SlidingWindowInferer: option to adaptively stitch in cpu memory for large images#5297
Merged
Conversation
Signed-off-by: myron <amyronenko@nvidia.com>
Nic-Ma
reviewed
Oct 9, 2022
Signed-off-by: myron <amyronenko@nvidia.com>
Nic-Ma
reviewed
Oct 13, 2022
Signed-off-by: myron <amyronenko@nvidia.com>
Signed-off-by: myron <amyronenko@nvidia.com>
Nic-Ma
approved these changes
Oct 13, 2022
Contributor
|
/black |
Contributor
|
/build |
Contributor
|
/build |
wyli
added a commit
to wyli/MONAI
that referenced
this pull request
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 <amyronenko@nvidia.com> Co-authored-by: Wenqi Li <831580+wyli@users.noreply.github.com>
bhashemian
pushed a commit
to JHancox/MONAI
that referenced
this pull request
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 <amyronenko@nvidia.com> Co-authored-by: Wenqi Li <831580+wyli@users.noreply.github.com> Signed-off-by: Behrooz <3968947+drbeh@users.noreply.github.com>
KumoLiu
pushed a commit
that referenced
this pull request
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 <amyronenko@nvidia.com> Co-authored-by: Wenqi Li <831580+wyli@users.noreply.github.com> Signed-off-by: KumoLiu <yunl@nvidia.com>
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
5 participants
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
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.