Summary:
Removed an unused function that caused compile errors in some configurations.
Added contrib function (exhaustive_search.knn) to compute the k nearest neighbors without constructing an index.
Renamed the equivalent GPU function as exhaustive_search.knn_gpu (it does not make much sense to mention numpy in the name as all functions take numpy arguments by default).
Reviewed By: beauby
Differential Revision: D24215427
fbshipit-source-id: 6d8e1eafa7c57593304b7b76f83b3015e4d2a2bb
Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/1445
As requested in https://github.com/facebookresearch/faiss/issues/1304, `bfKnn` can now produce int32 indices for output.
The native kernels themselves for brute-force kNN only operate on int32 indices in any case, so this is faster.
Also added a SWIG definition for float16 numpy arrays. As there is not a native half type, the reverse definition is undefined, so this is only really used for taking float16 data (e.g., from numpy) as input when in Python.
Added a `knn_numpy_gpu` wrapper as well that handles calling the `bfKnn` GPU implementation using CPU numpy arrays. This handles transposition and f32/f16/i32 data types as needed.
Reviewed By: mdouze
Differential Revision: D24152296
fbshipit-source-id: caa7daea23438cf26aa248e380f0dab2b6b907fd
