def test_correct_results(self, min_zoom, max_zoom, mode, align_corners, keep_size): key = "img" random_zoom = RandZoomd( key, prob=1.0, min_zoom=min_zoom, max_zoom=max_zoom, mode=mode, align_corners=align_corners, keep_size=keep_size, ) random_zoom.set_random_state(1234) zoomed = random_zoom({key: self.imt[0]}) expected = [] for channel in self.imt[0]: expected.append( zoom_scipy(channel, zoom=random_zoom._zoom, mode="nearest", order=0, prefilter=False)) expected = np.stack(expected).astype(np.float32) np.testing.assert_allclose(expected, zoomed[key], atol=1.0)
def test_gpu_zoom(self, min_zoom, max_zoom, order, mode, cval, prefilter): key = "img" if importlib.util.find_spec("cupy"): random_zoom = RandZoomd( key, prob=1.0, min_zoom=min_zoom, max_zoom=max_zoom, interp_order=order, mode=mode, cval=cval, prefilter=prefilter, use_gpu=True, keep_size=False, ) random_zoom.set_random_state(234) zoomed = random_zoom({key: self.imt[0]}) expected = list() for channel in self.imt[0]: expected.append( zoom_scipy(channel, zoom=random_zoom._zoom, mode=mode, order=order, cval=cval, prefilter=prefilter)) expected = np.stack(expected).astype(np.float32) self.assertTrue(np.allclose(expected, zoomed[key]))
def test_correct_results(self, min_zoom, max_zoom, order, mode, cval, prefilter, use_gpu, keep_size): key = "img" random_zoom = RandZoomd( key, prob=1.0, min_zoom=min_zoom, max_zoom=max_zoom, interp_order=order, mode=mode, cval=cval, prefilter=prefilter, use_gpu=use_gpu, keep_size=keep_size, ) random_zoom.set_random_state(234) zoomed = random_zoom({key: self.imt[0]}) expected = list() for channel in self.imt[0]: expected.append( zoom_scipy(channel, zoom=random_zoom._zoom, mode=mode, order=order, cval=cval, prefilter=prefilter)) expected = np.stack(expected).astype(np.float32) self.assertTrue(np.allclose(expected, zoomed[key]))
def test_correct_results(self, min_zoom, max_zoom, mode, align_corners, keep_size): key = "img" random_zoom = RandZoomd( key, prob=1.0, min_zoom=min_zoom, max_zoom=max_zoom, mode=mode, align_corners=align_corners, keep_size=keep_size, ) for p in TEST_NDARRAYS: random_zoom.set_random_state(1234) zoomed = random_zoom({key: p(self.imt[0])}) expected = [ zoom_scipy(channel, zoom=random_zoom.rand_zoom._zoom, mode="nearest", order=0, prefilter=False) for channel in self.imt[0] ] expected = np.stack(expected).astype(np.float32) assert_allclose(zoomed[key], p(expected), atol=1.0)
def test_auto_expand_3d(self): random_zoom = RandZoomd( keys="img", prob=1.0, min_zoom=[0.8, 0.7], max_zoom=[1.2, 1.3], mode="nearest", keep_size=False ) for p in TEST_NDARRAYS: random_zoom.set_random_state(1234) test_data = {"img": p(np.random.randint(0, 2, size=[2, 2, 3, 4]))} zoomed = random_zoom(test_data) assert_allclose(random_zoom.rand_zoom._zoom, (1.048844, 1.048844, 0.962637), atol=1e-2) assert_allclose(zoomed["img"].shape, (2, 2, 3, 3))