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ivf-hnsw

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Code for ECCV2018 paper: Revisiting the Inverted Indices for Billion-Scale Approximate Nearest Neighbors

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최종 버전 다운로드 (.zip)

- - load_deep1b.py
- - load_sift1b.sh
- - annotated.html
  - bc_s.png
  - bdwn.png
  - classes.html
  - classivfhnsw_1_1StopW-members.html
  - classivfhnsw_1_1StopW.html
  - closed.png
  - doxygen.css
  - doxygen.png
  - dynsections.js
  - files.html
  - ftv2blank.png
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  - ftv2doc.png
  - ftv2folderclosed.png
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  - ftv2splitbar.png
  - ftv2vertline.png
  - functions.html
  - functions_func.html
  - functions_type.html
  - functions_vars.html
  - hierarchy.html
  - index.html
  - IndexIVF__HNSW_8h_source.html
  - IndexIVF__HNSW__Grouping_8h_source.html
  - jquery.js
  - md_README.html
  - nav_f.png
  - nav_g.png
  - nav_h.png
  - open.png
  - pages.html
  - Parser_8h_source.html
  - structivfhnsw_1_1IndexIVF__HNSW-members.html
  - structivfhnsw_1_1IndexIVF__HNSW.html
  - structivfhnsw_1_1IndexIVF__HNSW.png
  - structivfhnsw_1_1IndexIVF__HNSW__Grouping-members.html
  - structivfhnsw_1_1IndexIVF__HNSW__Grouping.html
  - structivfhnsw_1_1IndexIVF__HNSW__Grouping.png
  - structParser-members.html
  - structParser.html
  - sync_off.png
  - sync_on.png
  - tab_a.png
  - tab_b.png
  - tab_h.png
  - tab_s.png
  - tabs.css
  - utils_8h_source.html
- Doxyfile
- run_deep1b.sh
- run_deep1b_grouping.sh
- run_deep1b_grouping_OPQ.sh
- run_deep1b_OPQ.sh
- run_sift1b.sh
- run_sift1b_grouping.sh
- run_sift1b_grouping_OPQ.sh
- run_sift1b_OPQ.sh
- CMakeLists.txt
- hnswalg.cpp
- hnswalg.h
- visited_list_pool.h
- CMakeLists.txt
- test_ivfhnsw_deep1b.cpp
- test_ivfhnsw_grouping_deep1b.cpp
- test_ivfhnsw_grouping_sift1b.cpp
- test_ivfhnsw_sift1b.cpp
.gitignore
.gitmodules
CMakeLists.txt
CMakeLists.txt.faiss
faiss
IndexIVF_HNSW.cpp
IndexIVF_HNSW.h
IndexIVF_HNSW_Grouping.cpp
IndexIVF_HNSW_Grouping.h
LICENSE
Parser.h
README.md
utils.cpp
utils.h

// repository documentation

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# Revisiting the Inverted Indices for Billion-Scale Approximate Nearest Neighbors

This is the code for the current state-of-the-art billion-scale nearest neighbor search system presented in the paper:

[Revisiting the Inverted Indices for Billion-Scale Approximate Nearest Neighbors](http://openaccess.thecvf.com/content_ECCV_2018/html/Dmitry_Baranchuk_Revisiting_the_Inverted_ECCV_2018_paper.html),
<br>
Dmitry Baranchuk, Artem Babenko, Yury Malkov

The code is developed upon the [FAISS](https://github.com/facebookresearch/faiss) library.

### Build

Today we provide the C++ implementation supporting only the CPU version, 
which requires a BLAS library.

The code requires a C++ compiler that understands:

- the Intel intrinsics for SSE instructions
- the GCC intrinsic for the popcount instruction
- basic OpenMP

#### Installation instructions
1) Clone repository

```git clone https://github.com/dbaranchuk/ivf-hnsw --recursive```

2) Configure FAISS

There are a few models for makefile.inc in the faiss/example_makefiles/
subdirectory. Copy the relevant one for your system to faiss/ and adjust to your
needs. In particular, for ivf-hnsw project, you need to set a proper BLAS library paths.
There are also indications for specific configurations in the
troubleshooting section of the [FAISS wiki](https://github.com/facebookresearch/faiss/wiki/Troubleshooting)

3) Replace FAISS CMakeList.txt

Replace faiss/CMakeList.txt with CMakeList.txt.faiss in order to 
deactivate building of unnecessary tests and the GPU version.

```mv CMakeLists.txt.faiss faiss/CMakeLists.txt```

4) Build project

```cmake . && make```

### Data
The proposed methods are tested on two 1 billion datasets: SIFT1B and DEEP1B. 
For using provided examples, all data files have to be in data/SIFT1B and data/DEEP1B.

#### Data files:
* SIFT1B:
   - dataset, [Datasets for approximate nearest neighbor search](http://corpus-texmex.irisa.fr/)
   
   ```cd data/SIFT1B && bash load_sift1b.sh```
   - learned 993127 centroids, [GoogleDrive](https://drive.google.com/file/d/1p9Aq5lTiXzmuP1ftJAIqKYEEN5EVBZsS/view?usp=sharing)
   - precomputed indices of assigned base points, [GoogleDrive](https://drive.google.com/file/d/1iFgzY2niWsCwKCPpbsjZh1urudrswEyL/view?usp=sharing)
* DEEP1B:
   - dataset, [YandexDrive](https://yadi.sk/d/11eDCm7Dsn9GA)
   
   ```cd data/DEEP1B && python load_deep1b.py```
   - learned 999973 centroids, [GoogleDrive](https://drive.google.com/file/d/1loJ0rEIBORM34vsVSZrNeJrq1OtrcmKu/view?usp=sharing)
   - precomputed indices of assigned base points, [GoogleDrive](https://drive.google.com/file/d/10DMFnLUs5Fdr_BCht9nsa2vSyG1LKJeV/view?usp=sharing) 
    
Note: precomputed indices are optional, as it just lets avoid assigning step, which takes about 2-3 days for 2^20 centroids.

### Run
tests/ provides two tests for each dataset: 
- IVFADC
- IVFADC + Grouping (+ Pruning)

Each test requires many options, so we provide bash scripts in examples/, 
exploiting these tests. Scripts are commented and 
the Parser class provides short descriptions for each option.  
  
Make sure that:
- models/SIFT1B/ and models/DEEP1B/ exist

```mkdir models && mkdir models/SIFT1B && mkdir models/DEEP1B```
- the data is placed to data/SIFT1B/ and data/DEEP1B/ respectively 
(or just make symbolic links)
- run, for example:

```bash examples/run_deep1b_grouping.sh```

### Documentation
The [doxygen documentation](https://cdn.rawgit.com/dbaranchuk/ivf-hnsw/fe2e4a85/docs/html/annotated.html) 
gives per-class information