| // 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: wjr@google.com (William Rucklidge) |
| // |
| // This file contains tests for the GradientChecker class. |
| |
| #include "ceres/gradient_checker.h" |
| |
| #include <cmath> |
| #include <random> |
| #include <utility> |
| #include <vector> |
| |
| #include "ceres/cost_function.h" |
| #include "ceres/problem.h" |
| #include "ceres/solver.h" |
| #include "ceres/test_util.h" |
| #include "glog/logging.h" |
| #include "gtest/gtest.h" |
| |
| namespace ceres::internal { |
| |
| const double kTolerance = 1e-12; |
| |
| // We pick a (non-quadratic) function whose derivative are easy: |
| // |
| // f = exp(- a' x). |
| // df = - f a. |
| // |
| // where 'a' is a vector of the same size as 'x'. In the block |
| // version, they are both block vectors, of course. |
| class GoodTestTerm : public CostFunction { |
| public: |
| template <class UniformRandomFunctor> |
| GoodTestTerm(int arity, int const* dim, UniformRandomFunctor&& randu) |
| : arity_(arity), return_value_(true) { |
| std::uniform_real_distribution<double> distribution(-1.0, 1.0); |
| // Make 'arity' random vectors. |
| a_.resize(arity_); |
| for (int j = 0; j < arity_; ++j) { |
| a_[j].resize(dim[j]); |
| for (int u = 0; u < dim[j]; ++u) { |
| a_[j][u] = randu(); |
| } |
| } |
| |
| for (int i = 0; i < arity_; i++) { |
| mutable_parameter_block_sizes()->push_back(dim[i]); |
| } |
| set_num_residuals(1); |
| } |
| |
| bool Evaluate(double const* const* parameters, |
| double* residuals, |
| double** jacobians) const override { |
| if (!return_value_) { |
| return false; |
| } |
| // Compute a . x. |
| double ax = 0; |
| for (int j = 0; j < arity_; ++j) { |
| for (int u = 0; u < parameter_block_sizes()[j]; ++u) { |
| ax += a_[j][u] * parameters[j][u]; |
| } |
| } |
| |
| // This is the cost, but also appears as a factor |
| // in the derivatives. |
| double f = *residuals = exp(-ax); |
| |
| // Accumulate 1st order derivatives. |
| if (jacobians) { |
| for (int j = 0; j < arity_; ++j) { |
| if (jacobians[j]) { |
| for (int u = 0; u < parameter_block_sizes()[j]; ++u) { |
| // See comments before class. |
| jacobians[j][u] = -f * a_[j][u]; |
| } |
| } |
| } |
| } |
| |
| return true; |
| } |
| |
| void SetReturnValue(bool return_value) { return_value_ = return_value; } |
| |
| private: |
| int arity_; |
| bool return_value_; |
| std::vector<std::vector<double>> a_; // our vectors. |
| }; |
| |
| class BadTestTerm : public CostFunction { |
| public: |
| template <class UniformRandomFunctor> |
| BadTestTerm(int arity, int const* dim, UniformRandomFunctor&& randu) |
| : arity_(arity) { |
| // Make 'arity' random vectors. |
| a_.resize(arity_); |
| for (int j = 0; j < arity_; ++j) { |
| a_[j].resize(dim[j]); |
| for (int u = 0; u < dim[j]; ++u) { |
| a_[j][u] = randu(); |
| } |
| } |
| |
| for (int i = 0; i < arity_; i++) { |
| mutable_parameter_block_sizes()->push_back(dim[i]); |
| } |
| set_num_residuals(1); |
| } |
| |
| bool Evaluate(double const* const* parameters, |
| double* residuals, |
| double** jacobians) const override { |
| // Compute a . x. |
| double ax = 0; |
| for (int j = 0; j < arity_; ++j) { |
| for (int u = 0; u < parameter_block_sizes()[j]; ++u) { |
| ax += a_[j][u] * parameters[j][u]; |
| } |
| } |
| |
| // This is the cost, but also appears as a factor |
| // in the derivatives. |
| double f = *residuals = exp(-ax); |
| |
| // Accumulate 1st order derivatives. |
| if (jacobians) { |
| for (int j = 0; j < arity_; ++j) { |
| if (jacobians[j]) { |
| for (int u = 0; u < parameter_block_sizes()[j]; ++u) { |
| // See comments before class. |
| jacobians[j][u] = -f * a_[j][u] + kTolerance; |
| } |
| } |
| } |
| } |
| |
| return true; |
| } |
| |
| private: |
| int arity_; |
| std::vector<std::vector<double>> a_; // our vectors. |
| }; |
| |
| static void CheckDimensions(const GradientChecker::ProbeResults& results, |
| const std::vector<int>& parameter_sizes, |
| const std::vector<int>& local_parameter_sizes, |
| int residual_size) { |
| CHECK_EQ(parameter_sizes.size(), local_parameter_sizes.size()); |
| int num_parameters = parameter_sizes.size(); |
| ASSERT_EQ(residual_size, results.residuals.size()); |
| ASSERT_EQ(num_parameters, results.local_jacobians.size()); |
| ASSERT_EQ(num_parameters, results.local_numeric_jacobians.size()); |
| ASSERT_EQ(num_parameters, results.jacobians.size()); |
| ASSERT_EQ(num_parameters, results.numeric_jacobians.size()); |
| for (int i = 0; i < num_parameters; ++i) { |
| EXPECT_EQ(residual_size, results.local_jacobians.at(i).rows()); |
| EXPECT_EQ(local_parameter_sizes[i], results.local_jacobians.at(i).cols()); |
| EXPECT_EQ(residual_size, results.local_numeric_jacobians.at(i).rows()); |
| EXPECT_EQ(local_parameter_sizes[i], |
| results.local_numeric_jacobians.at(i).cols()); |
| EXPECT_EQ(residual_size, results.jacobians.at(i).rows()); |
| EXPECT_EQ(parameter_sizes[i], results.jacobians.at(i).cols()); |
| EXPECT_EQ(residual_size, results.numeric_jacobians.at(i).rows()); |
| EXPECT_EQ(parameter_sizes[i], results.numeric_jacobians.at(i).cols()); |
| } |
| } |
| |
| TEST(GradientChecker, SmokeTest) { |
| // Test with 3 blocks of size 2, 3 and 4. |
| int const num_parameters = 3; |
| std::vector<int> parameter_sizes(3); |
| parameter_sizes[0] = 2; |
| parameter_sizes[1] = 3; |
| parameter_sizes[2] = 4; |
| |
| // Make a random set of blocks. |
| FixedArray<double*> parameters(num_parameters); |
| std::mt19937 prng; |
| std::uniform_real_distribution<double> distribution(-1.0, 1.0); |
| auto randu = [&prng, &distribution] { return distribution(prng); }; |
| for (int j = 0; j < num_parameters; ++j) { |
| parameters[j] = new double[parameter_sizes[j]]; |
| for (int u = 0; u < parameter_sizes[j]; ++u) { |
| parameters[j][u] = randu(); |
| } |
| } |
| |
| NumericDiffOptions numeric_diff_options; |
| GradientChecker::ProbeResults results; |
| |
| // Test that Probe returns true for correct Jacobians. |
| GoodTestTerm good_term(num_parameters, parameter_sizes.data(), randu); |
| std::vector<const Manifold*>* manifolds = nullptr; |
| GradientChecker good_gradient_checker( |
| &good_term, manifolds, numeric_diff_options); |
| EXPECT_TRUE( |
| good_gradient_checker.Probe(parameters.data(), kTolerance, nullptr)); |
| EXPECT_TRUE( |
| good_gradient_checker.Probe(parameters.data(), kTolerance, &results)) |
| << results.error_log; |
| |
| // Check that results contain sensible data. |
| ASSERT_EQ(results.return_value, true); |
| ASSERT_EQ(results.residuals.size(), 1); |
| CheckDimensions(results, parameter_sizes, parameter_sizes, 1); |
| EXPECT_GE(results.maximum_relative_error, 0.0); |
| EXPECT_TRUE(results.error_log.empty()); |
| |
| // Test that if the cost function return false, Probe should return false. |
| good_term.SetReturnValue(false); |
| EXPECT_FALSE( |
| good_gradient_checker.Probe(parameters.data(), kTolerance, nullptr)); |
| EXPECT_FALSE( |
| good_gradient_checker.Probe(parameters.data(), kTolerance, &results)) |
| << results.error_log; |
| |
| // Check that results contain sensible data. |
| ASSERT_EQ(results.return_value, false); |
| ASSERT_EQ(results.residuals.size(), 1); |
| CheckDimensions(results, parameter_sizes, parameter_sizes, 1); |
| for (int i = 0; i < num_parameters; ++i) { |
| EXPECT_EQ(results.local_jacobians.at(i).norm(), 0); |
| EXPECT_EQ(results.local_numeric_jacobians.at(i).norm(), 0); |
| } |
| EXPECT_EQ(results.maximum_relative_error, 0.0); |
| EXPECT_FALSE(results.error_log.empty()); |
| |
| // Test that Probe returns false for incorrect Jacobians. |
| BadTestTerm bad_term(num_parameters, parameter_sizes.data(), randu); |
| GradientChecker bad_gradient_checker( |
| &bad_term, manifolds, numeric_diff_options); |
| EXPECT_FALSE( |
| bad_gradient_checker.Probe(parameters.data(), kTolerance, nullptr)); |
| EXPECT_FALSE( |
| bad_gradient_checker.Probe(parameters.data(), kTolerance, &results)); |
| |
| // Check that results contain sensible data. |
| ASSERT_EQ(results.return_value, true); |
| ASSERT_EQ(results.residuals.size(), 1); |
| CheckDimensions(results, parameter_sizes, parameter_sizes, 1); |
| EXPECT_GT(results.maximum_relative_error, kTolerance); |
| EXPECT_FALSE(results.error_log.empty()); |
| |
| // Setting a high threshold should make the test pass. |
| EXPECT_TRUE(bad_gradient_checker.Probe(parameters.data(), 1.0, &results)); |
| |
| // Check that results contain sensible data. |
| ASSERT_EQ(results.return_value, true); |
| ASSERT_EQ(results.residuals.size(), 1); |
| CheckDimensions(results, parameter_sizes, parameter_sizes, 1); |
| EXPECT_GT(results.maximum_relative_error, 0.0); |
| EXPECT_TRUE(results.error_log.empty()); |
| |
| for (int j = 0; j < num_parameters; j++) { |
| delete[] parameters[j]; |
| } |
| } |
| |
| /** |
| * Helper cost function that multiplies the parameters by the given jacobians |
| * and adds a constant offset. |
| */ |
| class LinearCostFunction : public CostFunction { |
| public: |
| explicit LinearCostFunction(Vector residuals_offset) |
| : residuals_offset_(std::move(residuals_offset)) { |
| set_num_residuals(residuals_offset_.size()); |
| } |
| |
| bool Evaluate(double const* const* parameter_ptrs, |
| double* residuals_ptr, |
| double** residual_J_params) const final { |
| CHECK_GE(residual_J_params_.size(), 0.0); |
| VectorRef residuals(residuals_ptr, residual_J_params_[0].rows()); |
| residuals = residuals_offset_; |
| |
| for (size_t i = 0; i < residual_J_params_.size(); ++i) { |
| const Matrix& residual_J_param = residual_J_params_[i]; |
| int parameter_size = residual_J_param.cols(); |
| ConstVectorRef param(parameter_ptrs[i], parameter_size); |
| |
| // Compute residual. |
| residuals += residual_J_param * param; |
| |
| // Return Jacobian. |
| if (residual_J_params != nullptr && residual_J_params[i] != nullptr) { |
| Eigen::Map<Matrix> residual_J_param_out(residual_J_params[i], |
| residual_J_param.rows(), |
| residual_J_param.cols()); |
| if (jacobian_offsets_.count(i) != 0) { |
| residual_J_param_out = residual_J_param + jacobian_offsets_.at(i); |
| } else { |
| residual_J_param_out = residual_J_param; |
| } |
| } |
| } |
| return true; |
| } |
| |
| void AddParameter(const Matrix& residual_J_param) { |
| CHECK_EQ(num_residuals(), residual_J_param.rows()); |
| residual_J_params_.push_back(residual_J_param); |
| mutable_parameter_block_sizes()->push_back(residual_J_param.cols()); |
| } |
| |
| /// Add offset to the given Jacobian before returning it from Evaluate(), |
| /// thus introducing an error in the computation. |
| void SetJacobianOffset(size_t index, Matrix offset) { |
| CHECK_LT(index, residual_J_params_.size()); |
| CHECK_EQ(residual_J_params_[index].rows(), offset.rows()); |
| CHECK_EQ(residual_J_params_[index].cols(), offset.cols()); |
| jacobian_offsets_[index] = offset; |
| } |
| |
| private: |
| std::vector<Matrix> residual_J_params_; |
| std::map<int, Matrix> jacobian_offsets_; |
| Vector residuals_offset_; |
| }; |
| |
| // Helper function to compare two Eigen matrices (used in the test below). |
| static void ExpectMatricesClose(Matrix p, Matrix q, double tolerance) { |
| ASSERT_EQ(p.rows(), q.rows()); |
| ASSERT_EQ(p.cols(), q.cols()); |
| ExpectArraysClose(p.size(), p.data(), q.data(), tolerance); |
| } |
| |
| // Helper manifold that multiplies the delta vector by the given |
| // jacobian and adds it to the parameter. |
| class MatrixManifold : public Manifold { |
| public: |
| bool Plus(const double* x, |
| const double* delta, |
| double* x_plus_delta) const final { |
| VectorRef(x_plus_delta, AmbientSize()) = |
| ConstVectorRef(x, AmbientSize()) + |
| (global_to_local_ * ConstVectorRef(delta, TangentSize())); |
| return true; |
| } |
| |
| bool PlusJacobian(const double* /*x*/, double* jacobian) const final { |
| MatrixRef(jacobian, AmbientSize(), TangentSize()) = global_to_local_; |
| return true; |
| } |
| |
| bool Minus(const double* y, const double* x, double* y_minus_x) const final { |
| LOG(FATAL) << "Should not be called"; |
| return true; |
| } |
| |
| bool MinusJacobian(const double* x, double* jacobian) const final { |
| LOG(FATAL) << "Should not be called"; |
| return true; |
| } |
| |
| int AmbientSize() const final { return global_to_local_.rows(); } |
| int TangentSize() const final { return global_to_local_.cols(); } |
| |
| Matrix global_to_local_; |
| }; |
| |
| TEST(GradientChecker, TestCorrectnessWithManifolds) { |
| // Create cost function. |
| Eigen::Vector3d residual_offset(100.0, 200.0, 300.0); |
| LinearCostFunction cost_function(residual_offset); |
| Eigen::Matrix<double, 3, 3, Eigen::RowMajor> j0; |
| j0.row(0) << 1.0, 2.0, 3.0; |
| j0.row(1) << 4.0, 5.0, 6.0; |
| j0.row(2) << 7.0, 8.0, 9.0; |
| Eigen::Matrix<double, 3, 2, Eigen::RowMajor> j1; |
| j1.row(0) << 10.0, 11.0; |
| j1.row(1) << 12.0, 13.0; |
| j1.row(2) << 14.0, 15.0; |
| |
| Eigen::Vector3d param0(1.0, 2.0, 3.0); |
| Eigen::Vector2d param1(4.0, 5.0); |
| |
| cost_function.AddParameter(j0); |
| cost_function.AddParameter(j1); |
| |
| std::vector<int> parameter_sizes(2); |
| parameter_sizes[0] = 3; |
| parameter_sizes[1] = 2; |
| std::vector<int> tangent_sizes(2); |
| tangent_sizes[0] = 2; |
| tangent_sizes[1] = 2; |
| |
| // Test cost function for correctness. |
| Eigen::Matrix<double, 3, 3, Eigen::RowMajor> j1_out; |
| Eigen::Matrix<double, 3, 2, Eigen::RowMajor> j2_out; |
| Eigen::Vector3d residual; |
| std::vector<const double*> parameters(2); |
| parameters[0] = param0.data(); |
| parameters[1] = param1.data(); |
| std::vector<double*> jacobians(2); |
| jacobians[0] = j1_out.data(); |
| jacobians[1] = j2_out.data(); |
| cost_function.Evaluate(parameters.data(), residual.data(), jacobians.data()); |
| |
| Matrix residual_expected = residual_offset + j0 * param0 + j1 * param1; |
| |
| ExpectMatricesClose(j1_out, j0, std::numeric_limits<double>::epsilon()); |
| ExpectMatricesClose(j2_out, j1, std::numeric_limits<double>::epsilon()); |
| ExpectMatricesClose(residual, residual_expected, kTolerance); |
| |
| // Create manifold. |
| Eigen::Matrix<double, 3, 2, Eigen::RowMajor> global_to_local; |
| global_to_local.row(0) << 1.5, 2.5; |
| global_to_local.row(1) << 3.5, 4.5; |
| global_to_local.row(2) << 5.5, 6.5; |
| |
| MatrixManifold manifold; |
| manifold.global_to_local_ = global_to_local; |
| |
| // Test manifold for correctness. |
| Eigen::Vector3d x(7.0, 8.0, 9.0); |
| Eigen::Vector2d delta(10.0, 11.0); |
| |
| Eigen::Matrix<double, 3, 2, Eigen::RowMajor> global_to_local_out; |
| manifold.PlusJacobian(x.data(), global_to_local_out.data()); |
| ExpectMatricesClose(global_to_local_out, |
| global_to_local, |
| std::numeric_limits<double>::epsilon()); |
| |
| Eigen::Vector3d x_plus_delta; |
| manifold.Plus(x.data(), delta.data(), x_plus_delta.data()); |
| Eigen::Vector3d x_plus_delta_expected = x + (global_to_local * delta); |
| ExpectMatricesClose(x_plus_delta, x_plus_delta_expected, kTolerance); |
| |
| // Now test GradientChecker. |
| std::vector<const Manifold*> manifolds(2); |
| manifolds[0] = &manifold; |
| manifolds[1] = nullptr; |
| NumericDiffOptions numeric_diff_options; |
| GradientChecker::ProbeResults results; |
| GradientChecker gradient_checker( |
| &cost_function, &manifolds, numeric_diff_options); |
| |
| Problem::Options problem_options; |
| problem_options.cost_function_ownership = DO_NOT_TAKE_OWNERSHIP; |
| problem_options.manifold_ownership = DO_NOT_TAKE_OWNERSHIP; |
| Problem problem(problem_options); |
| Eigen::Vector3d param0_solver; |
| Eigen::Vector2d param1_solver; |
| problem.AddParameterBlock(param0_solver.data(), 3, &manifold); |
| problem.AddParameterBlock(param1_solver.data(), 2); |
| problem.AddResidualBlock( |
| &cost_function, nullptr, param0_solver.data(), param1_solver.data()); |
| |
| // First test case: everything is correct. |
| EXPECT_TRUE(gradient_checker.Probe(parameters.data(), kTolerance, nullptr)); |
| EXPECT_TRUE(gradient_checker.Probe(parameters.data(), kTolerance, &results)) |
| << results.error_log; |
| |
| // Check that results contain correct data. |
| ASSERT_EQ(results.return_value, true); |
| ExpectMatricesClose( |
| results.residuals, residual, std::numeric_limits<double>::epsilon()); |
| CheckDimensions(results, parameter_sizes, tangent_sizes, 3); |
| ExpectMatricesClose( |
| results.local_jacobians.at(0), j0 * global_to_local, kTolerance); |
| ExpectMatricesClose(results.local_jacobians.at(1), |
| j1, |
| std::numeric_limits<double>::epsilon()); |
| ExpectMatricesClose( |
| results.local_numeric_jacobians.at(0), j0 * global_to_local, kTolerance); |
| ExpectMatricesClose(results.local_numeric_jacobians.at(1), j1, kTolerance); |
| ExpectMatricesClose( |
| results.jacobians.at(0), j0, std::numeric_limits<double>::epsilon()); |
| ExpectMatricesClose( |
| results.jacobians.at(1), j1, std::numeric_limits<double>::epsilon()); |
| ExpectMatricesClose(results.numeric_jacobians.at(0), j0, kTolerance); |
| ExpectMatricesClose(results.numeric_jacobians.at(1), j1, kTolerance); |
| EXPECT_GE(results.maximum_relative_error, 0.0); |
| EXPECT_TRUE(results.error_log.empty()); |
| |
| // Test interaction with the 'check_gradients' option in Solver. |
| Solver::Options solver_options; |
| solver_options.linear_solver_type = DENSE_QR; |
| solver_options.check_gradients = true; |
| solver_options.initial_trust_region_radius = 1e10; |
| Solver solver; |
| Solver::Summary summary; |
| |
| param0_solver = param0; |
| param1_solver = param1; |
| solver.Solve(solver_options, &problem, &summary); |
| EXPECT_EQ(CONVERGENCE, summary.termination_type); |
| EXPECT_LE(summary.final_cost, 1e-12); |
| |
| // Second test case: Mess up reported derivatives with respect to 3rd |
| // component of 1st parameter. Check should fail. |
| Eigen::Matrix<double, 3, 3, Eigen::RowMajor> j0_offset; |
| j0_offset.setZero(); |
| j0_offset.col(2).setConstant(0.001); |
| cost_function.SetJacobianOffset(0, j0_offset); |
| EXPECT_FALSE(gradient_checker.Probe(parameters.data(), kTolerance, nullptr)); |
| EXPECT_FALSE(gradient_checker.Probe(parameters.data(), kTolerance, &results)) |
| << results.error_log; |
| |
| // Check that results contain correct data. |
| ASSERT_EQ(results.return_value, true); |
| ExpectMatricesClose( |
| results.residuals, residual, std::numeric_limits<double>::epsilon()); |
| CheckDimensions(results, parameter_sizes, tangent_sizes, 3); |
| ASSERT_EQ(results.local_jacobians.size(), 2); |
| ASSERT_EQ(results.local_numeric_jacobians.size(), 2); |
| ExpectMatricesClose(results.local_jacobians.at(0), |
| (j0 + j0_offset) * global_to_local, |
| kTolerance); |
| ExpectMatricesClose(results.local_jacobians.at(1), |
| j1, |
| std::numeric_limits<double>::epsilon()); |
| ExpectMatricesClose( |
| results.local_numeric_jacobians.at(0), j0 * global_to_local, kTolerance); |
| ExpectMatricesClose(results.local_numeric_jacobians.at(1), j1, kTolerance); |
| ExpectMatricesClose(results.jacobians.at(0), j0 + j0_offset, kTolerance); |
| ExpectMatricesClose( |
| results.jacobians.at(1), j1, std::numeric_limits<double>::epsilon()); |
| ExpectMatricesClose(results.numeric_jacobians.at(0), j0, kTolerance); |
| ExpectMatricesClose(results.numeric_jacobians.at(1), j1, kTolerance); |
| EXPECT_GT(results.maximum_relative_error, 0.0); |
| EXPECT_FALSE(results.error_log.empty()); |
| |
| // Test interaction with the 'check_gradients' option in Solver. |
| param0_solver = param0; |
| param1_solver = param1; |
| solver.Solve(solver_options, &problem, &summary); |
| EXPECT_EQ(FAILURE, summary.termination_type); |
| |
| // Now, zero out the manifold Jacobian with respect to the 3rd component of |
| // the 1st parameter. This makes the combination of cost function and manifold |
| // return correct values again. |
| manifold.global_to_local_.row(2).setZero(); |
| |
| // Verify that the gradient checker does not treat this as an error. |
| EXPECT_TRUE(gradient_checker.Probe(parameters.data(), kTolerance, &results)) |
| << results.error_log; |
| |
| // Check that results contain correct data. |
| ASSERT_EQ(results.return_value, true); |
| ExpectMatricesClose( |
| results.residuals, residual, std::numeric_limits<double>::epsilon()); |
| CheckDimensions(results, parameter_sizes, tangent_sizes, 3); |
| ASSERT_EQ(results.local_jacobians.size(), 2); |
| ASSERT_EQ(results.local_numeric_jacobians.size(), 2); |
| ExpectMatricesClose(results.local_jacobians.at(0), |
| (j0 + j0_offset) * manifold.global_to_local_, |
| kTolerance); |
| ExpectMatricesClose(results.local_jacobians.at(1), |
| j1, |
| std::numeric_limits<double>::epsilon()); |
| ExpectMatricesClose(results.local_numeric_jacobians.at(0), |
| j0 * manifold.global_to_local_, |
| kTolerance); |
| ExpectMatricesClose(results.local_numeric_jacobians.at(1), j1, kTolerance); |
| ExpectMatricesClose(results.jacobians.at(0), j0 + j0_offset, kTolerance); |
| ExpectMatricesClose( |
| results.jacobians.at(1), j1, std::numeric_limits<double>::epsilon()); |
| ExpectMatricesClose(results.numeric_jacobians.at(0), j0, kTolerance); |
| ExpectMatricesClose(results.numeric_jacobians.at(1), j1, kTolerance); |
| EXPECT_GE(results.maximum_relative_error, 0.0); |
| EXPECT_TRUE(results.error_log.empty()); |
| |
| // Test interaction with the 'check_gradients' option in Solver. |
| param0_solver = param0; |
| param1_solver = param1; |
| solver.Solve(solver_options, &problem, &summary); |
| EXPECT_EQ(CONVERGENCE, summary.termination_type); |
| EXPECT_LE(summary.final_cost, 1e-12); |
| } |
| } // namespace ceres::internal |