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test_clip_intensity_percentiles.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
import numpy as np
from parameterized import parameterized
from monai.transforms import ClipIntensityPercentiles
from monai.transforms.utils import soft_clip
from monai.transforms.utils_pytorch_numpy_unification import clip
from tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, NumpyImageTestCase3D, assert_allclose
class TestClipIntensityPercentiles2D(NumpyImageTestCase2D):
@parameterized.expand([[p] for p in TEST_NDARRAYS])
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 = np.percentile(self.imt, (5, 95))
expected = clip(self.imt, lower, upper)
assert_allclose(result, p(expected), type_test="tensor", rtol=1e-7, atol=0)
@parameterized.expand([[p] for p in TEST_NDARRAYS])
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 = np.percentile(self.imt, (0, 95))
expected = clip(self.imt, lower, upper)
assert_allclose(result, p(expected), type_test="tensor", rtol=1e-7, atol=0)
@parameterized.expand([[p] for p in TEST_NDARRAYS])
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 = np.percentile(self.imt, (5, 100))
expected = clip(self.imt, lower, upper)
assert_allclose(result, p(expected), type_test="tensor", rtol=1e-7, atol=0)
@parameterized.expand([[p] for p in TEST_NDARRAYS])
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 = np.percentile(self.imt, (5, 95))
expected = soft_clip(self.imt, sharpness_factor=1.0, minv=lower, maxv=upper)
# the rtol is set to 1e-6 because the logaddexp function used in softplus is not stable accross torch and numpy
assert_allclose(result, p(expected), type_test="tensor", rtol=1e-6, atol=0)
@parameterized.expand([[p] for p in TEST_NDARRAYS])
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 = np.percentile(self.imt, 95)
expected = soft_clip(self.imt, sharpness_factor=1.0, minv=None, maxv=upper)
# the rtol is set to 5e-5 because the logaddexp function used in softplus is not stable accross torch and numpy
assert_allclose(result, p(expected), type_test="tensor", rtol=5e-5, atol=0)
@parameterized.expand([[p] for p in TEST_NDARRAYS])
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 = np.percentile(self.imt, 5)
expected = soft_clip(self.imt, sharpness_factor=1.0, minv=lower, maxv=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, p(expected), type_test="tensor", rtol=1e-6, atol=0)
@parameterized.expand([[p] for p in TEST_NDARRAYS])
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(self.imt):
lower, upper = np.percentile(c, (5, 95))
expected = clip(c, lower, upper)
assert_allclose(result[i], p(expected), type_test="tensor", rtol=1e-7, atol=0)
def test_ill_sharpness_factor(self):
with self.assertRaises(ValueError):
ClipIntensityPercentiles(upper=95, lower=5, sharpness_factor=0.0)
def test_ill_lower_percentile(self):
with self.assertRaises(ValueError):
ClipIntensityPercentiles(upper=None, lower=-1)
def test_ill_upper_percentile(self):
with self.assertRaises(ValueError):
ClipIntensityPercentiles(upper=101, lower=None)
def test_ill_percentiles(self):
with self.assertRaises(ValueError):
ClipIntensityPercentiles(upper=95, lower=96)
def test_ill_both_none(self):
with self.assertRaises(ValueError):
ClipIntensityPercentiles(upper=None, lower=None)
class TestClipIntensityPercentiles3D(NumpyImageTestCase3D):
@parameterized.expand([[p] for p in TEST_NDARRAYS])
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 = np.percentile(self.imt, (5, 95))
expected = clip(self.imt, lower, upper)
assert_allclose(result, p(expected), type_test="tensor", rtol=1e-7, atol=0)
@parameterized.expand([[p] for p in TEST_NDARRAYS])
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 = np.percentile(self.imt, (0, 95))
expected = clip(self.imt, lower, upper)
assert_allclose(result, p(expected), type_test="tensor", rtol=1e-7, atol=0)
@parameterized.expand([[p] for p in TEST_NDARRAYS])
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 = np.percentile(self.imt, (5, 100))
expected = clip(self.imt, lower, upper)
assert_allclose(result, p(expected), type_test="tensor", rtol=1e-7, atol=0)
@parameterized.expand([[p] for p in TEST_NDARRAYS])
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 = np.percentile(self.imt, (5, 95))
expected = soft_clip(self.imt, sharpness_factor=1.0, minv=lower, maxv=upper)
# the rtol is set to 1e-6 because the logaddexp function used in softplus is not stable accross torch and numpy
assert_allclose(result, p(expected), type_test="tensor", rtol=1e-6, atol=0)
@parameterized.expand([[p] for p in TEST_NDARRAYS])
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 = np.percentile(self.imt, 95)
expected = soft_clip(self.imt, sharpness_factor=1.0, minv=None, maxv=upper)
# the rtol is set to 5e-5 because the logaddexp function used in softplus is not stable accross torch and numpy
assert_allclose(result, p(expected), type_test="tensor", rtol=5e-5, atol=0)
@parameterized.expand([[p] for p in TEST_NDARRAYS])
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 = np.percentile(self.imt, 5)
expected = soft_clip(self.imt, sharpness_factor=1.0, minv=lower, maxv=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, p(expected), type_test="tensor", rtol=1e-6, atol=0)
@parameterized.expand([[p] for p in TEST_NDARRAYS])
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(self.imt):
lower, upper = np.percentile(c, (5, 95))
expected = clip(c, lower, upper)
assert_allclose(result[i], p(expected), type_test="tensor", rtol=1e-7, atol=0)
if __name__ == "__main__":
unittest.main()