1e-1)
        sr = min(len(res.shape) - 1, 3)
        if isinstance(init_param["pixdim"], float):
            init_param["pixdim"] = [init_param["pixdim"]] * sr
        init_pixdim = init_param["pixdim"][:sr]
        norm = affine_to_spacing(res.affine, sr).cpu().numpy()
        assert_allclose(fall_back_tuple(init_pixdim, norm), norm, type_test=False)  # type: ignore

    @parameterized.expand(TESTS_TORCH)
    def test_spacing_torch(self, pixdim, img, track_meta: bool):