| 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174 | // Ceres Solver - A fast non-linear least squares minimizer// Copyright 2019 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/autodiff_cost_function.h"#include <memory>#include "gtest/gtest.h"#include "ceres/cost_function.h"#include "ceres/array_utils.h"namespace ceres {namespace internal {class BinaryScalarCost { public:  explicit BinaryScalarCost(double a): a_(a) {}  template <typename T>  bool operator()(const T* const x, const T* const y,                  T* cost) const {    cost[0] = x[0] * y[0] + x[1] * y[1]  - T(a_);    return true;  } private:  double a_;};TEST(AutodiffCostFunction, BilinearDifferentiationTest) {  CostFunction* cost_function  =    new AutoDiffCostFunction<BinaryScalarCost, 1, 2, 2>(        new BinaryScalarCost(1.0));  double** parameters = new double*[2];  parameters[0] = new double[2];  parameters[1] = new double[2];  parameters[0][0] = 1;  parameters[0][1] = 2;  parameters[1][0] = 3;  parameters[1][1] = 4;  double** jacobians = new double*[2];  jacobians[0] = new double[2];  jacobians[1] = new double[2];  double residuals = 0.0;  cost_function->Evaluate(parameters, &residuals, nullptr);  EXPECT_EQ(10.0, residuals);  cost_function->Evaluate(parameters, &residuals, jacobians);  EXPECT_EQ(10.0, residuals);  EXPECT_EQ(3, jacobians[0][0]);  EXPECT_EQ(4, jacobians[0][1]);  EXPECT_EQ(1, jacobians[1][0]);  EXPECT_EQ(2, jacobians[1][1]);  delete[] jacobians[0];  delete[] jacobians[1];  delete[] parameters[0];  delete[] parameters[1];  delete[] jacobians;  delete[] parameters;  delete cost_function;}struct TenParameterCost {  template <typename T>  bool operator()(const T* const x0,                  const T* const x1,                  const T* const x2,                  const T* const x3,                  const T* const x4,                  const T* const x5,                  const T* const x6,                  const T* const x7,                  const T* const x8,                  const T* const x9,                  T* cost) const {    cost[0] = *x0 + *x1 + *x2 + *x3 + *x4 + *x5 + *x6 + *x7 + *x8 + *x9;    return true;  }};TEST(AutodiffCostFunction, ManyParameterAutodiffInstantiates) {  CostFunction* cost_function  =      new AutoDiffCostFunction<          TenParameterCost, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1>(              new TenParameterCost);  double** parameters = new double*[10];  double** jacobians = new double*[10];  for (int i = 0; i < 10; ++i) {    parameters[i] = new double[1];    parameters[i][0] = i;    jacobians[i] = new double[1];  }  double residuals = 0.0;  cost_function->Evaluate(parameters, &residuals, nullptr);  EXPECT_EQ(45.0, residuals);  cost_function->Evaluate(parameters, &residuals, jacobians);  EXPECT_EQ(residuals, 45.0);  for (int i = 0; i < 10; ++i) {    EXPECT_EQ(1.0, jacobians[i][0]);  }  for (int i = 0; i < 10; ++i) {    delete[] jacobians[i];    delete[] parameters[i];  }  delete[] jacobians;  delete[] parameters;  delete cost_function;}struct OnlyFillsOneOutputFunctor {  template <typename T>  bool operator()(const T* x, T* output) const {    output[0] = x[0];    return true;  }};TEST(AutoDiffCostFunction, PartiallyFilledResidualShouldFailEvaluation) {  double parameter = 1.0;  double jacobian[2];  double residuals[2];  double* parameters[] = {¶meter};  double* jacobians[] = {jacobian};  std::unique_ptr<CostFunction> cost_function(      new AutoDiffCostFunction<OnlyFillsOneOutputFunctor, 2, 1>(          new OnlyFillsOneOutputFunctor));  InvalidateArray(2, jacobian);  InvalidateArray(2, residuals);  EXPECT_TRUE(cost_function->Evaluate(parameters, residuals, jacobians));  EXPECT_FALSE(IsArrayValid(2, jacobian));  EXPECT_FALSE(IsArrayValid(2, residuals));}}  // namespace internal}  // namespace ceres
 |