(self, min_zoom, max_zoom, mode, keep_size, align_corners=None)
| 35 | class TestRandZoom(NumpyImageTestCase2D): |
| 36 | @parameterized.expand(VALID_CASES) |
| 37 | def test_correct_results(self, min_zoom, max_zoom, mode, keep_size, align_corners=None): |
| 38 | for p in TEST_NDARRAYS_ALL: |
| 39 | init_param = { |
| 40 | "prob": 1.0, |
| 41 | "min_zoom": min_zoom, |
| 42 | "max_zoom": max_zoom, |
| 43 | "mode": mode, |
| 44 | "keep_size": keep_size, |
| 45 | "dtype": torch.float64, |
| 46 | "align_corners": align_corners, |
| 47 | } |
| 48 | random_zoom = RandZoom(**init_param) |
| 49 | random_zoom.set_random_state(1234) |
| 50 | im = p(self.imt[0]) |
| 51 | call_param = {"img": im} |
| 52 | zoomed = random_zoom(**call_param) |
| 53 | |
| 54 | # test lazy |
| 55 | # TODO: temporarily skip "nearest" test |
| 56 | if mode == InterpolateMode.BILINEAR: |
| 57 | test_resampler_lazy( |
| 58 | random_zoom, zoomed, init_param, call_param, seed=1234, atol=1e-4 if USE_COMPILED else 1e-6 |
| 59 | ) |
| 60 | |
| 61 | test_local_inversion(random_zoom, zoomed, im) |
| 62 | expected = [ |
| 63 | zoom_scipy(channel, zoom=random_zoom._zoom, mode="nearest", order=0, prefilter=False) |
| 64 | for channel in self.imt[0] |
| 65 | ] |
| 66 | |
| 67 | expected = np.stack(expected).astype(np.float32) |
| 68 | assert_allclose(zoomed, p(expected), atol=1.0, type_test=False) |
| 69 | |
| 70 | def test_keep_size(self): |
| 71 | for p in TEST_NDARRAYS_ALL: |
nothing calls this directly
no test coverage detected