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https://gitee.com/milvus-io/milvus.git
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temp update
Former-commit-id: 42adb2a912930d35c53d7fefca1f35d556b45c05
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zhiru
parent
0f8ac6dc65
commit
48fa9efd44
@ -9,144 +9,130 @@
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#include "wrapper/Operand.h"
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#include "wrapper/Index.h"
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#include "wrapper/IndexBuilder.h"
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#include <regex>
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using namespace zilliz::milvus::engine;
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TEST(xxx, Wrapper_Test){
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// std::string xxx = "dialect+driver://username:password@host:port/database";
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//mysql://scott:tiger@localhost/mydatabase
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TEST(operand_test, Wrapper_Test) {
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using std::cout;
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using std::endl;
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std::string littel_xx = "dixx://";
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std::regex xx_regex("([a-zA-Z0-9_-\\.]*):\\/\\/([a-zA-Z0-9_-\\.]*)\\:([a-zA-Z0-9_-\\.]*)\\@([a-zA-Z0-9_-\\.]*)\\:([0-9]*)\\/([a-zA-Z0-9_-\\.]*)");
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std::smatch pieces_match;
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std::regex_match(littel_xx, pieces_match, xx_regex);
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auto opd = std::make_shared<Operand>();
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opd->index_type = "IVF";
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opd->preproc = "OPQ";
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opd->postproc = "PQ";
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opd->metric_type = "L2";
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opd->d = 64;
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for(auto &x : pieces_match) {
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std::cout << "hehhe: " << x.str() << std::endl;
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}
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auto opd_str = operand_to_str(opd);
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auto new_opd = str_to_operand(opd_str);
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// TODO: fix all place where using opd to build index.
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assert(new_opd->get_index_type(10000) == opd->get_index_type(10000));
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}
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//TEST(operand_test, Wrapper_Test) {
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// using std::cout;
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// using std::endl;
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//
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// auto opd = std::make_shared<Operand>();
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// opd->index_type = "IVF";
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// opd->preproc = "OPQ";
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// opd->postproc = "PQ";
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// opd->metric_type = "L2";
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// opd->d = 64;
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//
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// auto opd_str = operand_to_str(opd);
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// auto new_opd = str_to_operand(opd_str);
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//
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// // TODO: fix all place where using opd to build index.
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// assert(new_opd->get_index_type(10000) == opd->get_index_type(10000));
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//}
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//
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//TEST(build_test, Wrapper_Test) {
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// // dimension of the vectors to index
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// int d = 3;
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//
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// // make a set of nt training vectors in the unit cube
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// size_t nt = 10000;
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//
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// // a reasonable number of cetroids to index nb vectors
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// int ncentroids = 16;
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//
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// std::random_device rd;
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// std::mt19937 gen(rd());
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//
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// std::vector<float> xb;
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// std::vector<long> ids;
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//
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// //prepare train data
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// std::uniform_real_distribution<> dis_xt(-1.0, 1.0);
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// std::vector<float> xt(nt * d);
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// for (size_t i = 0; i < nt * d; i++) {
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// xt[i] = dis_xt(gen);
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// }
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//
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// //train the index
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// auto opd = std::make_shared<Operand>();
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// opd->index_type = "IVF";
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// opd->d = d;
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// opd->ncent = ncentroids;
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// IndexBuilderPtr index_builder_1 = GetIndexBuilder(opd);
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// auto index_1 = index_builder_1->build_all(0, xb, ids, nt, xt);
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// ASSERT_TRUE(index_1 != nullptr);
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//
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// // size of the database we plan to index
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// size_t nb = 100000;
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//
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// //prepare raw data
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// xb.resize(nb);
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// ids.resize(nb);
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// for (size_t i = 0; i < nb; i++) {
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// xb[i] = dis_xt(gen);
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// ids[i] = i;
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// }
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// index_1->add_with_ids(nb, xb.data(), ids.data());
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//
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// //search in first quadrant
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// int nq = 1, k = 10;
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// std::vector<float> xq = {0.5, 0.5, 0.5};
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// float *result_dists = new float[k];
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// long *result_ids = new long[k];
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// index_1->search(nq, xq.data(), k, result_dists, result_ids);
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//
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// for (int i = 0; i < k; i++) {
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// if (result_ids[i] < 0) {
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// ASSERT_TRUE(false);
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// break;
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// }
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//
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// long id = result_ids[i];
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// std::cout << "No." << id << " [" << xb[id * 3] << ", " << xb[id * 3 + 1] << ", "
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// << xb[id * 3 + 2] << "] distance = " << result_dists[i] << std::endl;
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//
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// //makesure result vector is in first quadrant
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// ASSERT_TRUE(xb[id * 3] > 0.0);
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// ASSERT_TRUE(xb[id * 3 + 1] > 0.0);
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// ASSERT_TRUE(xb[id * 3 + 2] > 0.0);
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// }
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//
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// delete[] result_dists;
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// delete[] result_ids;
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//}
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//
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//TEST(gpu_build_test, Wrapper_Test) {
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// using std::vector;
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//
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// int d = 256;
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// int nb = 3 * 1000 * 100;
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// int nq = 100;
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// vector<float> xb(d * nb);
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// vector<float> xq(d * nq);
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// vector<long> ids(nb);
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//
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// std::random_device rd;
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// std::mt19937 gen(rd());
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// std::uniform_real_distribution<> dis_xt(-1.0, 1.0);
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// for (auto &e : xb) { e = float(dis_xt(gen)); }
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// for (auto &e : xq) { e = float(dis_xt(gen)); }
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// for (int i = 0; i < nb; ++i) { ids[i] = i; }
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//
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// auto opd = std::make_shared<Operand>();
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// opd->index_type = "IVF";
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// opd->d = d;
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// opd->ncent = 256;
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//
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// IndexBuilderPtr index_builder_1 = GetIndexBuilder(opd);
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// auto index_1 = index_builder_1->build_all(nb, xb.data(), ids.data());
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// assert(index_1->ntotal == nb);
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// assert(index_1->dim == d);
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//
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// // sanity check: search 5 first vectors of xb
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// int k = 1;
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// vector<long> I(5 * k);
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// vector<float> D(5 * k);
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// index_1->search(5, xb.data(), k, D.data(), I.data());
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// for (int i = 0; i < 5; ++i) { assert(i == I[i]); }
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//}
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TEST(build_test, Wrapper_Test) {
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// dimension of the vectors to index
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int d = 3;
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// make a set of nt training vectors in the unit cube
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size_t nt = 10000;
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// a reasonable number of cetroids to index nb vectors
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int ncentroids = 16;
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std::random_device rd;
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std::mt19937 gen(rd());
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std::vector<float> xb;
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std::vector<long> ids;
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//prepare train data
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std::uniform_real_distribution<> dis_xt(-1.0, 1.0);
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std::vector<float> xt(nt * d);
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for (size_t i = 0; i < nt * d; i++) {
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xt[i] = dis_xt(gen);
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}
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//train the index
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auto opd = std::make_shared<Operand>();
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opd->index_type = "IVF";
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opd->d = d;
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opd->ncent = ncentroids;
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IndexBuilderPtr index_builder_1 = GetIndexBuilder(opd);
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auto index_1 = index_builder_1->build_all(0, xb, ids, nt, xt);
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ASSERT_TRUE(index_1 != nullptr);
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// size of the database we plan to index
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size_t nb = 100000;
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//prepare raw data
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xb.resize(nb);
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ids.resize(nb);
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for (size_t i = 0; i < nb; i++) {
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xb[i] = dis_xt(gen);
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ids[i] = i;
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}
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index_1->add_with_ids(nb, xb.data(), ids.data());
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//search in first quadrant
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int nq = 1, k = 10;
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std::vector<float> xq = {0.5, 0.5, 0.5};
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float *result_dists = new float[k];
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long *result_ids = new long[k];
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index_1->search(nq, xq.data(), k, result_dists, result_ids);
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for (int i = 0; i < k; i++) {
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if (result_ids[i] < 0) {
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ASSERT_TRUE(false);
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break;
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}
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long id = result_ids[i];
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std::cout << "No." << id << " [" << xb[id * 3] << ", " << xb[id * 3 + 1] << ", "
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<< xb[id * 3 + 2] << "] distance = " << result_dists[i] << std::endl;
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//makesure result vector is in first quadrant
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ASSERT_TRUE(xb[id * 3] > 0.0);
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ASSERT_TRUE(xb[id * 3 + 1] > 0.0);
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ASSERT_TRUE(xb[id * 3 + 2] > 0.0);
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}
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delete[] result_dists;
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delete[] result_ids;
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}
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TEST(gpu_build_test, Wrapper_Test) {
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using std::vector;
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int d = 256;
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int nb = 3 * 1000 * 100;
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int nq = 100;
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vector<float> xb(d * nb);
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vector<float> xq(d * nq);
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vector<long> ids(nb);
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std::random_device rd;
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std::mt19937 gen(rd());
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std::uniform_real_distribution<> dis_xt(-1.0, 1.0);
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for (auto &e : xb) { e = float(dis_xt(gen)); }
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for (auto &e : xq) { e = float(dis_xt(gen)); }
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for (int i = 0; i < nb; ++i) { ids[i] = i; }
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auto opd = std::make_shared<Operand>();
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opd->index_type = "IVF";
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opd->d = d;
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opd->ncent = 256;
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IndexBuilderPtr index_builder_1 = GetIndexBuilder(opd);
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auto index_1 = index_builder_1->build_all(nb, xb.data(), ids.data());
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assert(index_1->ntotal == nb);
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assert(index_1->dim == d);
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// sanity check: search 5 first vectors of xb
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int k = 1;
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vector<long> I(5 * k);
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vector<float> D(5 * k);
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index_1->search(5, xb.data(), k, D.data(), I.data());
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for (int i = 0; i < 5; ++i) { assert(i == I[i]); }
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}
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