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feat: add channel_wise option for ShiftIntensity #6789

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SauravMaheshkar wants to merge 5 commits into from
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feat: add channel_wise option for ShiftIntensity #6789

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6f2b6c6
feat: add channel_wise to ShiftIntensity
SauravMaheshkar Jul 27, 2023
ce343e8
style: add ignore flag
SauravMaheshkar Jul 27, 2023
256139c
Merge branch 'dev' into saurav/channel-wise
SauravMaheshkar Jul 28, 2023
b489aab
Merge branch 'dev' into saurav/channel-wise
SauravMaheshkar Aug 8, 2023
52d5de0
Merge branch 'dev' into saurav/channel-wise
SauravMaheshkar Aug 20, 2023
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25 changes: 18 additions & 7 deletions monai/transforms/intensity/array.py
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Original file line number Diff line number Diff line change
Expand Up @@ -227,13 +227,15 @@ class ShiftIntensity(Transform):
offset: offset value to shift the intensity of image.
safe: if `True`, then do safe dtype convert when intensity overflow. default to `False`.
E.g., `[256, -12]` -> `[array(0), array(244)]`. If `True`, then `[256, -12]` -> `[array(255), array(0)]`.
channel_wise: if `True`, shift the intensity for each channel of image with random offset
"""

backend = [TransformBackends.TORCH, TransformBackends.NUMPY]

def __init__(self, offset: float, safe: bool = False) -> None:
def __init__(self, offset: float, safe: bool = False, channel_wise: bool = False) -> None:
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@wyli wyli Jul 27, 2023

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Thank you, please also update the corresponding APIs in https://github.com/Project-MONAI/MONAI/blob/dev/monai/transforms/intensity/dictionary.py and add test cases to the existing unit tests such as https://github.com/Project-MONAI/MONAI/blob/dev/tests/test_shift_intensity.py

self.offset = offset
self.safe = safe
self.channel_wise = channel_wise

def __call__(self, img: NdarrayOrTensor, offset: float | None = None) -> NdarrayOrTensor:
"""
Expand All @@ -242,10 +244,16 @@ def __call__(self, img: NdarrayOrTensor, offset: float | None = None) -> Ndarray

img = convert_to_tensor(img, track_meta=get_track_meta())
offset = self.offset if offset is None else offset
out = img + offset
out, *_ = convert_data_type(data=out, dtype=img.dtype, safe=self.safe)

return out
if self.channel_wise:
for i, d in enumerate(img):
out = d + offset
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@KumoLiu KumoLiu Jul 28, 2023

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Thanks, but if you don't randomize offset here, the output won't make any difference since offset is a constant.

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@SauravMaheshkar SauravMaheshkar Jul 28, 2023 •
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Ahh yes good catch, any suggestions on how I do this ?

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@KumoLiu KumoLiu Jul 28, 2023

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Hi @SauravMaheshkar, I would suggest you refer to this PR.
Thanks!

out, *_ = convert_data_type(data=out, dtype=d.dtype, safe=self.safe)
img[i] = out
return img
else:
out = img + offset
out, *_ = convert_data_type(data=out, dtype=img.dtype, safe=self.safe)
return out # type: ignore


class RandShiftIntensity(RandomizableTransform):
Expand All @@ -255,13 +263,16 @@ class RandShiftIntensity(RandomizableTransform):

backend = [TransformBackends.TORCH, TransformBackends.NUMPY]

def __init__(self, offsets: tuple[float, float] | float, safe: bool = False, prob: float = 0.1) -> None:
def __init__(
self, offsets: tuple[float, float] | float, safe: bool = False, channel_wise: bool = False, prob: float = 0.1
) -> None:
"""
Args:
offsets: offset range to randomly shift.
if single number, offset value is picked from (-offsets, offsets).
safe: if `True`, then do safe dtype convert when intensity overflow. default to `False`.
E.g., `[256, -12]` -> `[array(0), array(244)]`. If `True`, then `[256, -12]` -> `[array(255), array(0)]`.
channel_wise: if `True`, shift the intensity for each channel of image with random offset
prob: probability of shift.
"""
RandomizableTransform.__init__(self, prob)
Expand All @@ -272,7 +283,7 @@ def __init__(self, offsets: tuple[float, float] | float, safe: bool = False, pro
else:
self.offsets = (min(offsets), max(offsets))
self._offset = self.offsets[0]
self._shifter = ShiftIntensity(self._offset, safe)
self._shifter = ShiftIntensity(self._offset, safe, channel_wise)

def randomize(self, data: Any | None = None) -> None:
super().randomize(None)
Expand Down