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Fix precision issue in TestClipIntensityPercentiles3D #7808

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merged 7 commits into from
May 28, 2024
Merged

Fix precision issue in TestClipIntensityPercentiles3D #7808

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dcfeb59
fix #7797
KumoLiu May 27, 2024
54923ef
Merge branch 'dev' of https://github.com/Project-MONAI/MONAI into clip
KumoLiu May 27, 2024
34d6230
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] May 27, 2024
9ebf062
fix flake8
KumoLiu May 27, 2024
4240215
minor fix
KumoLiu May 27, 2024
8162b38
fix flake8
KumoLiu May 27, 2024
695c0f5
Merge branch 'dev' into clip
KumoLiu May 27, 2024
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75 changes: 44 additions & 31 deletions tests/test_clip_intensity_percentiles.py
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -18,74 +18,92 @@
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])
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])
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])
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])
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])
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])
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])
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)
Expand Down Expand Up @@ -118,35 +136,31 @@ 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])
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])
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])
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)

Expand All @@ -155,27 +169,26 @@ 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])
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])
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)
Expand Down
55 changes: 23 additions & 32 deletions tests/test_clip_intensity_percentilesd.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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):

Expand All @@ -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])
Expand All @@ -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])
Expand All @@ -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])
Expand All @@ -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])
Expand All @@ -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])
Expand All @@ -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])
Expand All @@ -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)
Expand Down Expand Up @@ -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])
Expand All @@ -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])
Expand All @@ -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])
Expand All @@ -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)

Expand All @@ -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])
Expand All @@ -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)

Expand All @@ -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)
Expand Down
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