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Fix test error in test_soft_clipping_one_sided_high #7624

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Merged
KumoLiu merged 3 commits into from
Apr 11, 2024
Merged

Fix test error in test_soft_clipping_one_sided_high #7624

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7a25c3a
fix #7616
KumoLiu Apr 11, 2024
4d72758
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Apr 11, 2024
9a468f0
fix dtype
KumoLiu Apr 11, 2024
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60 changes: 30 additions & 30 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 @@ -12,12 +12,12 @@

import unittest

import numpy as np
import torch
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 monai.transforms.utils_pytorch_numpy_unification import clip, percentile
from tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, NumpyImageTestCase3D, assert_allclose


Expand All @@ -28,35 +28,35 @@ 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)
lower, upper = percentile(im, (5, 95))
expected = clip(im, 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)
lower, upper = percentile(im, (0, 95))
expected = clip(im, 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)
lower, upper = percentile(im, (5, 100))
expected = clip(im, 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)
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
assert_allclose(result, p(expected), type_test="tensor", rtol=1e-6, atol=0)

Expand All @@ -65,8 +65,8 @@ 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 = np.percentile(self.imt, 95)
expected = soft_clip(self.imt, sharpness_factor=1.0, minv=None, maxv=upper)
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
assert_allclose(result, p(expected), type_test="tensor", rtol=5e-5, atol=0)

Expand All @@ -75,8 +75,8 @@ 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 = np.percentile(self.imt, 5)
expected = soft_clip(self.imt, sharpness_factor=1.0, minv=lower, maxv=None)
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
assert_allclose(result, p(expected), type_test="tensor", rtol=1e-6, atol=0)

Expand All @@ -85,8 +85,8 @@ 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(self.imt):
lower, upper = np.percentile(c, (5, 95))
for i, c in enumerate(im):
lower, upper = percentile(c, (5, 95))
expected = clip(c, lower, upper)
assert_allclose(result[i], p(expected), type_test="tensor", rtol=1e-7, atol=0)

Expand Down Expand Up @@ -118,35 +118,35 @@ 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)
lower, upper = percentile(im, (5, 95))
expected = clip(im, 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)
lower, upper = percentile(im, (0, 95))
expected = clip(im, 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)
lower, upper = percentile(im, (5, 100))
expected = clip(im, 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)
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
assert_allclose(result, p(expected), type_test="tensor", rtol=1e-6, atol=0)

Expand All @@ -155,8 +155,8 @@ 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 = np.percentile(self.imt, 95)
expected = soft_clip(self.imt, sharpness_factor=1.0, minv=None, maxv=upper)
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
assert_allclose(result, p(expected), type_test="tensor", rtol=5e-5, atol=0)

Expand All @@ -165,8 +165,8 @@ 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 = np.percentile(self.imt, 5)
expected = soft_clip(self.imt, sharpness_factor=1.0, minv=lower, maxv=None)
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
assert_allclose(result, p(expected), type_test="tensor", rtol=1e-6, atol=0)

Expand All @@ -175,8 +175,8 @@ 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(self.imt):
lower, upper = np.percentile(c, (5, 95))
for i, c in enumerate(im):
lower, upper = percentile(c, (5, 95))
expected = clip(c, lower, upper)
assert_allclose(result[i], p(expected), type_test="tensor", rtol=1e-7, atol=0)

Expand Down
60 changes: 30 additions & 30 deletions tests/test_clip_intensity_percentilesd.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -13,12 +13,12 @@

import unittest

import numpy as np
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
from monai.transforms.utils_pytorch_numpy_unification import clip, percentile
from tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, NumpyImageTestCase3D, assert_allclose


Expand All @@ -30,8 +30,8 @@ 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 = np.percentile(self.imt, (5, 95))
expected = clip(self.imt, lower, upper)
lower, upper = percentile(im, (5, 95))
expected = clip(im, lower, upper)
assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-7, atol=0)

@parameterized.expand([[p] for p in TEST_NDARRAYS])
Expand All @@ -40,8 +40,8 @@ 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 = np.percentile(self.imt, (0, 95))
expected = clip(self.imt, lower, upper)
lower, upper = percentile(im, (0, 95))
expected = clip(im, lower, upper)
assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-7, atol=0)

@parameterized.expand([[p] for p in TEST_NDARRAYS])
Expand All @@ -50,8 +50,8 @@ 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 = np.percentile(self.imt, (5, 100))
expected = clip(self.imt, lower, upper)
lower, upper = percentile(im, (5, 100))
expected = clip(im, lower, upper)
assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-7, atol=0)

@parameterized.expand([[p] for p in TEST_NDARRAYS])
Expand All @@ -60,8 +60,8 @@ 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 = np.percentile(self.imt, (5, 95))
expected = soft_clip(self.imt, sharpness_factor=1.0, minv=lower, maxv=upper)
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
assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-6, atol=0)

Expand All @@ -71,8 +71,8 @@ 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 = np.percentile(self.imt, 95)
expected = soft_clip(self.imt, sharpness_factor=1.0, minv=None, maxv=upper)
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
assert_allclose(result[key], p(expected), type_test="tensor", rtol=5e-5, atol=0)

Expand All @@ -82,8 +82,8 @@ 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 = np.percentile(self.imt, 5)
expected = soft_clip(self.imt, sharpness_factor=1.0, minv=lower, maxv=None)
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
assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-6, atol=0)

Expand All @@ -93,8 +93,8 @@ 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(self.imt):
lower, upper = np.percentile(c, (5, 95))
for i, c in enumerate(im):
lower, upper = percentile(c, (5, 95))
expected = clip(c, lower, upper)
assert_allclose(result[key][i], p(expected), type_test="tensor", rtol=1e-7, atol=0)

Expand Down Expand Up @@ -132,8 +132,8 @@ 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 = np.percentile(self.imt, (5, 95))
expected = clip(self.imt, lower, upper)
lower, upper = percentile(im, (5, 95))
expected = clip(im, lower, upper)
assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-7, atol=0)

@parameterized.expand([[p] for p in TEST_NDARRAYS])
Expand All @@ -142,8 +142,8 @@ 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 = np.percentile(self.imt, (0, 95))
expected = clip(self.imt, lower, upper)
lower, upper = percentile(im, (0, 95))
expected = clip(im, lower, upper)
assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-7, atol=0)

@parameterized.expand([[p] for p in TEST_NDARRAYS])
Expand All @@ -152,8 +152,8 @@ 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 = np.percentile(self.imt, (5, 100))
expected = clip(self.imt, lower, upper)
lower, upper = percentile(im, (5, 100))
expected = clip(im, lower, upper)
assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-7, atol=0)

@parameterized.expand([[p] for p in TEST_NDARRAYS])
Expand All @@ -162,8 +162,8 @@ 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 = np.percentile(self.imt, (5, 95))
expected = soft_clip(self.imt, sharpness_factor=1.0, minv=lower, maxv=upper)
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
assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-6, atol=0)

Expand All @@ -173,8 +173,8 @@ 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 = np.percentile(self.imt, 95)
expected = soft_clip(self.imt, sharpness_factor=1.0, minv=None, maxv=upper)
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
assert_allclose(result[key], p(expected), type_test="tensor", rtol=5e-5, atol=0)

Expand All @@ -184,8 +184,8 @@ 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 = np.percentile(self.imt, 5)
expected = soft_clip(self.imt, sharpness_factor=1.0, minv=lower, maxv=None)
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
assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-6, atol=0)

Expand All @@ -195,8 +195,8 @@ 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(self.imt):
lower, upper = np.percentile(c, (5, 95))
for i, c in enumerate(im):
lower, upper = percentile(c, (5, 95))
expected = clip(c, lower, upper)
assert_allclose(result[key][i], p(expected), type_test="tensor", rtol=1e-7, atol=0)

Expand Down