(self, min_zoom, max_zoom, mode, align_corners, keep_size)
| 33 | class TestRandZoomd(NumpyImageTestCase2D): |
| 34 | @parameterized.expand(VALID_CASES) |
| 35 | def test_correct_results(self, min_zoom, max_zoom, mode, align_corners, keep_size): |
| 36 | key = "img" |
| 37 | init_param = { |
| 38 | "keys": key, |
| 39 | "prob": 1.0, |
| 40 | "min_zoom": min_zoom, |
| 41 | "max_zoom": max_zoom, |
| 42 | "mode": mode, |
| 43 | "align_corners": align_corners, |
| 44 | "keep_size": keep_size, |
| 45 | "dtype": torch.float64, |
| 46 | } |
| 47 | random_zoom = RandZoomd(**init_param) |
| 48 | for p in TEST_NDARRAYS_ALL: |
| 49 | random_zoom.set_random_state(1234) |
| 50 | |
| 51 | im = p(self.imt[0]) |
| 52 | call_param = {"data": {key: im}} |
| 53 | zoomed = random_zoom(**call_param) |
| 54 | |
| 55 | # test lazy |
| 56 | # TODO: temporarily skip "nearest" test |
| 57 | if mode == "bilinear": |
| 58 | test_resampler_lazy( |
| 59 | random_zoom, zoomed, init_param, call_param, key, seed=1234, atol=1e-4 if USE_COMPILED else 1e-6 |
| 60 | ) |
| 61 | random_zoom.lazy = False |
| 62 | |
| 63 | test_local_inversion(random_zoom, zoomed, {key: im}, key) |
| 64 | expected = [ |
| 65 | zoom_scipy(channel, zoom=random_zoom.rand_zoom._zoom, mode="nearest", order=0, prefilter=False) |
| 66 | for channel in self.imt[0] |
| 67 | ] |
| 68 | |
| 69 | expected = np.stack(expected).astype(np.float32) |
| 70 | assert_allclose(zoomed[key], p(expected), atol=1.0, type_test=False) |
| 71 | |
| 72 | def test_keep_size(self): |
| 73 | key = "img" |
nothing calls this directly
no test coverage detected