SlidingWindowInferer: option to adaptively stitch in cpu memory for large images#5297
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Nic-Ma merged 9 commits intoProject-MONAI:devfrom Oct 13, 2022
myron:sliding
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SlidingWindowInferer: option to adaptively stitch in cpu memory for large images#5297Nic-Ma merged 9 commits intoProject-MONAI:devfrom myron:sliding
Nic-Ma merged 9 commits intoProject-MONAI:devfrom
myron:sliding
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Signed-off-by: myron <[email protected]>
Nic-Ma
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Signed-off-by: myron <[email protected]>
Signed-off-by: myron <[email protected]>
Nic-Ma
reviewed
Oct 13, 2022
Signed-off-by: myron <[email protected]>
Signed-off-by: myron <[email protected]>
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wyli
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Oct 15, 2022
…arge images (Project-MONAI#5297) SlidingWindowInferer: option to adaptively stitch in cpu memory for large images. This adds an option to provide maximum input image volume (number of elements) to dynamically change stitching to cpu memory (to avoid gpu memory crashes). For example with `cpu_thresh=400*400*400`, all input images with large volume will be stitched on cpu. At the moment, a user must decide beforehand, to stitch ALL images on cpu or gpu (by specifying the 'device' parameter). But in many datasets, only a few large images require device==cpu, and running inference on cpu for ALL will be unnecessary slow. It's related to Project-MONAI#4625 Project-MONAI#4495 Project-MONAI#3497 Project-MONAI#4726 Project-MONAI#4588 ### Types of changes <!--- Put an `x` in all the boxes that apply, and remove the not applicable items --> - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. Signed-off-by: myron <[email protected]> Co-authored-by: Wenqi Li <[email protected]>
bhashemian
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Oct 20, 2022
…arge images (Project-MONAI#5297) SlidingWindowInferer: option to adaptively stitch in cpu memory for large images. This adds an option to provide maximum input image volume (number of elements) to dynamically change stitching to cpu memory (to avoid gpu memory crashes). For example with `cpu_thresh=400*400*400`, all input images with large volume will be stitched on cpu. At the moment, a user must decide beforehand, to stitch ALL images on cpu or gpu (by specifying the 'device' parameter). But in many datasets, only a few large images require device==cpu, and running inference on cpu for ALL will be unnecessary slow. It's related to Project-MONAI#4625 Project-MONAI#4495 Project-MONAI#3497 Project-MONAI#4726 Project-MONAI#4588 ### Types of changes <!--- Put an `x` in all the boxes that apply, and remove the not applicable items --> - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. Signed-off-by: myron <[email protected]> Co-authored-by: Wenqi Li <[email protected]> Signed-off-by: Behrooz <[email protected]>
KumoLiu
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Nov 2, 2022
…arge images (#5297) SlidingWindowInferer: option to adaptively stitch in cpu memory for large images. This adds an option to provide maximum input image volume (number of elements) to dynamically change stitching to cpu memory (to avoid gpu memory crashes). For example with `cpu_thresh=400*400*400`, all input images with large volume will be stitched on cpu. At the moment, a user must decide beforehand, to stitch ALL images on cpu or gpu (by specifying the 'device' parameter). But in many datasets, only a few large images require device==cpu, and running inference on cpu for ALL will be unnecessary slow. It's related to #4625 #4495 #3497 #4726 #4588 ### Types of changes <!--- Put an `x` in all the boxes that apply, and remove the not applicable items --> - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. Signed-off-by: myron <[email protected]> Co-authored-by: Wenqi Li <[email protected]> Signed-off-by: KumoLiu <[email protected]>
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SlidingWindowInferer: option to adaptively stitch in cpu memory for large images.
This adds an option to provide maximum input image volume (number of elements) to dynamically change stitching to cpu memory (to avoid gpu memory crashes). For example with
cpu_thresh=400*400*400, all input images with large volume will be stitched on cpu.At the moment, a user must decide beforehand, to stitch ALL images on cpu or gpu (by specifying the 'device' parameter). But in many datasets, only a few large images require device==cpu, and running inference on cpu for ALL will be unnecessary slow.
It's related to
#4625
#4495
#3497
#4726
#4588
Types of changes
./runtests.sh -f -u --net --coverage../runtests.sh --quick --unittests --disttests.make htmlcommand in thedocs/folder.