internal/ceres/autodiff_benchmarks/brdf_benchmark.cc

// Ceres Solver - A fast non-linear least squares minimizer
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// Author: darius.rueckert@fau.de (Darius Rueckert)

#include <memory>

#include "benchmark/benchmark.h"
#include "brdf_cost_function.h"
#include "ceres/ceres.h"
#include "codegen/test_utils.h"

namespace ceres {

#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());

  while (state.KeepRunning()) {
    cost_function->Evaluate(parameters, residuals, jacobians);
  }
}

BENCHMARK(BM_BrdfCodeGen);
#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));

  while (state.KeepRunning()) {
    cost_function->Evaluate(parameters, residuals, jacobians);
  }
}

BENCHMARK(BM_BrdfAutoDiff);

}  // namespace ceres

BENCHMARK_MAIN();