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tests/transforms/test_clip_intensity_percentilesd.py

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+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+import unittest
+
+from parameterized import parameterized
+
+from monai.transforms import ClipIntensityPercentilesd
+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):
+
+    @parameterized.expand([[p] for p in TEST_NDARRAYS])
+    def test_hard_clipping_two_sided(self, p):
+        key = "img"
+        hard_clipper = ClipIntensityPercentilesd(keys=[key], upper=95, lower=5)
+        im = p(self.imt)
+        result = hard_clipper({key: im})
+        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])
+    def test_hard_clipping_one_sided_high(self, p):
+        key = "img"
+        hard_clipper = ClipIntensityPercentilesd(keys=[key], upper=95, lower=None)
+        im = p(self.imt)
+        result = hard_clipper({key: im})
+        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])
+    def test_hard_clipping_one_sided_low(self, p):
+        key = "img"
+        hard_clipper = ClipIntensityPercentilesd(keys=[key], upper=None, lower=5)
+        im = p(self.imt)
+        result = hard_clipper({key: im})
+        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])
+    def test_soft_clipping_two_sided(self, p):
+        key = "img"
+        soft_clipper = ClipIntensityPercentilesd(keys=[key], upper=95, lower=5, sharpness_factor=1.0)
+        im = p(self.imt)
+        result = soft_clipper({key: im})
+        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])
+    def test_soft_clipping_one_sided_high(self, p):
+        key = "img"
+        soft_clipper = ClipIntensityPercentilesd(keys=[key], upper=95, lower=None, sharpness_factor=1.0)
+        im = p(self.imt)
+        result = soft_clipper({key: im})
+        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])
+    def test_soft_clipping_one_sided_low(self, p):
+        key = "img"
+        soft_clipper = ClipIntensityPercentilesd(keys=[key], upper=None, lower=5, sharpness_factor=1.0)
+        im = p(self.imt)
+        result = soft_clipper({key: im})
+        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])
+    def test_channel_wise(self, p):
+        key = "img"
+        clipper = ClipIntensityPercentilesd(keys=[key], upper=95, lower=5, channel_wise=True)
+        im = p(self.imt)
+        result = clipper({key: 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)
+
+    def test_ill_sharpness_factor(self):
+        key = "img"
+        with self.assertRaises(ValueError):
+            ClipIntensityPercentilesd(keys=[key], upper=95, lower=5, sharpness_factor=0.0)
+
+    def test_ill_lower_percentile(self):
+        key = "img"
+        with self.assertRaises(ValueError):
+            ClipIntensityPercentilesd(keys=[key], upper=None, lower=-1)
+
+    def test_ill_upper_percentile(self):
+        key = "img"
+        with self.assertRaises(ValueError):
+            ClipIntensityPercentilesd(keys=[key], upper=101, lower=None)
+
+    def test_ill_percentiles(self):
+        key = "img"
+        with self.assertRaises(ValueError):
+            ClipIntensityPercentilesd(keys=[key], upper=95, lower=96)
+
+    def test_ill_both_none(self):
+        key = "img"
+        with self.assertRaises(ValueError):
+            ClipIntensityPercentilesd(keys=[key], upper=None, lower=None)
+
+
+class TestClipIntensityPercentilesd3D(NumpyImageTestCase3D):
+
+    @parameterized.expand([[p] for p in TEST_NDARRAYS])
+    def test_hard_clipping_two_sided(self, p):
+        key = "img"
+        hard_clipper = ClipIntensityPercentilesd(keys=[key], upper=95, lower=5)
+        im = p(self.imt)
+        result = hard_clipper({key: im})
+        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])
+    def test_hard_clipping_one_sided_high(self, p):
+        key = "img"
+        hard_clipper = ClipIntensityPercentilesd(keys=[key], upper=95, lower=None)
+        im = p(self.imt)
+        result = hard_clipper({key: im})
+        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])
+    def test_hard_clipping_one_sided_low(self, p):
+        key = "img"
+        hard_clipper = ClipIntensityPercentilesd(keys=[key], upper=None, lower=5)
+        im = p(self.imt)
+        result = hard_clipper({key: im})
+        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])
+    def test_soft_clipping_two_sided(self, p):
+        key = "img"
+        soft_clipper = ClipIntensityPercentilesd(keys=[key], upper=95, lower=5, sharpness_factor=1.0)
+        im = p(self.imt)
+        result = soft_clipper({key: im})
+        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])
+    def test_soft_clipping_one_sided_high(self, p):
+        key = "img"
+        soft_clipper = ClipIntensityPercentilesd(keys=[key], upper=95, lower=None, sharpness_factor=1.0)
+        im = p(self.imt)
+        result = soft_clipper({key: im})
+        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])
+    def test_soft_clipping_one_sided_low(self, p):
+        key = "img"
+        soft_clipper = ClipIntensityPercentilesd(keys=[key], upper=None, lower=5, sharpness_factor=1.0)
+        im = p(self.imt)
+        result = soft_clipper({key: im})
+        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)
+
+    @parameterized.expand([[p] for p in TEST_NDARRAYS])
+    def test_channel_wise(self, p):
+        key = "img"
+        clipper = ClipIntensityPercentilesd(keys=[key], upper=95, lower=5, channel_wise=True)
+        im = p(self.imt)
+        result = clipper({key: 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)
+
+
+if __name__ == "__main__":
+    unittest.main()