diff --git a/tests/test_clip_intensity_percentiles.py b/tests/test_clip_intensity_percentiles.py index cab2a89a47..77f811db87 100644 --- a/tests/test_clip_intensity_percentiles.py +++ b/tests/test_clip_intensity_percentiles.py @@ -18,9 +18,32 @@ from monai.transforms import ClipIntensityPercentiles from monai.transforms.utils import soft_clip from monai.transforms.utils_pytorch_numpy_unification import clip, percentile +from monai.utils.type_conversion import convert_to_tensor from tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, NumpyImageTestCase3D, assert_allclose +def test_hard_clip_func(im, lower, upper): + im_t = convert_to_tensor(im) + if lower is None: + upper = percentile(im_t, upper) + elif upper is None: + lower = percentile(im_t, lower) + else: + lower, upper = percentile(im_t, (lower, upper)) + return clip(im_t, lower, upper) + + +def test_soft_clip_func(im, lower, upper): + im_t = convert_to_tensor(im) + if lower is None: + upper = percentile(im_t, upper) + elif upper is None: + lower = percentile(im_t, lower) + else: + lower, upper = percentile(im_t, (lower, upper)) + return soft_clip(im_t, minv=lower, maxv=upper, sharpness_factor=1.0, dtype=torch.float32) + + class TestClipIntensityPercentiles2D(NumpyImageTestCase2D): @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -28,8 +51,7 @@ def test_hard_clipping_two_sided(self, p): hard_clipper = ClipIntensityPercentiles(upper=95, lower=5) im = p(self.imt) result = hard_clipper(im) - lower, upper = percentile(im, (5, 95)) - expected = clip(im, lower, upper) + expected = test_hard_clip_func(im, 5, 95) assert_allclose(result, p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -37,8 +59,7 @@ def test_hard_clipping_one_sided_high(self, p): hard_clipper = ClipIntensityPercentiles(upper=95, lower=None) im = p(self.imt) result = hard_clipper(im) - lower, upper = percentile(im, (0, 95)) - expected = clip(im, lower, upper) + expected = test_hard_clip_func(im, 0, 95) assert_allclose(result, p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -46,8 +67,7 @@ def test_hard_clipping_one_sided_low(self, p): hard_clipper = ClipIntensityPercentiles(upper=None, lower=5) im = p(self.imt) result = hard_clipper(im) - lower, upper = percentile(im, (5, 100)) - expected = clip(im, lower, upper) + expected = test_hard_clip_func(im, 5, 100) assert_allclose(result, p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -55,9 +75,8 @@ def test_soft_clipping_two_sided(self, p): soft_clipper = ClipIntensityPercentiles(upper=95, lower=5, sharpness_factor=1.0) im = p(self.imt) result = soft_clipper(im) - lower, upper = percentile(im, (5, 95)) - expected = soft_clip(im, sharpness_factor=1.0, minv=lower, maxv=upper, dtype=torch.float32) - # the rtol is set to 1e-6 because the logaddexp function used in softplus is not stable accross torch and numpy + expected = test_soft_clip_func(im, 5, 95) + # the rtol is set to 1e-4 because the logaddexp function used in softplus is not stable accross torch and numpy assert_allclose(result, p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -65,9 +84,8 @@ def test_soft_clipping_one_sided_high(self, p): soft_clipper = ClipIntensityPercentiles(upper=95, lower=None, sharpness_factor=1.0) im = p(self.imt) result = soft_clipper(im) - upper = percentile(im, 95) - expected = soft_clip(im, sharpness_factor=1.0, minv=None, maxv=upper, dtype=torch.float32) - # the rtol is set to 5e-5 because the logaddexp function used in softplus is not stable accross torch and numpy + expected = test_soft_clip_func(im, None, 95) + # the rtol is set to 1e-4 because the logaddexp function used in softplus is not stable accross torch and numpy assert_allclose(result, p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -75,9 +93,8 @@ def test_soft_clipping_one_sided_low(self, p): soft_clipper = ClipIntensityPercentiles(upper=None, lower=5, sharpness_factor=1.0) im = p(self.imt) result = soft_clipper(im) - lower = percentile(im, 5) - expected = soft_clip(im, sharpness_factor=1.0, minv=lower, maxv=None, dtype=torch.float32) - # the rtol is set to 1e-6 because the logaddexp function used in softplus is not stable accross torch and numpy + expected = test_soft_clip_func(im, 5, None) + # the rtol is set to 1e-4 because the logaddexp function used in softplus is not stable accross torch and numpy assert_allclose(result, p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -85,7 +102,8 @@ def test_channel_wise(self, p): clipper = ClipIntensityPercentiles(upper=95, lower=5, channel_wise=True) im = p(self.imt) result = clipper(im) - for i, c in enumerate(im): + im_t = convert_to_tensor(self.imt) + for i, c in enumerate(im_t): lower, upper = percentile(c, (5, 95)) expected = clip(c, lower, upper) assert_allclose(result[i], p(expected), type_test="tensor", rtol=1e-4, atol=0) @@ -118,8 +136,7 @@ def test_hard_clipping_two_sided(self, p): hard_clipper = ClipIntensityPercentiles(upper=95, lower=5) im = p(self.imt) result = hard_clipper(im) - lower, upper = percentile(im, (5, 95)) - expected = clip(im, lower, upper) + expected = test_hard_clip_func(im, 5, 95) assert_allclose(result, p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -127,8 +144,7 @@ def test_hard_clipping_one_sided_high(self, p): hard_clipper = ClipIntensityPercentiles(upper=95, lower=None) im = p(self.imt) result = hard_clipper(im) - lower, upper = percentile(im, (0, 95)) - expected = clip(im, lower, upper) + expected = test_hard_clip_func(im, 0, 95) assert_allclose(result, p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -136,8 +152,7 @@ def test_hard_clipping_one_sided_low(self, p): hard_clipper = ClipIntensityPercentiles(upper=None, lower=5) im = p(self.imt) result = hard_clipper(im) - lower, upper = percentile(im, (5, 100)) - expected = clip(im, lower, upper) + expected = test_hard_clip_func(im, 5, 100) assert_allclose(result, p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -145,8 +160,7 @@ def test_soft_clipping_two_sided(self, p): soft_clipper = ClipIntensityPercentiles(upper=95, lower=5, sharpness_factor=1.0) im = p(self.imt) result = soft_clipper(im) - lower, upper = percentile(im, (5, 95)) - expected = soft_clip(im, sharpness_factor=1.0, minv=lower, maxv=upper, dtype=torch.float32) + expected = test_soft_clip_func(im, 5, 95) # the rtol is set to 1e-4 because the logaddexp function used in softplus is not stable accross torch and numpy assert_allclose(result, p(expected), type_test="tensor", rtol=1e-4, atol=0) @@ -155,9 +169,8 @@ def test_soft_clipping_one_sided_high(self, p): soft_clipper = ClipIntensityPercentiles(upper=95, lower=None, sharpness_factor=1.0) im = p(self.imt) result = soft_clipper(im) - upper = percentile(im, 95) - expected = soft_clip(im, sharpness_factor=1.0, minv=None, maxv=upper, dtype=torch.float32) - # the rtol is set to 5e-5 because the logaddexp function used in softplus is not stable accross torch and numpy + expected = test_soft_clip_func(im, None, 95) + # the rtol is set to 1e-4 because the logaddexp function used in softplus is not stable accross torch and numpy assert_allclose(result, p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -165,9 +178,8 @@ def test_soft_clipping_one_sided_low(self, p): soft_clipper = ClipIntensityPercentiles(upper=None, lower=5, sharpness_factor=1.0) im = p(self.imt) result = soft_clipper(im) - lower = percentile(im, 5) - expected = soft_clip(im, sharpness_factor=1.0, minv=lower, maxv=None, dtype=torch.float32) - # the rtol is set to 1e-6 because the logaddexp function used in softplus is not stable accross torch and numpy + expected = test_soft_clip_func(im, 5, None) + # the rtol is set to 1e-4 because the logaddexp function used in softplus is not stable accross torch and numpy assert_allclose(result, p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -175,7 +187,8 @@ def test_channel_wise(self, p): clipper = ClipIntensityPercentiles(upper=95, lower=5, channel_wise=True) im = p(self.imt) result = clipper(im) - for i, c in enumerate(im): + im_t = convert_to_tensor(self.imt) + for i, c in enumerate(im_t): lower, upper = percentile(c, (5, 95)) expected = clip(c, lower, upper) assert_allclose(result[i], p(expected), type_test="tensor", rtol=1e-4, atol=0) diff --git a/tests/test_clip_intensity_percentilesd.py b/tests/test_clip_intensity_percentilesd.py index 98840419a0..3e06b18418 100644 --- a/tests/test_clip_intensity_percentilesd.py +++ b/tests/test_clip_intensity_percentilesd.py @@ -13,14 +13,15 @@ import unittest -import torch from parameterized import parameterized from monai.transforms import ClipIntensityPercentilesd -from monai.transforms.utils import soft_clip from monai.transforms.utils_pytorch_numpy_unification import clip, percentile +from monai.utils.type_conversion import convert_to_tensor from tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, NumpyImageTestCase3D, assert_allclose +from .test_clip_intensity_percentiles import test_hard_clip_func, test_soft_clip_func + class TestClipIntensityPercentilesd2D(NumpyImageTestCase2D): @@ -30,8 +31,7 @@ def test_hard_clipping_two_sided(self, p): hard_clipper = ClipIntensityPercentilesd(keys=[key], upper=95, lower=5) im = p(self.imt) result = hard_clipper({key: im}) - lower, upper = percentile(im, (5, 95)) - expected = clip(im, lower, upper) + expected = test_hard_clip_func(im, 5, 95) assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -40,8 +40,7 @@ def test_hard_clipping_one_sided_high(self, p): hard_clipper = ClipIntensityPercentilesd(keys=[key], upper=95, lower=None) im = p(self.imt) result = hard_clipper({key: im}) - lower, upper = percentile(im, (0, 95)) - expected = clip(im, lower, upper) + expected = test_hard_clip_func(im, 0, 95) assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -50,8 +49,7 @@ def test_hard_clipping_one_sided_low(self, p): hard_clipper = ClipIntensityPercentilesd(keys=[key], upper=None, lower=5) im = p(self.imt) result = hard_clipper({key: im}) - lower, upper = percentile(im, (5, 100)) - expected = clip(im, lower, upper) + expected = test_hard_clip_func(im, 5, 100) assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -60,9 +58,8 @@ def test_soft_clipping_two_sided(self, p): soft_clipper = ClipIntensityPercentilesd(keys=[key], upper=95, lower=5, sharpness_factor=1.0) im = p(self.imt) result = soft_clipper({key: im}) - lower, upper = percentile(im, (5, 95)) - expected = soft_clip(im, sharpness_factor=1.0, minv=lower, maxv=upper, dtype=torch.float32) - # the rtol is set to 1e-6 because the logaddexp function used in softplus is not stable accross torch and numpy + expected = test_soft_clip_func(im, 5, 95) + # the rtol is set to 1e-4 because the logaddexp function used in softplus is not stable accross torch and numpy assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -71,9 +68,8 @@ def test_soft_clipping_one_sided_high(self, p): soft_clipper = ClipIntensityPercentilesd(keys=[key], upper=95, lower=None, sharpness_factor=1.0) im = p(self.imt) result = soft_clipper({key: im}) - upper = percentile(im, 95) - expected = soft_clip(im, sharpness_factor=1.0, minv=None, maxv=upper, dtype=torch.float32) - # the rtol is set to 5e-5 because the logaddexp function used in softplus is not stable accross torch and numpy + expected = test_soft_clip_func(im, None, 95) + # the rtol is set to 1e-4 because the logaddexp function used in softplus is not stable accross torch and numpy assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -82,9 +78,8 @@ def test_soft_clipping_one_sided_low(self, p): soft_clipper = ClipIntensityPercentilesd(keys=[key], upper=None, lower=5, sharpness_factor=1.0) im = p(self.imt) result = soft_clipper({key: im}) - lower = percentile(im, 5) - expected = soft_clip(im, sharpness_factor=1.0, minv=lower, maxv=None, dtype=torch.float32) - # the rtol is set to 1e-6 because the logaddexp function used in softplus is not stable accross torch and numpy + expected = test_soft_clip_func(im, 5, None) + # the rtol is set to 1e-4 because the logaddexp function used in softplus is not stable accross torch and numpy assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -93,7 +88,8 @@ def test_channel_wise(self, p): clipper = ClipIntensityPercentilesd(keys=[key], upper=95, lower=5, channel_wise=True) im = p(self.imt) result = clipper({key: im}) - for i, c in enumerate(im): + im_t = convert_to_tensor(self.imt) + for i, c in enumerate(im_t): lower, upper = percentile(c, (5, 95)) expected = clip(c, lower, upper) assert_allclose(result[key][i], p(expected), type_test="tensor", rtol=1e-3, atol=0) @@ -132,8 +128,7 @@ def test_hard_clipping_two_sided(self, p): hard_clipper = ClipIntensityPercentilesd(keys=[key], upper=95, lower=5) im = p(self.imt) result = hard_clipper({key: im}) - lower, upper = percentile(im, (5, 95)) - expected = clip(im, lower, upper) + expected = test_hard_clip_func(im, 5, 95) assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -142,8 +137,7 @@ def test_hard_clipping_one_sided_high(self, p): hard_clipper = ClipIntensityPercentilesd(keys=[key], upper=95, lower=None) im = p(self.imt) result = hard_clipper({key: im}) - lower, upper = percentile(im, (0, 95)) - expected = clip(im, lower, upper) + expected = test_hard_clip_func(im, 0, 95) assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -152,8 +146,7 @@ def test_hard_clipping_one_sided_low(self, p): hard_clipper = ClipIntensityPercentilesd(keys=[key], upper=None, lower=5) im = p(self.imt) result = hard_clipper({key: im}) - lower, upper = percentile(im, (5, 100)) - expected = clip(im, lower, upper) + expected = test_hard_clip_func(im, 5, 100) assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -162,8 +155,7 @@ def test_soft_clipping_two_sided(self, p): soft_clipper = ClipIntensityPercentilesd(keys=[key], upper=95, lower=5, sharpness_factor=1.0) im = p(self.imt) result = soft_clipper({key: im}) - lower, upper = percentile(im, (5, 95)) - expected = soft_clip(im, sharpness_factor=1.0, minv=lower, maxv=upper, dtype=torch.float32) + expected = test_soft_clip_func(im, 5, 95) # the rtol is set to 1e-4 because the logaddexp function used in softplus is not stable accross torch and numpy assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-4, atol=0) @@ -173,9 +165,8 @@ def test_soft_clipping_one_sided_high(self, p): soft_clipper = ClipIntensityPercentilesd(keys=[key], upper=95, lower=None, sharpness_factor=1.0) im = p(self.imt) result = soft_clipper({key: im}) - upper = percentile(im, 95) - expected = soft_clip(im, sharpness_factor=1.0, minv=None, maxv=upper, dtype=torch.float32) - # the rtol is set to 5e-5 because the logaddexp function used in softplus is not stable accross torch and numpy + expected = test_soft_clip_func(im, None, 95) + # the rtol is set to 1e-4 because the logaddexp function used in softplus is not stable accross torch and numpy assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -184,8 +175,7 @@ def test_soft_clipping_one_sided_low(self, p): soft_clipper = ClipIntensityPercentilesd(keys=[key], upper=None, lower=5, sharpness_factor=1.0) im = p(self.imt) result = soft_clipper({key: im}) - lower = percentile(im, 5) - expected = soft_clip(im, sharpness_factor=1.0, minv=lower, maxv=None, dtype=torch.float32) + expected = test_soft_clip_func(im, 5, None) # the rtol is set to 1e-6 because the logaddexp function used in softplus is not stable accross torch and numpy assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-4, atol=0) @@ -195,7 +185,8 @@ def test_channel_wise(self, p): clipper = ClipIntensityPercentilesd(keys=[key], upper=95, lower=5, channel_wise=True) im = p(self.imt) result = clipper({key: im}) - for i, c in enumerate(im): + im_t = convert_to_tensor(im) + for i, c in enumerate(im_t): lower, upper = percentile(c, (5, 95)) expected = clip(c, lower, upper) assert_allclose(result[key][i], p(expected), type_test="tensor", rtol=1e-4, atol=0)