kohr-h opened a new pull request #13143: [Python] Support NDArray indexing with None and Ellipsis URL: https://github.com/apache/incubator-mxnet/pull/13143 ## Description ## This PR adds the capability of indexing `NDArray` with `None` to create new axes, and with `...` (`Ellipsis`) as a shorthand for an adequate number of `:` (`slice(None)`). ## Checklist ## ### Essentials ### Please feel free to remove inapplicable items for your PR. - [ ] The PR title starts with [MXNET-$JIRA_ID], where $JIRA_ID refers to the relevant [JIRA issue](https://issues.apache.org/jira/projects/MXNET/issues) created (except PRs with tiny changes) - [x] Changes are complete (i.e. I finished coding on this PR) - [ ] All changes have test coverage: - Unit tests are added for small changes to verify correctness (e.g. adding a new operator) - Nightly tests are added for complicated/long-running ones (e.g. changing distributed kvstore) - Build tests will be added for build configuration changes (e.g. adding a new build option with NCCL) - [ ] Code is well-documented: - For user-facing API changes, API doc string has been updated. - Check the API doc at http://mxnet-ci-doc.s3-accelerate.dualstack.amazonaws.com/PR-$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 ### - `NDArray.__getitem__` and `NDArray.__setitem__` now first expand `None` and `Ellipsis` entries in indices. - `__setitem__` ignores `None` entries, which corresponds to the behavior of NumPy. ## Comments ## - I've noted that indexing is not as copy-free as it could be. For instance: ```py >>> x = nd.ones(3) >>> y = x[:2] # same as y = x[slice(2)] >>> y[:] = 0 >>> x # as expected [0. 0. 1.] <NDArray 3 @cpu(0)> >>> y = x[(slice(2),)] # Should be equivalent >>> y[:] = -17 >>> x # unchanged! [0. 0. 1.] <NDArray 3 @cpu(0)> ``` - Another nifty case is this one: ```py >>> x = nd.ones((2, 3)) >>> y = x[:2] >>> y[:] = 0 >>> x # as expected [[0. 0. 0.] [1. 1. 1.]] <NDArray 2x3 @cpu(0)> >>> y = x[:2, :] >>> y[:] = -17 >>> x # unchanged! [[0. 0. 0.] [1. 1. 1.]] <NDArray 2x3 @cpu(0)> ``` I think that the implementation does not live up to the promise of the documentation: ``` Returns a sliced view of this array if the elements fetched are contiguous in memory; otherwise, returns a newly created NDArray. ``` In particular, the "detection" of contiguousness is not good. I'll make that an extra issue, but it's related to this PR, so I mention it here.
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