Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/2860
Optimized range search function where the GPU computes by default and falls back on gpu for queries where there are too many results.
Parallelize the CPU to GPU cloning, it seems to work.
Support range_search_preassigned in Python
Fix long-standing issue with SWIG exposed functions that did not release the GIL (in particular the MapLong2Long).
Adds a MapInt64ToInt64 that is more efficient than MapLong2Long.
Reviewed By: algoriddle
Differential Revision: D45672301
fbshipit-source-id: 2e77397c40083818584dbafa5427149359a2abfd
Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/2781
This is a benchmarking script for keypoint matching with labelled ground-truth.
Reviewed By: alexanderguzhva
Differential Revision: D44036091
fbshipit-source-id: d9d7c089c4d172b66f33dc968c00713a1b79c2d1
Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/2582
A few more or less cosmetic improvements
* Index::idx_t was in the Index object, which does not make much sense, this diff moves it to faiss::idx_t
* replace multiprocessing.dummy with multiprocessing.pool
* add Alexandr as a core contributor of Faiss in the README ;-)
```
for i in $( find . -name \*.cu -o -name \*.cuh -o -name \*.h -o -name \*.cpp ) ; do
sed -i s/Index::idx_t/idx_t/ $i
done
```
For the fbcode deps:
```
for i in $( fbgs Index::idx_t --exclude fbcode/faiss -l ) ; do
sed -i s/Index::idx_t/idx_t/ $i
done
```
Reviewed By: algoriddle
Differential Revision: D41437507
fbshipit-source-id: 8300f2a3ae97cace6172f3f14a9be3a83999fb89
Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/1972
This fixes a few issues that I ran into + adds tests:
- range_search_max_results with IP search
- a few missing downcasts for VectorTRansforms
- ResultHeap supports max IP search
Reviewed By: wickedfoo
Differential Revision: D29525093
fbshipit-source-id: d4ff0aff1d83af9717ff1aaa2fe3cda7b53019a3
Summary:
Added a few functions in contrib to:
- run range searches by batches on the query or the database side
- emulate range search on GPU: search on GPU with k=1024, if the farthest neighbor is still within range, re-perform search on CPU
- as reference implementations for precision-recall on range search datasets
- optimized code to plot precision-recall plots (ie. sweep over thresholds)
The new functions are mainly in a new `evaluation.py`
Reviewed By: wickedfoo
Differential Revision: D25627619
fbshipit-source-id: 58f90654c32c925557d7bbf8083efbb710712e03
