(self, zoom, mode, keep_size, align_corners=None)
| 36 | class TestZoomd(NumpyImageTestCase2D): |
| 37 | @parameterized.expand(VALID_CASES) |
| 38 | def test_correct_results(self, zoom, mode, keep_size, align_corners=None): |
| 39 | key = "img" |
| 40 | init_param = { |
| 41 | "keys": key, |
| 42 | "zoom": zoom, |
| 43 | "mode": mode, |
| 44 | "keep_size": keep_size, |
| 45 | "dtype": torch.float64, |
| 46 | "align_corners": align_corners, |
| 47 | } |
| 48 | zoom_fn = Zoomd(**init_param) |
| 49 | for p in TEST_NDARRAYS_ALL: |
| 50 | im = p(self.imt[0]) |
| 51 | call_param = {"data": {key: im}} |
| 52 | zoomed = zoom_fn(**call_param) |
| 53 | |
| 54 | # test lazy |
| 55 | # TODO: temporarily skip "nearest" test |
| 56 | if mode == "bilinear": |
| 57 | test_resampler_lazy( |
| 58 | zoom_fn, zoomed, init_param, call_param, output_key=key, atol=1e-4 if USE_COMPILED else 1e-6 |
| 59 | ) |
| 60 | zoom_fn.lazy = False |
| 61 | |
| 62 | test_local_inversion(zoom_fn, zoomed, {key: im}, key) |
| 63 | _order = 0 |
| 64 | if mode.endswith("linear"): |
| 65 | _order = 1 |
| 66 | expected = [ |
| 67 | zoom_scipy(channel, zoom=zoom, mode="nearest", order=_order, prefilter=False) for channel in self.imt[0] |
| 68 | ] |
| 69 | |
| 70 | expected = np.stack(expected).astype(np.float32) |
| 71 | assert_allclose(zoomed[key], p(expected), atol=1.0, type_test=False) |
| 72 | |
| 73 | def test_keep_size(self): |
| 74 | key = "img" |
nothing calls this directly
no test coverage detected