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a996a4a052
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
63 lines
1.6 KiB
C++
63 lines
1.6 KiB
C++
/**
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* Copyright (c) Facebook, Inc. and its affiliates.
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*
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* This source code is licensed under the MIT license found in the
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* LICENSE file in the root directory of this source tree.
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*/
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#include <cstdio>
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#include <cstdlib>
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#include <gtest/gtest.h>
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#include <faiss/IndexBinaryFlat.h>
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#include <faiss/utils/hamming.h>
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TEST(BinaryFlat, accuracy) {
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// dimension of the vectors to index
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int d = 64;
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// size of the database we plan to index
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size_t nb = 1000;
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// make the index object and train it
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faiss::IndexBinaryFlat index(d);
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std::vector<uint8_t> database(nb * (d / 8));
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for (size_t i = 0; i < nb * (d / 8); i++) {
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database[i] = rand() % 0x100;
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}
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{ // populating the database
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index.add(nb, database.data());
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}
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size_t nq = 200;
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{ // searching the database
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std::vector<uint8_t> queries(nq * (d / 8));
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for (size_t i = 0; i < nq * (d / 8); i++) {
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queries[i] = rand() % 0x100;
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}
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int k = 5;
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std::vector<faiss::idx_t> nns(k * nq);
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std::vector<int> dis(k * nq);
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index.search(nq, queries.data(), k, dis.data(), nns.data());
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for (size_t i = 0; i < nq; ++i) {
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faiss::HammingComputer8 hc(queries.data() + i * (d / 8), d / 8);
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hamdis_t dist_min = hc.hamming(database.data());
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for (size_t j = 1; j < nb; ++j) {
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hamdis_t dist = hc.hamming(database.data() + j * (d / 8));
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if (dist < dist_min) {
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dist_min = dist;
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}
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}
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EXPECT_EQ(dist_min, dis[k * i]);
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}
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}
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}
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