internal/ceres/gradient_checking_cost_function.cc - ceres-solver - Git at Google

 // 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.
 //
 // Authors: keir@google.com (Keir Mierle),
 //          dgossow@google.com (David Gossow)

 #include "ceres/gradient_checking_cost_function.h"

 #include <algorithm>
 #include <cmath>
 #include <cstdint>
 #include <memory>
 #include <numeric>
 #include <string>
 #include <utility>
 #include <vector>

 #include "absl/log/check.h"
 #include "absl/log/log.h"
 #include "ceres/dynamic_numeric_diff_cost_function.h"
 #include "ceres/gradient_checker.h"
 #include "ceres/internal/eigen.h"
 #include "ceres/parameter_block.h"
 #include "ceres/problem.h"
 #include "ceres/problem_impl.h"
 #include "ceres/program.h"
 #include "ceres/residual_block.h"
 #include "ceres/stringprintf.h"
 #include "ceres/types.h"

 namespace ceres::internal {

 namespace {

 class GradientCheckingCostFunction final : public CostFunction {
  public:
   GradientCheckingCostFunction(const CostFunction* function,
                                const std::vector<const Manifold*>* manifolds,
                                const NumericDiffOptions& options,
                                double relative_precision,
                                std::string extra_info,
                                GradientCheckingIterationCallback* callback)
       : function_(function),
         gradient_checker_(function, manifolds, options),
         relative_precision_(relative_precision),
         extra_info_(std::move(extra_info)),
         callback_(callback) {
     CHECK(callback_ != nullptr);
     const std::vector<int32_t>& parameter_block_sizes =
         function->parameter_block_sizes();
     *mutable_parameter_block_sizes() = parameter_block_sizes;
     set_num_residuals(function->num_residuals());
   }

   bool Evaluate(double const* const* parameters,
                 double* residuals,
                 double** jacobians) const final {
     if (!jacobians) {
       // Nothing to check in this case; just forward.
       return function_->Evaluate(parameters, residuals, nullptr);
     }

     GradientChecker::ProbeResults results;
     bool okay =
         gradient_checker_.Probe(parameters, relative_precision_, &results);

     // If the cost function returned false, there's nothing we can say about
     // the gradients.
     if (results.return_value == false) {
       return false;
     }

     // Copy the residuals.
     const int num_residuals = function_->num_residuals();
     MatrixRef(residuals, num_residuals, 1) = results.residuals;

     // Copy the original jacobian blocks into the jacobians array.
     const std::vector<int32_t>& block_sizes =
         function_->parameter_block_sizes();
     for (int k = 0; k < block_sizes.size(); k++) {
       if (jacobians[k] != nullptr) {
         MatrixRef(jacobians[k],
                   results.jacobians[k].rows(),
                   results.jacobians[k].cols()) = results.jacobians[k];
       }
     }

     if (!okay) {
       std::string error_log =
           "Gradient Error detected!\nExtra info for this residual: " +
           extra_info_ + "\n" + results.error_log;
       callback_->SetGradientErrorDetected(error_log);
     }
     return true;
   }

  private:
   const CostFunction* function_;
   GradientChecker gradient_checker_;
   double relative_precision_;
   std::string extra_info_;
   GradientCheckingIterationCallback* callback_;
 };

 }  // namespace

 GradientCheckingIterationCallback::GradientCheckingIterationCallback()
     : gradient_error_detected_(false) {}

 CallbackReturnType GradientCheckingIterationCallback::operator()(
     const IterationSummary& /*summary*/) {
   if (gradient_error_detected_) {
     LOG(ERROR) << "Gradient error detected. Terminating solver.";
     return SOLVER_ABORT;
   }
   return SOLVER_CONTINUE;
 }

 void GradientCheckingIterationCallback::SetGradientErrorDetected(
     std::string& error_log) {
   std::lock_guard<std::mutex> l(mutex_);
   gradient_error_detected_ = true;
   error_log_ += "\n" + error_log;
 }

 std::unique_ptr<CostFunction> CreateGradientCheckingCostFunction(
     const CostFunction* cost_function,
     const std::vector<const Manifold*>* manifolds,
     double relative_step_size,
     double relative_precision,
     const std::string& extra_info,
     GradientCheckingIterationCallback* callback) {
   NumericDiffOptions numeric_diff_options;
   numeric_diff_options.relative_step_size = relative_step_size;

   return std::make_unique<GradientCheckingCostFunction>(cost_function,
                                                         manifolds,
                                                         numeric_diff_options,
                                                         relative_precision,
                                                         extra_info,
                                                         callback);
 }

 std::unique_ptr<ProblemImpl> CreateGradientCheckingProblemImpl(
     ProblemImpl* problem_impl,
     double relative_step_size,
     double relative_precision,
     GradientCheckingIterationCallback* callback) {
   CHECK(callback != nullptr);
   // We create new CostFunctions by wrapping the original CostFunction in a
   // gradient checking CostFunction. So its okay for the ProblemImpl to take
   // ownership of it and destroy it. The LossFunctions and Manifolds are reused
   // and since they are owned by problem_impl, gradient_checking_problem_impl
   // should not take ownership of it.
   Problem::Options gradient_checking_problem_options;
   gradient_checking_problem_options.cost_function_ownership = TAKE_OWNERSHIP;
   gradient_checking_problem_options.loss_function_ownership =
       DO_NOT_TAKE_OWNERSHIP;
   gradient_checking_problem_options.manifold_ownership = DO_NOT_TAKE_OWNERSHIP;
   gradient_checking_problem_options.context = problem_impl->context();

   NumericDiffOptions numeric_diff_options;
   numeric_diff_options.relative_step_size = relative_step_size;

   auto gradient_checking_problem_impl =
       std::make_unique<ProblemImpl>(gradient_checking_problem_options);

   Program* program = problem_impl->mutable_program();

   // For every ParameterBlock in problem_impl, create a new parameter block with
   // the same manifold and constancy.
   const std::vector<ParameterBlock*>& parameter_blocks =
       program->parameter_blocks();
   for (auto* parameter_block : parameter_blocks) {
     gradient_checking_problem_impl->AddParameterBlock(
         parameter_block->mutable_user_state(),
         parameter_block->Size(),
         parameter_block->mutable_manifold());

     if (parameter_block->IsConstant()) {
       gradient_checking_problem_impl->SetParameterBlockConstant(
           parameter_block->mutable_user_state());
     }

     for (int i = 0; i < parameter_block->Size(); ++i) {
       gradient_checking_problem_impl->SetParameterUpperBound(
           parameter_block->mutable_user_state(),
           i,
           parameter_block->UpperBound(i));
       gradient_checking_problem_impl->SetParameterLowerBound(
           parameter_block->mutable_user_state(),
           i,
           parameter_block->LowerBound(i));
     }
   }

   // For every ResidualBlock in problem_impl, create a new
   // ResidualBlock by wrapping its CostFunction inside a
   // GradientCheckingCostFunction.
   const std::vector<ResidualBlock*>& residual_blocks =
       program->residual_blocks();
   for (int i = 0; i < residual_blocks.size(); ++i) {
     ResidualBlock* residual_block = residual_blocks[i];

     // Build a human readable string which identifies the
     // ResidualBlock. This is used by the GradientCheckingCostFunction
     // when logging debugging information.
     std::string extra_info =
         StringPrintf("Residual block id %d; depends on parameters [", i);
     std::vector<double*> parameter_blocks;
     std::vector<const Manifold*> manifolds;
     parameter_blocks.reserve(residual_block->NumParameterBlocks());
     manifolds.reserve(residual_block->NumParameterBlocks());
     for (int j = 0; j < residual_block->NumParameterBlocks(); ++j) {
       ParameterBlock* parameter_block = residual_block->parameter_blocks()[j];
       parameter_blocks.push_back(parameter_block->mutable_user_state());
       StringAppendF(&extra_info, "%p", parameter_block->mutable_user_state());
       extra_info += (j < residual_block->NumParameterBlocks() - 1) ? ", " : "]";
       manifolds.push_back(
           problem_impl->GetManifold(parameter_block->mutable_user_state()));
     }

     // Wrap the original CostFunction in a GradientCheckingCostFunction.
     CostFunction* gradient_checking_cost_function =
         new GradientCheckingCostFunction(residual_block->cost_function(),
                                          &manifolds,
                                          numeric_diff_options,
                                          relative_precision,
                                          extra_info,
                                          callback);

     // The const_cast is necessary because
     // ProblemImpl::AddResidualBlock can potentially take ownership of
     // the LossFunction, but in this case we are guaranteed that this
     // will not be the case, so this const_cast is harmless.
     gradient_checking_problem_impl->AddResidualBlock(
         gradient_checking_cost_function,
         const_cast<LossFunction*>(residual_block->loss_function()),
         parameter_blocks.data(),
         static_cast<int>(parameter_blocks.size()));
   }

   // Normally, when a problem is given to the solver, we guarantee
   // that the state pointers for each parameter block point to the
   // user provided data. Since we are creating this new problem from a
   // problem given to us at an arbitrary stage of the solve, we cannot
   // depend on this being the case, so we explicitly call
   // SetParameterBlockStatePtrsToUserStatePtrs to ensure that this is
   // the case.
   gradient_checking_problem_impl->mutable_program()
       ->SetParameterBlockStatePtrsToUserStatePtrs();

   return gradient_checking_problem_impl;
 }

 }  // namespace ceres::internal
	// 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.
	//
	// Authors: keir@google.com (Keir Mierle),
	// dgossow@google.com (David Gossow)

	#include "ceres/gradient_checking_cost_function.h"

	#include <algorithm>
	#include <cmath>
	#include <cstdint>
	#include <memory>
	#include <numeric>
	#include <string>
	#include <utility>
	#include <vector>

	#include "absl/log/check.h"
	#include "absl/log/log.h"
	#include "ceres/dynamic_numeric_diff_cost_function.h"
	#include "ceres/gradient_checker.h"
	#include "ceres/internal/eigen.h"
	#include "ceres/parameter_block.h"
	#include "ceres/problem.h"
	#include "ceres/problem_impl.h"
	#include "ceres/program.h"
	#include "ceres/residual_block.h"
	#include "ceres/stringprintf.h"
	#include "ceres/types.h"

	namespace ceres::internal {

	namespace {

	class GradientCheckingCostFunction final : public CostFunction {
	public:
	GradientCheckingCostFunction(const CostFunction* function,
	const std::vector<const Manifold> manifolds,
	const NumericDiffOptions& options,
	double relative_precision,
	std::string extra_info,
	GradientCheckingIterationCallback* callback)
	: function_(function),
	gradient_checker_(function, manifolds, options),
	relative_precision_(relative_precision),
	extra_info_(std::move(extra_info)),
	callback_(callback) {
	CHECK(callback_ != nullptr);
	const std::vector<int32_t>& parameter_block_sizes =
	function->parameter_block_sizes();
	*mutable_parameter_block_sizes() = parameter_block_sizes;
	set_num_residuals(function->num_residuals());
	}

	bool Evaluate(double const* const* parameters,
	double* residuals,
	double** jacobians) const final {
	if (!jacobians) {
	// Nothing to check in this case; just forward.
	return function_->Evaluate(parameters, residuals, nullptr);
	}

	GradientChecker::ProbeResults results;
	bool okay =
	gradient_checker_.Probe(parameters, relative_precision_, &results);

	// If the cost function returned false, there's nothing we can say about
	// the gradients.
	if (results.return_value == false) {
	return false;
	}

	// Copy the residuals.
	const int num_residuals = function_->num_residuals();
	MatrixRef(residuals, num_residuals, 1) = results.residuals;

	// Copy the original jacobian blocks into the jacobians array.
	const std::vector<int32_t>& block_sizes =
	function_->parameter_block_sizes();
	for (int k = 0; k < block_sizes.size(); k++) {
	if (jacobians[k] != nullptr) {
	MatrixRef(jacobians[k],
	results.jacobians[k].rows(),
	results.jacobians[k].cols()) = results.jacobians[k];
	}
	}

	if (!okay) {
	std::string error_log =
	"Gradient Error detected!\nExtra info for this residual: " +
	extra_info_ + "\n" + results.error_log;
	callback_->SetGradientErrorDetected(error_log);
	}
	return true;
	}

	private:
	const CostFunction* function_;
	GradientChecker gradient_checker_;
	double relative_precision_;
	std::string extra_info_;
	GradientCheckingIterationCallback* callback_;
	};

	} // namespace

	GradientCheckingIterationCallback::GradientCheckingIterationCallback()
	: gradient_error_detected_(false) {}

	CallbackReturnType GradientCheckingIterationCallback::operator()(
	const IterationSummary& /summary/) {
	if (gradient_error_detected_) {
	LOG(ERROR) << "Gradient error detected. Terminating solver.";
	return SOLVER_ABORT;
	}
	return SOLVER_CONTINUE;
	}

	void GradientCheckingIterationCallback::SetGradientErrorDetected(
	std::string& error_log) {
	std::lock_guard<std::mutex> l(mutex_);
	gradient_error_detected_ = true;
	error_log_ += "\n" + error_log;
	}

	std::unique_ptr<CostFunction> CreateGradientCheckingCostFunction(
	const CostFunction* cost_function,
	const std::vector<const Manifold> manifolds,
	double relative_step_size,
	double relative_precision,
	const std::string& extra_info,
	GradientCheckingIterationCallback* callback) {
	NumericDiffOptions numeric_diff_options;
	numeric_diff_options.relative_step_size = relative_step_size;

	return std::make_unique<GradientCheckingCostFunction>(cost_function,
	manifolds,
	numeric_diff_options,
	relative_precision,
	extra_info,
	callback);
	}

	std::unique_ptr<ProblemImpl> CreateGradientCheckingProblemImpl(
	ProblemImpl* problem_impl,
	double relative_step_size,
	double relative_precision,
	GradientCheckingIterationCallback* callback) {
	CHECK(callback != nullptr);
	// We create new CostFunctions by wrapping the original CostFunction in a
	// gradient checking CostFunction. So its okay for the ProblemImpl to take
	// ownership of it and destroy it. The LossFunctions and Manifolds are reused
	// and since they are owned by problem_impl, gradient_checking_problem_impl
	// should not take ownership of it.
	Problem::Options gradient_checking_problem_options;
	gradient_checking_problem_options.cost_function_ownership = TAKE_OWNERSHIP;
	gradient_checking_problem_options.loss_function_ownership =
	DO_NOT_TAKE_OWNERSHIP;
	gradient_checking_problem_options.manifold_ownership = DO_NOT_TAKE_OWNERSHIP;
	gradient_checking_problem_options.context = problem_impl->context();

	NumericDiffOptions numeric_diff_options;
	numeric_diff_options.relative_step_size = relative_step_size;

	auto gradient_checking_problem_impl =
	std::make_unique<ProblemImpl>(gradient_checking_problem_options);

	Program* program = problem_impl->mutable_program();

	// For every ParameterBlock in problem_impl, create a new parameter block with
	// the same manifold and constancy.
	const std::vector<ParameterBlock*>& parameter_blocks =
	program->parameter_blocks();
	for (auto* parameter_block : parameter_blocks) {
	gradient_checking_problem_impl->AddParameterBlock(
	parameter_block->mutable_user_state(),
	parameter_block->Size(),
	parameter_block->mutable_manifold());

	if (parameter_block->IsConstant()) {
	gradient_checking_problem_impl->SetParameterBlockConstant(
	parameter_block->mutable_user_state());
	}

	for (int i = 0; i < parameter_block->Size(); ++i) {
	gradient_checking_problem_impl->SetParameterUpperBound(
	parameter_block->mutable_user_state(),
	i,
	parameter_block->UpperBound(i));
	gradient_checking_problem_impl->SetParameterLowerBound(
	parameter_block->mutable_user_state(),
	i,
	parameter_block->LowerBound(i));
	}
	}

	// For every ResidualBlock in problem_impl, create a new
	// ResidualBlock by wrapping its CostFunction inside a
	// GradientCheckingCostFunction.
	const std::vector<ResidualBlock*>& residual_blocks =
	program->residual_blocks();
	for (int i = 0; i < residual_blocks.size(); ++i) {
	ResidualBlock* residual_block = residual_blocks[i];

	// Build a human readable string which identifies the
	// ResidualBlock. This is used by the GradientCheckingCostFunction
	// when logging debugging information.
	std::string extra_info =
	StringPrintf("Residual block id %d; depends on parameters [", i);
	std::vector<double*> parameter_blocks;
	std::vector<const Manifold*> manifolds;
	parameter_blocks.reserve(residual_block->NumParameterBlocks());
	manifolds.reserve(residual_block->NumParameterBlocks());
	for (int j = 0; j < residual_block->NumParameterBlocks(); ++j) {
	ParameterBlock* parameter_block = residual_block->parameter_blocks()[j];
	parameter_blocks.push_back(parameter_block->mutable_user_state());
	StringAppendF(&extra_info, "%p", parameter_block->mutable_user_state());
	extra_info += (j < residual_block->NumParameterBlocks() - 1) ? ", " : "]";
	manifolds.push_back(
	problem_impl->GetManifold(parameter_block->mutable_user_state()));
	}

	// Wrap the original CostFunction in a GradientCheckingCostFunction.
	CostFunction* gradient_checking_cost_function =
	new GradientCheckingCostFunction(residual_block->cost_function(),
	&manifolds,
	numeric_diff_options,
	relative_precision,
	extra_info,
	callback);

	// The const_cast is necessary because
	// ProblemImpl::AddResidualBlock can potentially take ownership of
	// the LossFunction, but in this case we are guaranteed that this
	// will not be the case, so this const_cast is harmless.
	gradient_checking_problem_impl->AddResidualBlock(
	gradient_checking_cost_function,
	const_cast<LossFunction*>(residual_block->loss_function()),
	parameter_blocks.data(),
	static_cast<int>(parameter_blocks.size()));
	}

	// Normally, when a problem is given to the solver, we guarantee
	// that the state pointers for each parameter block point to the
	// user provided data. Since we are creating this new problem from a
	// problem given to us at an arbitrary stage of the solve, we cannot
	// depend on this being the case, so we explicitly call
	// SetParameterBlockStatePtrsToUserStatePtrs to ensure that this is
	// the case.
	gradient_checking_problem_impl->mutable_program()
	->SetParameterBlockStatePtrsToUserStatePtrs();

	return gradient_checking_problem_impl;
	}

	} // namespace ceres::internal