| // Ceres Solver - A fast non-linear least squares minimizer |
| // Copyright 2022 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: keir@google.com (Keir Mierle) |
| // |
| // The ProgramEvaluator runs the cost functions contained in each residual block |
| // and stores the result into a jacobian. The particular type of jacobian is |
| // abstracted out using two template parameters: |
| // |
| // - An "EvaluatePreparer" that is responsible for creating the array with |
| // pointers to the jacobian blocks where the cost function evaluates to. |
| // - A "JacobianWriter" that is responsible for storing the resulting |
| // jacobian blocks in the passed sparse matrix. |
| // |
| // This abstraction affords an efficient evaluator implementation while still |
| // supporting writing to multiple sparse matrix formats. For example, when the |
| // ProgramEvaluator is parameterized for writing to block sparse matrices, the |
| // residual jacobians are written directly into their final position in the |
| // block sparse matrix by the user's CostFunction; there is no copying. |
| // |
| // The evaluation is threaded with OpenMP or C++ threads. |
| // |
| // The EvaluatePreparer and JacobianWriter interfaces are as follows: |
| // |
| // class EvaluatePreparer { |
| // // Prepare the jacobians array for use as the destination of a call to |
| // // a cost function's evaluate method. |
| // void Prepare(const ResidualBlock* residual_block, |
| // int residual_block_index, |
| // SparseMatrix* jacobian, |
| // double** jacobians); |
| // } |
| // |
| // class JacobianWriter { |
| // // Create a jacobian that this writer can write. Same as |
| // // Evaluator::CreateJacobian. |
| // std::unique_ptr<SparseMatrix> CreateJacobian() const; |
| // |
| // // Create num_threads evaluate preparers.Resulting preparers are valid |
| // // while *this is. |
| // |
| // std::unique_ptr<EvaluatePreparer[]> CreateEvaluatePreparers( |
| // int num_threads); |
| // |
| // // Write the block jacobians from a residual block evaluation to the |
| // // larger sparse jacobian. |
| // void Write(int residual_id, |
| // int residual_offset, |
| // double** jacobians, |
| // SparseMatrix* jacobian); |
| // } |
| // |
| // Note: The ProgramEvaluator is not thread safe, since internally it maintains |
| // some per-thread scratch space. |
| |
| #ifndef CERES_INTERNAL_PROGRAM_EVALUATOR_H_ |
| #define CERES_INTERNAL_PROGRAM_EVALUATOR_H_ |
| |
| // This include must come before any #ifndef check on Ceres compile options. |
| // clang-format off |
| #include "ceres/internal/config.h" |
| // clang-format on |
| |
| #include <atomic> |
| #include <map> |
| #include <memory> |
| #include <string> |
| #include <vector> |
| |
| #include "ceres/evaluation_callback.h" |
| #include "ceres/execution_summary.h" |
| #include "ceres/internal/eigen.h" |
| #include "ceres/parallel_for.h" |
| #include "ceres/parameter_block.h" |
| #include "ceres/program.h" |
| #include "ceres/residual_block.h" |
| #include "ceres/small_blas.h" |
| |
| namespace ceres { |
| namespace internal { |
| |
| struct NullJacobianFinalizer { |
| void operator()(SparseMatrix* jacobian, int num_parameters) {} |
| }; |
| |
| template <typename EvaluatePreparer, |
| typename JacobianWriter, |
| typename JacobianFinalizer = NullJacobianFinalizer> |
| class ProgramEvaluator final : public Evaluator { |
| public: |
| ProgramEvaluator(const Evaluator::Options& options, Program* program) |
| : options_(options), |
| program_(program), |
| jacobian_writer_(options, program), |
| evaluate_preparers_(std::move( |
| jacobian_writer_.CreateEvaluatePreparers(options.num_threads))) { |
| #ifdef CERES_NO_THREADS |
| if (options_.num_threads > 1) { |
| LOG(WARNING) << "No threading support is compiled into this binary; " |
| << "only options.num_threads = 1 is supported. Switching " |
| << "to single threaded mode."; |
| options_.num_threads = 1; |
| } |
| #endif // CERES_NO_THREADS |
| |
| BuildResidualLayout(*program, &residual_layout_); |
| evaluate_scratch_ = std::move(CreateEvaluatorScratch( |
| *program, static_cast<unsigned>(options.num_threads))); |
| } |
| |
| // Implementation of Evaluator interface. |
| std::unique_ptr<SparseMatrix> CreateJacobian() const final { |
| return jacobian_writer_.CreateJacobian(); |
| } |
| |
| bool Evaluate(const Evaluator::EvaluateOptions& evaluate_options, |
| const double* state, |
| double* cost, |
| double* residuals, |
| double* gradient, |
| SparseMatrix* jacobian) final { |
| ScopedExecutionTimer total_timer("Evaluator::Total", &execution_summary_); |
| ScopedExecutionTimer call_type_timer( |
| gradient == nullptr && jacobian == nullptr ? "Evaluator::Residual" |
| : "Evaluator::Jacobian", |
| &execution_summary_); |
| |
| // The parameters are stateful, so set the state before evaluating. |
| if (!program_->StateVectorToParameterBlocks(state)) { |
| return false; |
| } |
| |
| // Notify the user about a new evaluation point if they are interested. |
| if (options_.evaluation_callback != nullptr) { |
| program_->CopyParameterBlockStateToUserState(); |
| options_.evaluation_callback->PrepareForEvaluation( |
| /*jacobians=*/(gradient != nullptr || jacobian != nullptr), |
| evaluate_options.new_evaluation_point); |
| } |
| |
| if (residuals != nullptr) { |
| VectorRef(residuals, program_->NumResiduals()).setZero(); |
| } |
| |
| if (jacobian != nullptr) { |
| jacobian->SetZero(); |
| } |
| |
| // Each thread gets it's own cost and evaluate scratch space. |
| for (int i = 0; i < options_.num_threads; ++i) { |
| evaluate_scratch_[i].cost = 0.0; |
| if (gradient != nullptr) { |
| VectorRef(evaluate_scratch_[i].gradient.get(), |
| program_->NumEffectiveParameters()) |
| .setZero(); |
| } |
| } |
| |
| const int num_residual_blocks = program_->NumResidualBlocks(); |
| // This bool is used to disable the loop if an error is encountered without |
| // breaking out of it. The remaining loop iterations are still run, but with |
| // an empty body, and so will finish quickly. |
| std::atomic_bool abort(false); |
| ParallelFor( |
| options_.context, |
| 0, |
| num_residual_blocks, |
| options_.num_threads, |
| [&](int thread_id, int i) { |
| if (abort) { |
| return; |
| } |
| |
| EvaluatePreparer* preparer = &evaluate_preparers_[thread_id]; |
| EvaluateScratch* scratch = &evaluate_scratch_[thread_id]; |
| |
| // Prepare block residuals if requested. |
| const ResidualBlock* residual_block = program_->residual_blocks()[i]; |
| double* block_residuals = nullptr; |
| if (residuals != nullptr) { |
| block_residuals = residuals + residual_layout_[i]; |
| } else if (gradient != nullptr) { |
| block_residuals = scratch->residual_block_residuals.get(); |
| } |
| |
| // Prepare block jacobians if requested. |
| double** block_jacobians = nullptr; |
| if (jacobian != nullptr || gradient != nullptr) { |
| preparer->Prepare(residual_block, |
| i, |
| jacobian, |
| scratch->jacobian_block_ptrs.get()); |
| block_jacobians = scratch->jacobian_block_ptrs.get(); |
| } |
| |
| // Evaluate the cost, residuals, and jacobians. |
| double block_cost; |
| if (!residual_block->Evaluate( |
| evaluate_options.apply_loss_function, |
| &block_cost, |
| block_residuals, |
| block_jacobians, |
| scratch->residual_block_evaluate_scratch.get())) { |
| abort = true; |
| return; |
| } |
| |
| scratch->cost += block_cost; |
| |
| // Store the jacobians, if they were requested. |
| if (jacobian != nullptr) { |
| jacobian_writer_.Write( |
| i, residual_layout_[i], block_jacobians, jacobian); |
| } |
| |
| // Compute and store the gradient, if it was requested. |
| if (gradient != nullptr) { |
| int num_residuals = residual_block->NumResiduals(); |
| int num_parameter_blocks = residual_block->NumParameterBlocks(); |
| for (int j = 0; j < num_parameter_blocks; ++j) { |
| const ParameterBlock* parameter_block = |
| residual_block->parameter_blocks()[j]; |
| if (parameter_block->IsConstant()) { |
| continue; |
| } |
| |
| MatrixTransposeVectorMultiply<Eigen::Dynamic, Eigen::Dynamic, 1>( |
| block_jacobians[j], |
| num_residuals, |
| parameter_block->TangentSize(), |
| block_residuals, |
| scratch->gradient.get() + parameter_block->delta_offset()); |
| } |
| } |
| }); |
| |
| if (!abort) { |
| const int num_parameters = program_->NumEffectiveParameters(); |
| |
| // Sum the cost and gradient (if requested) from each thread. |
| (*cost) = 0.0; |
| if (gradient != nullptr) { |
| VectorRef(gradient, num_parameters).setZero(); |
| } |
| for (int i = 0; i < options_.num_threads; ++i) { |
| (*cost) += evaluate_scratch_[i].cost; |
| if (gradient != nullptr) { |
| VectorRef(gradient, num_parameters) += |
| VectorRef(evaluate_scratch_[i].gradient.get(), num_parameters); |
| } |
| } |
| |
| // Finalize the Jacobian if it is available. |
| // `num_parameters` is passed to the finalizer so that additional |
| // storage can be reserved for additional diagonal elements if |
| // necessary. |
| if (jacobian != nullptr) { |
| JacobianFinalizer f; |
| f(jacobian, num_parameters); |
| } |
| } |
| return !abort; |
| } |
| |
| bool Plus(const double* state, |
| const double* delta, |
| double* state_plus_delta) const final { |
| return program_->Plus(state, delta, state_plus_delta); |
| } |
| |
| int NumParameters() const final { return program_->NumParameters(); } |
| int NumEffectiveParameters() const final { |
| return program_->NumEffectiveParameters(); |
| } |
| |
| int NumResiduals() const final { return program_->NumResiduals(); } |
| |
| std::map<std::string, CallStatistics> Statistics() const final { |
| return execution_summary_.statistics(); |
| } |
| |
| private: |
| // Per-thread scratch space needed to evaluate and store each residual block. |
| struct EvaluateScratch { |
| void Init(int max_parameters_per_residual_block, |
| int max_scratch_doubles_needed_for_evaluate, |
| int max_residuals_per_residual_block, |
| int num_parameters) { |
| residual_block_evaluate_scratch = |
| std::make_unique<double[]>(max_scratch_doubles_needed_for_evaluate); |
| gradient = std::make_unique<double[]>(num_parameters); |
| VectorRef(gradient.get(), num_parameters).setZero(); |
| residual_block_residuals = |
| std::make_unique<double[]>(max_residuals_per_residual_block); |
| jacobian_block_ptrs = |
| std::make_unique<double*[]>(max_parameters_per_residual_block); |
| } |
| |
| double cost; |
| std::unique_ptr<double[]> residual_block_evaluate_scratch; |
| // The gradient on the manifold. |
| std::unique_ptr<double[]> gradient; |
| // Enough space to store the residual for the largest residual block. |
| std::unique_ptr<double[]> residual_block_residuals; |
| std::unique_ptr<double*[]> jacobian_block_ptrs; |
| }; |
| |
| static void BuildResidualLayout(const Program& program, |
| std::vector<int>* residual_layout) { |
| const std::vector<ResidualBlock*>& residual_blocks = |
| program.residual_blocks(); |
| residual_layout->resize(program.NumResidualBlocks()); |
| int residual_pos = 0; |
| for (int i = 0; i < residual_blocks.size(); ++i) { |
| const int num_residuals = residual_blocks[i]->NumResiduals(); |
| (*residual_layout)[i] = residual_pos; |
| residual_pos += num_residuals; |
| } |
| } |
| |
| // Create scratch space for each thread evaluating the program. |
| static std::unique_ptr<EvaluateScratch[]> CreateEvaluatorScratch( |
| const Program& program, unsigned num_threads) { |
| int max_parameters_per_residual_block = |
| program.MaxParametersPerResidualBlock(); |
| int max_scratch_doubles_needed_for_evaluate = |
| program.MaxScratchDoublesNeededForEvaluate(); |
| int max_residuals_per_residual_block = |
| program.MaxResidualsPerResidualBlock(); |
| int num_parameters = program.NumEffectiveParameters(); |
| |
| auto evaluate_scratch = std::make_unique<EvaluateScratch[]>(num_threads); |
| for (int i = 0; i < num_threads; i++) { |
| evaluate_scratch[i].Init(max_parameters_per_residual_block, |
| max_scratch_doubles_needed_for_evaluate, |
| max_residuals_per_residual_block, |
| num_parameters); |
| } |
| return evaluate_scratch; |
| } |
| |
| Evaluator::Options options_; |
| Program* program_; |
| JacobianWriter jacobian_writer_; |
| std::unique_ptr<EvaluatePreparer[]> evaluate_preparers_; |
| std::unique_ptr<EvaluateScratch[]> evaluate_scratch_; |
| std::vector<int> residual_layout_; |
| ::ceres::internal::ExecutionSummary execution_summary_; |
| }; |
| |
| } // namespace internal |
| } // namespace ceres |
| |
| #endif // CERES_INTERNAL_PROGRAM_EVALUATOR_H_ |
