Implementation of intensity clipping transform: bot hard and soft approaches #7535
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… soft clipping approaches Signed-off-by: Lucas Robinet <robinet.lucas@iuct-oncopole.fr>
Signed-off-by: Lucas Robinet <robinet.lucas@iuct-oncopole.fr>
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Thanks for the PR, overall looks good to me.
Signed-off-by: Lucas Robinet <robinet.lucas@iuct-oncopole.fr>
… percentile-clipper
Signed-off-by: Lucas Robinet <robinet.lucas@iuct-oncopole.fr>
Signed-off-by: Lucas Robinet <robinet.lucas@iuct-oncopole.fr>
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Signed-off-by: Lucas Robinet <robinet.lucas@iuct-oncopole.fr>
… percentile-clipper
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Signed-off-by: Lucas Robinet <robinet.lucas@iuct-oncopole.fr>
Signed-off-by: Lucas Robinet <robinet.lucas@iuct-oncopole.fr>
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…ames for ClipIntensityPercentiles class Signed-off-by: Lucas Robinet <robinet.lucas@iuct-oncopole.fr>
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…lues Signed-off-by: Lucas Robinet <robinet.lucas@iuct-oncopole.fr>
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Fixes Issue #7512.
Description
Addition of a transformation allowing values above or below a certain percentile to be clipped.
Clipping can be hard or soft.
With soft clipping, the function remains derivable and the order of the values is respected, with smoother corners.
The soft clipping function is based on this medium article https://medium.com/life-at-hopper/clip-it-clip-it-good-1f1bf711b291
It's important to note that I've chosen to switch from Nones values to percentiles to take account of the fact that soft clipping can be one-sided or two-sided.
In fact, providing percentiles of 100 or 0 doesn't change anything in the case of hard clipping, but it does in the case of soft clipping because the function is smoothed. Hence the interest in introducing the possibility of putting None to avoid smoothing the function on one side or the other.
To implement this we had to define a
softplusfunction inmonai.transforms.utils_pytorch_numpy_unification.py. One of the problems is thatnp.logaddexpdo not exactly yields same outputs astorch.logaddexp. I've left it as is and lowered the tolerance of the tests slightly, but it's possible to force the conversion to numpy and then switch back to torch to ensure better unification between the frameworks.I've also added the
soft_clipfunction inmonai.transforms.utils.pywith the associated unit tests to ensure that the transformation works properly.Types of changes
./runtests.sh -f -u --net --coverage../runtests.sh --quick --unittests --disttests.make htmlcommand in thedocs/folder.