| // Ceres Solver - A fast non-linear least squares minimizer |
| // Copyright 2020 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: darius.rueckert@fau.de (Darius Rueckert) |
| |
| #include <memory> |
| |
| #include "benchmark/benchmark.h" |
| #include "ceres/autodiff_benchmarks/brdf_cost_function.h" |
| #include "ceres/autodiff_benchmarks/linear_cost_functions.h" |
| #include "ceres/autodiff_benchmarks/snavely_reprojection_error.h" |
| #include "ceres/ceres.h" |
| #include "ceres/codegen/test_utils.h" |
| |
| namespace ceres { |
| |
| #ifdef WITH_CODE_GENERATION |
| static void BM_Linear1CodeGen(benchmark::State& state) { |
| double parameter_block1[] = {1.}; |
| double* parameters[] = {parameter_block1}; |
| |
| double jacobian1[1]; |
| double residuals[1]; |
| double* jacobians[] = {jacobian1}; |
| |
| std::unique_ptr<ceres::CostFunction> cost_function(new Linear1CostFunction()); |
| |
| for (auto _ : state) { |
| cost_function->Evaluate( |
| parameters, residuals, state.range(0) ? jacobians : nullptr); |
| } |
| } |
| BENCHMARK(BM_Linear1CodeGen)->Arg(0)->Arg(1); |
| #endif |
| |
| static void BM_Linear1AutoDiff(benchmark::State& state) { |
| using FunctorType = |
| ceres::internal::CostFunctionToFunctor<Linear1CostFunction>; |
| |
| double parameter_block1[] = {1.}; |
| double* parameters[] = {parameter_block1}; |
| |
| double jacobian1[1]; |
| double residuals[1]; |
| double* jacobians[] = {jacobian1}; |
| |
| std::unique_ptr<ceres::CostFunction> cost_function( |
| new ceres::AutoDiffCostFunction<FunctorType, 1, 1>(new FunctorType())); |
| |
| for (auto _ : state) { |
| cost_function->Evaluate( |
| parameters, residuals, state.range(0) ? jacobians : nullptr); |
| } |
| } |
| BENCHMARK(BM_Linear1AutoDiff)->Arg(0)->Arg(1); |
| ; |
| |
| #ifdef WITH_CODE_GENERATION |
| static void BM_Linear10CodeGen(benchmark::State& state) { |
| double parameter_block1[] = {1., 2., 3., 4., 5., 6., 7., 8., 9., 10.}; |
| double* parameters[] = {parameter_block1}; |
| |
| double jacobian1[10 * 10]; |
| double residuals[10]; |
| double* jacobians[] = {jacobian1}; |
| |
| std::unique_ptr<ceres::CostFunction> cost_function( |
| new Linear10CostFunction()); |
| |
| for (auto _ : state) { |
| cost_function->Evaluate( |
| parameters, residuals, state.range(0) ? jacobians : nullptr); |
| } |
| } |
| BENCHMARK(BM_Linear10CodeGen)->Arg(0)->Arg(1); |
| ; |
| #endif |
| |
| static void BM_Linear10AutoDiff(benchmark::State& state) { |
| using FunctorType = |
| ceres::internal::CostFunctionToFunctor<Linear10CostFunction>; |
| |
| double parameter_block1[] = {1., 2., 3., 4., 5., 6., 7., 8., 9., 10.}; |
| double* parameters[] = {parameter_block1}; |
| |
| double jacobian1[10 * 10]; |
| double residuals[10]; |
| double* jacobians[] = {jacobian1}; |
| |
| std::unique_ptr<ceres::CostFunction> cost_function( |
| new ceres::AutoDiffCostFunction<FunctorType, 10, 10>(new FunctorType())); |
| |
| for (auto _ : state) { |
| cost_function->Evaluate( |
| parameters, residuals, state.range(0) ? jacobians : nullptr); |
| } |
| } |
| BENCHMARK(BM_Linear10AutoDiff)->Arg(0)->Arg(1); |
| ; |
| |
| // From the NIST problem collection. |
| struct Rat43CostFunctor { |
| Rat43CostFunctor(const double x, const double y) : x_(x), y_(y) {} |
| |
| template <typename T> |
| bool operator()(const T* parameters, T* residuals) const { |
| const T& b1 = parameters[0]; |
| const T& b2 = parameters[1]; |
| const T& b3 = parameters[2]; |
| const T& b4 = parameters[3]; |
| residuals[0] = b1 * pow(1.0 + exp(b2 - b3 * x_), -1.0 / b4) - y_; |
| return true; |
| } |
| |
| private: |
| const double x_; |
| const double y_; |
| }; |
| |
| static void BM_Rat43AutoDiff(benchmark::State& state) { |
| double parameter_block1[] = {1., 2., 3., 4.}; |
| double* parameters[] = {parameter_block1}; |
| |
| double jacobian1[] = {0.0, 0.0, 0.0, 0.0}; |
| double residuals; |
| double* jacobians[] = {jacobian1}; |
| const double x = 0.2; |
| const double y = 0.3; |
| std::unique_ptr<ceres::CostFunction> cost_function( |
| new ceres::AutoDiffCostFunction<Rat43CostFunctor, 1, 4>( |
| new Rat43CostFunctor(x, y))); |
| |
| for (auto _ : state) { |
| cost_function->Evaluate( |
| parameters, &residuals, state.range(0) ? jacobians : nullptr); |
| } |
| } |
| BENCHMARK(BM_Rat43AutoDiff)->Arg(0)->Arg(1); |
| |
| #ifdef WITH_CODE_GENERATION |
| static void BM_SnavelyReprojectionCodeGen(benchmark::State& state) { |
| double parameter_block1[] = {1., 2., 3., 4., 5., 6., 7., 8., 9.}; |
| double parameter_block2[] = {1., 2., 3.}; |
| double* parameters[] = {parameter_block1, parameter_block2}; |
| |
| double jacobian1[2 * 9]; |
| double jacobian2[2 * 3]; |
| double residuals[2]; |
| double* jacobians[] = {jacobian1, jacobian2}; |
| |
| const double x = 0.2; |
| const double y = 0.3; |
| |
| std::unique_ptr<ceres::CostFunction> cost_function( |
| new SnavelyReprojectionError(x, y)); |
| |
| for (auto _ : state) { |
| cost_function->Evaluate( |
| parameters, residuals, state.range(0) ? jacobians : nullptr); |
| } |
| } |
| BENCHMARK(BM_SnavelyReprojectionCodeGen)->Arg(0)->Arg(1); |
| ; |
| #endif |
| |
| static void BM_SnavelyReprojectionAutoDiff(benchmark::State& state) { |
| using FunctorType = |
| ceres::internal::CostFunctionToFunctor<SnavelyReprojectionError>; |
| |
| double parameter_block1[] = {1., 2., 3., 4., 5., 6., 7., 8., 9.}; |
| double parameter_block2[] = {1., 2., 3.}; |
| double* parameters[] = {parameter_block1, parameter_block2}; |
| |
| double jacobian1[2 * 9]; |
| double jacobian2[2 * 3]; |
| double residuals[2]; |
| double* jacobians[] = {jacobian1, jacobian2}; |
| |
| const double x = 0.2; |
| const double y = 0.3; |
| std::unique_ptr<ceres::CostFunction> cost_function( |
| new ceres::AutoDiffCostFunction<FunctorType, 2, 9, 3>( |
| new FunctorType(x, y))); |
| |
| for (auto _ : state) { |
| cost_function->Evaluate( |
| parameters, residuals, state.range(0) ? jacobians : nullptr); |
| } |
| } |
| |
| BENCHMARK(BM_SnavelyReprojectionAutoDiff)->Arg(0)->Arg(1); |
| ; |
| |
| #ifdef WITH_CODE_GENERATION |
| static void BM_BrdfCodeGen(benchmark::State& state) { |
| using FunctorType = ceres::internal::CostFunctionToFunctor<Brdf>; |
| |
| double material[] = {1., 2., 3., 4., 5., 6., 7., 8., 9., 10.}; |
| auto c = Eigen::Vector3d(0.1, 0.2, 0.3); |
| auto n = Eigen::Vector3d(-0.1, 0.5, 0.2).normalized(); |
| auto v = Eigen::Vector3d(0.5, -0.2, 0.9).normalized(); |
| auto l = Eigen::Vector3d(-0.3, 0.4, -0.3).normalized(); |
| auto x = Eigen::Vector3d(0.5, 0.7, -0.1).normalized(); |
| auto y = Eigen::Vector3d(0.2, -0.2, -0.2).normalized(); |
| |
| double* parameters[7] = { |
| material, c.data(), n.data(), v.data(), l.data(), x.data(), y.data()}; |
| |
| double jacobian[(10 + 6 * 3) * 3]; |
| double residuals[3]; |
| double* jacobians[7] = { |
| jacobian + 0, |
| jacobian + 10 * 3, |
| jacobian + 13 * 3, |
| jacobian + 16 * 3, |
| jacobian + 19 * 3, |
| jacobian + 22 * 3, |
| jacobian + 25 * 3, |
| }; |
| |
| std::unique_ptr<ceres::CostFunction> cost_function(new Brdf()); |
| |
| for (auto _ : state) { |
| cost_function->Evaluate( |
| parameters, residuals, state.range(0) ? jacobians : nullptr); |
| } |
| } |
| |
| BENCHMARK(BM_BrdfCodeGen)->Arg(0)->Arg(1); |
| ; |
| #endif |
| |
| static void BM_BrdfAutoDiff(benchmark::State& state) { |
| using FunctorType = ceres::internal::CostFunctionToFunctor<Brdf>; |
| |
| double material[] = {1., 2., 3., 4., 5., 6., 7., 8., 9., 10.}; |
| auto c = Eigen::Vector3d(0.1, 0.2, 0.3); |
| auto n = Eigen::Vector3d(-0.1, 0.5, 0.2).normalized(); |
| auto v = Eigen::Vector3d(0.5, -0.2, 0.9).normalized(); |
| auto l = Eigen::Vector3d(-0.3, 0.4, -0.3).normalized(); |
| auto x = Eigen::Vector3d(0.5, 0.7, -0.1).normalized(); |
| auto y = Eigen::Vector3d(0.2, -0.2, -0.2).normalized(); |
| |
| double* parameters[7] = { |
| material, c.data(), n.data(), v.data(), l.data(), x.data(), y.data()}; |
| |
| double jacobian[(10 + 6 * 3) * 3]; |
| double residuals[3]; |
| double* jacobians[7] = { |
| jacobian + 0, |
| jacobian + 10 * 3, |
| jacobian + 13 * 3, |
| jacobian + 16 * 3, |
| jacobian + 19 * 3, |
| jacobian + 22 * 3, |
| jacobian + 25 * 3, |
| }; |
| |
| std::unique_ptr<ceres::CostFunction> cost_function( |
| new ceres::AutoDiffCostFunction<FunctorType, 3, 10, 3, 3, 3, 3, 3, 3>( |
| new FunctorType)); |
| |
| for (auto _ : state) { |
| cost_function->Evaluate( |
| parameters, residuals, state.range(0) ? jacobians : nullptr); |
| } |
| } |
| |
| BENCHMARK(BM_BrdfAutoDiff)->Arg(0)->Arg(1); |
| ; |
| |
| } // namespace ceres |
| |
| BENCHMARK_MAIN(); |