| // 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/autodiff_cost_function.h" |
| |
| #include <memory> |
| |
| #include "ceres/array_utils.h" |
| #include "ceres/cost_function.h" |
| #include "gtest/gtest.h" |
| |
| namespace ceres::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)); |
| |
| auto** 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; |
| |
| auto** 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); |
| |
| auto** parameters = new double*[10]; |
| auto** 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 ceres::internal |