(self, _, spatial_axis)
| 48 | |
| 49 | @parameterized.expand(VALID_CASES) |
| 50 | def test_correct_results(self, _, spatial_axis): |
| 51 | for p in TEST_NDARRAYS_ALL: |
| 52 | im = p(self.imt[0]) |
| 53 | init_param = {"spatial_axis": spatial_axis} |
| 54 | flip = Flip(**init_param) |
| 55 | expected = [np.flip(channel, spatial_axis) for channel in self.imt[0]] |
| 56 | expected = np.stack(expected) |
| 57 | call_param = {"img": im} |
| 58 | result = flip(**call_param) |
| 59 | test_resampler_lazy(flip, result, init_param, call_param) |
| 60 | assert_allclose(result, p(expected), type_test="tensor") |
| 61 | test_local_inversion(flip, result, im) |
| 62 | |
| 63 | @parameterized.expand(TORCH_CASES) |
| 64 | def test_torch(self, spatial_axis, img: torch.Tensor, track_meta: bool, device): |
nothing calls this directly
no test coverage detected