| 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154 | // 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.// Copyright 2023 Google Inc. All Rights Reserved.//// Authors: wjr@google.com (William Rucklidge),//          keir@google.com (Keir Mierle),//          dgossow@google.com (David Gossow)#ifndef CERES_PUBLIC_GRADIENT_CHECKER_H_#define CERES_PUBLIC_GRADIENT_CHECKER_H_#include <memory>#include <string>#include <vector>#include "ceres/cost_function.h"#include "ceres/dynamic_numeric_diff_cost_function.h"#include "ceres/internal/disable_warnings.h"#include "ceres/internal/eigen.h"#include "ceres/internal/export.h"#include "ceres/internal/fixed_array.h"#include "ceres/manifold.h"#include "glog/logging.h"namespace ceres {// GradientChecker compares the Jacobians returned by a cost function against// derivatives estimated using finite differencing.//// The condition enforced is that////    (J_actual(i, j) - J_numeric(i, j))//   ------------------------------------  <  relative_precision//   max(J_actual(i, j), J_numeric(i, j))//// where J_actual(i, j) is the Jacobian as computed by the supplied cost// function (by the user) multiplied by the manifold Jacobian and J_numeric is// the Jacobian as computed by finite differences, multiplied by the manifold// Jacobian as well.//// How to use: Fill in an array of pointers to parameter blocks for your// CostFunction, and then call Probe(). Check that the return value is 'true'.class CERES_EXPORT GradientChecker { public:  // This will not take ownership of the cost function or manifolds.  //  // function: The cost function to probe.  //  // manifolds: A vector of manifolds for each parameter. May be nullptr or  // contain nullptrs to indicate that the respective parameter blocks are  // Euclidean.  //  // options: Options to use for numerical differentiation.  GradientChecker(const CostFunction* function,                  const std::vector<const Manifold*>* manifolds,                  const NumericDiffOptions& options);  // Contains results from a call to Probe for later inspection.  struct CERES_EXPORT ProbeResults {    // The return value of the cost function.    bool return_value;    // Computed residual vector.    Vector residuals;    // The sizes of the Jacobians below are dictated by the cost function's    // parameter block size and residual block sizes. If a parameter block has a    // manifold associated with it, the size of the "local" Jacobian will be    // determined by the dimension of the manifold (which is the same as the    // dimension of the tangent space) and residual block size, otherwise it    // will be identical to the regular Jacobian.    // Derivatives as computed by the cost function.    std::vector<Matrix> jacobians;    // Derivatives as computed by the cost function in local space.    std::vector<Matrix> local_jacobians;    // Derivatives as computed by numerical differentiation in local space.    std::vector<Matrix> numeric_jacobians;    // Derivatives as computed by numerical differentiation in local space.    std::vector<Matrix> local_numeric_jacobians;    // Contains the maximum relative error found in the local Jacobians.    double maximum_relative_error;    // If an error was detected, this will contain a detailed description of    // that error.    std::string error_log;  };  // Call the cost function, compute alternative Jacobians using finite  // differencing and compare results. If manifolds are given, the Jacobians  // will be multiplied by the manifold Jacobians before performing the check,  // which effectively means that all errors along the null space of the  // manifold will be ignored.  Returns false if the Jacobians don't match, the  // cost function return false, or if a cost function returns a different  // residual when called with a Jacobian output argument vs. calling it  // without. Otherwise returns true.  //  // parameters: The parameter values at which to probe.  // relative_precision: A threshold for the relative difference between the  // Jacobians. If the Jacobians differ by more than this amount, then the  // probe fails.  // results: On return, the Jacobians (and other information) will be stored  // here. May be nullptr.  //  // Returns true if no problems are detected and the difference between the  // Jacobians is less than error_tolerance.  bool Probe(double const* const* parameters,             double relative_precision,             ProbeResults* results) const; private:  GradientChecker() = delete;  GradientChecker(const GradientChecker&) = delete;  void operator=(const GradientChecker&) = delete;  std::vector<const Manifold*> manifolds_;  const CostFunction* function_;  std::unique_ptr<CostFunction> finite_diff_cost_function_;};}  // namespace ceres#include "ceres/internal/reenable_warnings.h"#endif  // CERES_PUBLIC_GRADIENT_CHECKER_H_
 |