sandeep-krishnamurthy opened a new pull request #13802: Image normalize operator - GPU support, 3D/4D inputs URL: https://github.com/apache/incubator-mxnet/pull/13802 ## Description ## 1. Make normalize as operator. No contribution to gradient. 2. Support CPU and GPU. This PR adds GPU supports and re-organizes previously supported CPU. 3. Normalize transformation operator can take 3D (c, h, w) or 4D (n, c, h, w) at once. So that a batch of input can be passed in the transformation. 4. Parallelization with OMP 5. This is backward compatible change. No existing functionality will be affected. # Background We can enable users to fuse transformation operators with network graph and export it. This end to end network with transforms + network will greatly simplify inference deployment. ## Checklist ## ### Essentials ### Please feel free to remove inapplicable items for your PR. - [X] Changes are complete (i.e. I finished coding on this PR) - [X] All changes have test coverage: - Unit tests are added for small changes to verify correctness (e.g. adding a new operator) - Nightly tests are added for complicated/long-running ones (e.g. changing distributed kvstore) - Build tests will be added for build configuration changes (e.g. adding a new build option with NCCL) - [X] Code is well-documented: - For user-facing API changes, API doc string has been updated. - For new C++ functions in header files, their functionalities and arguments are documented. - Check the API doc at http://mxnet-ci-doc.s3-accelerate.dualstack.amazonaws.com/PR-$PR_ID/$BUILD_ID/index.html - [X] To the my best knowledge, examples are either not affected by this change, or have been fixed to be compatible with this change ### Changes ### - Make Normalize to accept 3D or 4D input - CPU / GPU support - No gradient contribution - Reorganize like any other operators ## Comments ## - A related PR was raised here https://github.com/apache/incubator-mxnet/pull/13614 reviewed by @apeforest and @stu1130 - I have addressed those comments. I am closing other PR as it was mixing multiple transforms and functionality. @apeforest @stu1130 @zhreshold @nswamy - For review
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