| // Ceres Solver - A fast non-linear least squares minimizer |
| // Copyright 2023 Google Inc. All rights reserved. |
| // http://ceres-solver.org/ |
| // |
| // Redistribution and use in source and binary forms, with or without |
| // modification, are permitted provided that the following conditions are met: |
| // |
| // * Redistributions of source code must retain the above copyright notice, |
| // this list of conditions and the following disclaimer. |
| // * Redistributions in binary form must reproduce the above copyright notice, |
| // this list of conditions and the following disclaimer in the documentation |
| // and/or other materials provided with the distribution. |
| // * Neither the name of Google Inc. nor the names of its contributors may be |
| // used to endorse or promote products derived from this software without |
| // specific prior written permission. |
| // |
| // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" |
| // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE |
| // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE |
| // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE |
| // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR |
| // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF |
| // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS |
| // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN |
| // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) |
| // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE |
| // POSSIBILITY OF SUCH DAMAGE. |
| // |
| // Author: sameeragarwal@google.com (Sameer Agarwal) |
| |
| #include "ceres/dense_cholesky.h" |
| |
| #include <memory> |
| #include <numeric> |
| #include <sstream> |
| #include <string> |
| #include <utility> |
| #include <vector> |
| |
| #include "Eigen/Dense" |
| #include "ceres/internal/config.h" |
| #include "ceres/internal/eigen.h" |
| #include "ceres/iterative_refiner.h" |
| #include "ceres/linear_solver.h" |
| #include "glog/logging.h" |
| #include "gmock/gmock.h" |
| #include "gtest/gtest.h" |
| |
| namespace ceres::internal { |
| |
| using Param = ::testing::tuple<DenseLinearAlgebraLibraryType, bool>; |
| constexpr bool kMixedPrecision = true; |
| constexpr bool kFullPrecision = false; |
| |
| namespace { |
| |
| std::string ParamInfoToString(testing::TestParamInfo<Param> info) { |
| Param param = info.param; |
| std::stringstream ss; |
| ss << DenseLinearAlgebraLibraryTypeToString(::testing::get<0>(param)) << "_" |
| << (::testing::get<1>(param) ? "MixedPrecision" : "FullPrecision"); |
| return ss.str(); |
| } |
| } // namespace |
| |
| class DenseCholeskyTest : public ::testing::TestWithParam<Param> {}; |
| |
| TEST_P(DenseCholeskyTest, FactorAndSolve) { |
| // TODO(sameeragarwal): Convert these tests into type parameterized tests so |
| // that we can test the single and double precision solvers. |
| |
| using Scalar = double; |
| using MatrixType = Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic>; |
| using VectorType = Eigen::Matrix<Scalar, Eigen::Dynamic, 1>; |
| |
| LinearSolver::Options options; |
| ContextImpl context; |
| #ifndef CERES_NO_CUDA |
| options.context = &context; |
| std::string error; |
| CHECK(context.InitCuda(&error)) << error; |
| #endif // CERES_NO_CUDA |
| options.dense_linear_algebra_library_type = ::testing::get<0>(GetParam()); |
| options.use_mixed_precision_solves = ::testing::get<1>(GetParam()); |
| const int kNumRefinementSteps = 4; |
| if (options.use_mixed_precision_solves) { |
| options.max_num_refinement_iterations = kNumRefinementSteps; |
| } |
| auto dense_cholesky = DenseCholesky::Create(options); |
| |
| const int kNumTrials = 10; |
| const int kMinNumCols = 1; |
| const int kMaxNumCols = 10; |
| for (int num_cols = kMinNumCols; num_cols < kMaxNumCols; ++num_cols) { |
| for (int trial = 0; trial < kNumTrials; ++trial) { |
| const MatrixType a = MatrixType::Random(num_cols, num_cols); |
| MatrixType lhs = a.transpose() * a; |
| lhs += VectorType::Ones(num_cols).asDiagonal(); |
| Vector x = VectorType::Random(num_cols); |
| Vector rhs = lhs * x; |
| Vector actual = Vector::Random(num_cols); |
| |
| LinearSolver::Summary summary; |
| summary.termination_type = dense_cholesky->FactorAndSolve( |
| num_cols, lhs.data(), rhs.data(), actual.data(), &summary.message); |
| EXPECT_EQ(summary.termination_type, LinearSolverTerminationType::SUCCESS); |
| EXPECT_NEAR((x - actual).norm() / x.norm(), |
| 0.0, |
| std::numeric_limits<double>::epsilon() * 10) |
| << "\nexpected: " << x.transpose() |
| << "\nactual : " << actual.transpose(); |
| } |
| } |
| } |
| |
| INSTANTIATE_TEST_SUITE_P(EigenCholesky, |
| DenseCholeskyTest, |
| ::testing::Combine(::testing::Values(EIGEN), |
| ::testing::Values(kMixedPrecision, |
| kFullPrecision)), |
| ParamInfoToString); |
| #ifndef CERES_NO_LAPACK |
| INSTANTIATE_TEST_SUITE_P(LapackCholesky, |
| DenseCholeskyTest, |
| ::testing::Combine(::testing::Values(LAPACK), |
| ::testing::Values(kMixedPrecision, |
| kFullPrecision)), |
| ParamInfoToString); |
| #endif |
| #ifndef CERES_NO_CUDA |
| INSTANTIATE_TEST_SUITE_P(CudaCholesky, |
| DenseCholeskyTest, |
| ::testing::Combine(::testing::Values(CUDA), |
| ::testing::Values(kMixedPrecision, |
| kFullPrecision)), |
| ParamInfoToString); |
| #endif |
| |
| class MockDenseCholesky : public DenseCholesky { |
| public: |
| MOCK_METHOD3(Factorize, |
| LinearSolverTerminationType(int num_cols, |
| double* lhs, |
| std::string* message)); |
| MOCK_METHOD3(Solve, |
| LinearSolverTerminationType(const double* rhs, |
| double* solution, |
| std::string* message)); |
| }; |
| |
| class MockDenseIterativeRefiner : public DenseIterativeRefiner { |
| public: |
| MockDenseIterativeRefiner() : DenseIterativeRefiner(1) {} |
| MOCK_METHOD5(Refine, |
| void(int num_cols, |
| const double* lhs, |
| const double* rhs, |
| DenseCholesky* dense_cholesky, |
| double* solution)); |
| }; |
| |
| using testing::_; |
| using testing::Return; |
| |
| TEST(RefinedDenseCholesky, Factorize) { |
| auto dense_cholesky = std::make_unique<MockDenseCholesky>(); |
| auto iterative_refiner = std::make_unique<MockDenseIterativeRefiner>(); |
| EXPECT_CALL(*dense_cholesky, Factorize(_, _, _)) |
| .Times(1) |
| .WillRepeatedly(Return(LinearSolverTerminationType::SUCCESS)); |
| EXPECT_CALL(*iterative_refiner, Refine(_, _, _, _, _)).Times(0); |
| RefinedDenseCholesky refined_dense_cholesky(std::move(dense_cholesky), |
| std::move(iterative_refiner)); |
| double lhs; |
| std::string message; |
| EXPECT_EQ(refined_dense_cholesky.Factorize(1, &lhs, &message), |
| LinearSolverTerminationType::SUCCESS); |
| }; |
| |
| TEST(RefinedDenseCholesky, FactorAndSolveWithUnsuccessfulFactorization) { |
| auto dense_cholesky = std::make_unique<MockDenseCholesky>(); |
| auto iterative_refiner = std::make_unique<MockDenseIterativeRefiner>(); |
| EXPECT_CALL(*dense_cholesky, Factorize(_, _, _)) |
| .Times(1) |
| .WillRepeatedly(Return(LinearSolverTerminationType::FAILURE)); |
| EXPECT_CALL(*dense_cholesky, Solve(_, _, _)).Times(0); |
| EXPECT_CALL(*iterative_refiner, Refine(_, _, _, _, _)).Times(0); |
| RefinedDenseCholesky refined_dense_cholesky(std::move(dense_cholesky), |
| std::move(iterative_refiner)); |
| double lhs; |
| std::string message; |
| double rhs; |
| double solution; |
| EXPECT_EQ( |
| refined_dense_cholesky.FactorAndSolve(1, &lhs, &rhs, &solution, &message), |
| LinearSolverTerminationType::FAILURE); |
| }; |
| |
| TEST(RefinedDenseCholesky, FactorAndSolveWithSuccess) { |
| auto dense_cholesky = std::make_unique<MockDenseCholesky>(); |
| auto iterative_refiner = std::make_unique<MockDenseIterativeRefiner>(); |
| EXPECT_CALL(*dense_cholesky, Factorize(_, _, _)) |
| .Times(1) |
| .WillRepeatedly(Return(LinearSolverTerminationType::SUCCESS)); |
| EXPECT_CALL(*dense_cholesky, Solve(_, _, _)) |
| .Times(1) |
| .WillRepeatedly(Return(LinearSolverTerminationType::SUCCESS)); |
| EXPECT_CALL(*iterative_refiner, Refine(_, _, _, _, _)).Times(1); |
| |
| RefinedDenseCholesky refined_dense_cholesky(std::move(dense_cholesky), |
| std::move(iterative_refiner)); |
| double lhs; |
| std::string message; |
| double rhs; |
| double solution; |
| EXPECT_EQ( |
| refined_dense_cholesky.FactorAndSolve(1, &lhs, &rhs, &solution, &message), |
| LinearSolverTerminationType::SUCCESS); |
| }; |
| |
| } // namespace ceres::internal |