[GitHub] samskalicky opened a new pull request #12067: Fix for issue #11733

[GitBox <gi...@apache.org>]

samskalicky opened a new pull request #12067: Fix for issue #11733
URL: https://github.com/apache/incubator-mxnet/pull/12067
 
 
   ## Description ##
   Fix for issue #11733 
   Problem was that the random input data was cast from higher precision to lower precision and then results between different precisions compared. Fix was to start with lower precision data and cast to higher precision data so that all precisions compute on the same data.
   
   ## Checklist ##
   ### Essentials ###
   Please feel free to remove inapplicable items for your PR.
   - [ ] The PR title starts with [MXNET-$JIRA_ID], where $JIRA_ID refers to the relevant [JIRA issue](https://issues.apache.org/jira/projects/MXNET/issues) created (except PRs with tiny changes)
   - [X] Changes are complete (i.e. I finished coding on this PR)
   - [X] All changes have test coverage:
   - Unit tests are added for small changes to verify correctness (e.g. adding a new operator)
   - Nightly tests are added for complicated/long-running ones (e.g. changing distributed kvstore)
   - Build tests will be added for build configuration changes (e.g. adding a new build option with NCCL)
   - [X] Code is well-documented: 
   - For user-facing API changes, API doc string has been updated. 
   - For new C++ functions in header files, their functionalities and arguments are documented. 
   - For new examples, README.md is added to explain the what the example does, the source of the dataset, expected performance on test set and reference to the original paper if applicable
   - Check the API doc at http://mxnet-ci-doc.s3-accelerate.dualstack.amazonaws.com/PR-$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 ###
   added support to check_consistency function to generate random numbers for a specific datatype (ie. fp16)
   
   this ensures that for tests that compare results among different precisions, that data is generated in the least precise type and casted to the most precise
   
   changed test_pooling_with_type test case to specify fp16 precision for random input data
   renamed the 2nd test_pooling_with_type function to test_pooling_with_type2 so it doesnt redefine the first and both are tested
   
   fixed equation formatting issue in pooling operator description
   
   Added myself to the contributors readme file
   
   ## Comments ##
   
   

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