| // Ceres Solver - A fast non-linear least squares minimizer |
| // Copyright 2010, 2011, 2012 Google Inc. All rights reserved. |
| // http://code.google.com/p/ceres-solver/ |
| // |
| // 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) |
| |
| #include "ceres/solver_impl.h" |
| |
| #include <cstdio> |
| #include <iostream> // NOLINT |
| #include <numeric> |
| #include "ceres/coordinate_descent_minimizer.h" |
| #include "ceres/evaluator.h" |
| #include "ceres/gradient_checking_cost_function.h" |
| #include "ceres/iteration_callback.h" |
| #include "ceres/levenberg_marquardt_strategy.h" |
| #include "ceres/linear_solver.h" |
| #include "ceres/map_util.h" |
| #include "ceres/minimizer.h" |
| #include "ceres/ordered_groups.h" |
| #include "ceres/parameter_block.h" |
| #include "ceres/parameter_block_ordering.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/trust_region_minimizer.h" |
| #include "ceres/wall_time.h" |
| |
| namespace ceres { |
| namespace internal { |
| namespace { |
| |
| // Callback for updating the user's parameter blocks. Updates are only |
| // done if the step is successful. |
| class StateUpdatingCallback : public IterationCallback { |
| public: |
| StateUpdatingCallback(Program* program, double* parameters) |
| : program_(program), parameters_(parameters) {} |
| |
| CallbackReturnType operator()(const IterationSummary& summary) { |
| if (summary.step_is_successful) { |
| program_->StateVectorToParameterBlocks(parameters_); |
| program_->CopyParameterBlockStateToUserState(); |
| } |
| return SOLVER_CONTINUE; |
| } |
| |
| private: |
| Program* program_; |
| double* parameters_; |
| }; |
| |
| // Callback for logging the state of the minimizer to STDERR or STDOUT |
| // depending on the user's preferences and logging level. |
| class LoggingCallback : public IterationCallback { |
| public: |
| explicit LoggingCallback(bool log_to_stdout) |
| : log_to_stdout_(log_to_stdout) {} |
| |
| ~LoggingCallback() {} |
| |
| CallbackReturnType operator()(const IterationSummary& summary) { |
| const char* kReportRowFormat = |
| "% 4d: f:% 8e d:% 3.2e g:% 3.2e h:% 3.2e " |
| "rho:% 3.2e mu:% 3.2e li:% 3d it:% 3.2e tt:% 3.2e"; |
| string output = StringPrintf(kReportRowFormat, |
| summary.iteration, |
| summary.cost, |
| summary.cost_change, |
| summary.gradient_max_norm, |
| summary.step_norm, |
| summary.relative_decrease, |
| summary.trust_region_radius, |
| summary.linear_solver_iterations, |
| summary.iteration_time_in_seconds, |
| summary.cumulative_time_in_seconds); |
| if (log_to_stdout_) { |
| cout << output << endl; |
| } else { |
| VLOG(1) << output; |
| } |
| return SOLVER_CONTINUE; |
| } |
| |
| private: |
| const bool log_to_stdout_; |
| }; |
| |
| // Basic callback to record the execution of the solver to a file for |
| // offline analysis. |
| class FileLoggingCallback : public IterationCallback { |
| public: |
| explicit FileLoggingCallback(const string& filename) |
| : fptr_(NULL) { |
| fptr_ = fopen(filename.c_str(), "w"); |
| CHECK_NOTNULL(fptr_); |
| } |
| |
| virtual ~FileLoggingCallback() { |
| if (fptr_ != NULL) { |
| fclose(fptr_); |
| } |
| } |
| |
| virtual CallbackReturnType operator()(const IterationSummary& summary) { |
| fprintf(fptr_, |
| "%4d %e %e\n", |
| summary.iteration, |
| summary.cost, |
| summary.cumulative_time_in_seconds); |
| return SOLVER_CONTINUE; |
| } |
| private: |
| FILE* fptr_; |
| }; |
| |
| } // namespace |
| |
| void SolverImpl::Minimize(const Solver::Options& options, |
| Program* program, |
| CoordinateDescentMinimizer* inner_iteration_minimizer, |
| Evaluator* evaluator, |
| LinearSolver* linear_solver, |
| double* parameters, |
| Solver::Summary* summary) { |
| Minimizer::Options minimizer_options(options); |
| |
| // TODO(sameeragarwal): Add support for logging the configuration |
| // and more detailed stats. |
| scoped_ptr<IterationCallback> file_logging_callback; |
| if (!options.solver_log.empty()) { |
| file_logging_callback.reset(new FileLoggingCallback(options.solver_log)); |
| minimizer_options.callbacks.insert(minimizer_options.callbacks.begin(), |
| file_logging_callback.get()); |
| } |
| |
| LoggingCallback logging_callback(options.minimizer_progress_to_stdout); |
| if (options.logging_type != SILENT) { |
| minimizer_options.callbacks.insert(minimizer_options.callbacks.begin(), |
| &logging_callback); |
| } |
| |
| StateUpdatingCallback updating_callback(program, parameters); |
| if (options.update_state_every_iteration) { |
| // This must get pushed to the front of the callbacks so that it is run |
| // before any of the user callbacks. |
| minimizer_options.callbacks.insert(minimizer_options.callbacks.begin(), |
| &updating_callback); |
| } |
| |
| minimizer_options.evaluator = evaluator; |
| scoped_ptr<SparseMatrix> jacobian(evaluator->CreateJacobian()); |
| minimizer_options.jacobian = jacobian.get(); |
| minimizer_options.inner_iteration_minimizer = inner_iteration_minimizer; |
| |
| TrustRegionStrategy::Options trust_region_strategy_options; |
| trust_region_strategy_options.linear_solver = linear_solver; |
| trust_region_strategy_options.initial_radius = |
| options.initial_trust_region_radius; |
| trust_region_strategy_options.max_radius = options.max_trust_region_radius; |
| trust_region_strategy_options.lm_min_diagonal = options.lm_min_diagonal; |
| trust_region_strategy_options.lm_max_diagonal = options.lm_max_diagonal; |
| trust_region_strategy_options.trust_region_strategy_type = |
| options.trust_region_strategy_type; |
| trust_region_strategy_options.dogleg_type = options.dogleg_type; |
| scoped_ptr<TrustRegionStrategy> strategy( |
| TrustRegionStrategy::Create(trust_region_strategy_options)); |
| minimizer_options.trust_region_strategy = strategy.get(); |
| |
| TrustRegionMinimizer minimizer; |
| double minimizer_start_time = WallTimeInSeconds(); |
| minimizer.Minimize(minimizer_options, parameters, summary); |
| summary->minimizer_time_in_seconds = |
| WallTimeInSeconds() - minimizer_start_time; |
| } |
| |
| void SolverImpl::Solve(const Solver::Options& original_options, |
| ProblemImpl* original_problem_impl, |
| Solver::Summary* summary) { |
| double solver_start_time = WallTimeInSeconds(); |
| |
| Program* original_program = original_problem_impl->mutable_program(); |
| ProblemImpl* problem_impl = original_problem_impl; |
| |
| // Reset the summary object to its default values. |
| *CHECK_NOTNULL(summary) = Solver::Summary(); |
| |
| summary->num_parameter_blocks = problem_impl->NumParameterBlocks(); |
| summary->num_parameters = problem_impl->NumParameters(); |
| summary->num_residual_blocks = problem_impl->NumResidualBlocks(); |
| summary->num_residuals = problem_impl->NumResiduals(); |
| |
| // Empty programs are usually a user error. |
| if (summary->num_parameter_blocks == 0) { |
| summary->error = "Problem contains no parameter blocks."; |
| LOG(ERROR) << summary->error; |
| return; |
| } |
| |
| if (summary->num_residual_blocks == 0) { |
| summary->error = "Problem contains no residual blocks."; |
| LOG(ERROR) << summary->error; |
| return; |
| } |
| |
| Solver::Options options(original_options); |
| options.linear_solver_ordering = NULL; |
| options.inner_iteration_ordering = NULL; |
| |
| #ifndef CERES_USE_OPENMP |
| if (options.num_threads > 1) { |
| LOG(WARNING) |
| << "OpenMP support is not compiled into this binary; " |
| << "only options.num_threads=1 is supported. Switching " |
| << "to single threaded mode."; |
| options.num_threads = 1; |
| } |
| if (options.num_linear_solver_threads > 1) { |
| LOG(WARNING) |
| << "OpenMP support is not compiled into this binary; " |
| << "only options.num_linear_solver_threads=1 is supported. Switching " |
| << "to single threaded mode."; |
| options.num_linear_solver_threads = 1; |
| } |
| #endif |
| |
| summary->num_threads_given = original_options.num_threads; |
| summary->num_threads_used = options.num_threads; |
| |
| if (options.lsqp_iterations_to_dump.size() > 0) { |
| LOG(WARNING) << "Dumping linear least squares problems to disk is" |
| " currently broken. Ignoring Solver::Options::lsqp_iterations_to_dump"; |
| } |
| |
| // Evaluate the initial cost, residual vector and the jacobian |
| // matrix if requested by the user. The initial cost needs to be |
| // computed on the original unpreprocessed problem, as it is used to |
| // determine the value of the "fixed" part of the objective function |
| // after the problem has undergone reduction. |
| if (!Evaluator::Evaluate(original_program, |
| options.num_threads, |
| &(summary->initial_cost), |
| options.return_initial_residuals |
| ? &summary->initial_residuals |
| : NULL, |
| options.return_initial_gradient |
| ? &summary->initial_gradient |
| : NULL, |
| options.return_initial_jacobian |
| ? &summary->initial_jacobian |
| : NULL)) { |
| summary->termination_type = NUMERICAL_FAILURE; |
| summary->error = "Unable to evaluate the initial cost."; |
| LOG(ERROR) << summary->error; |
| return; |
| } |
| |
| original_program->SetParameterBlockStatePtrsToUserStatePtrs(); |
| |
| // If the user requests gradient checking, construct a new |
| // ProblemImpl by wrapping the CostFunctions of problem_impl inside |
| // GradientCheckingCostFunction and replacing problem_impl with |
| // gradient_checking_problem_impl. |
| scoped_ptr<ProblemImpl> gradient_checking_problem_impl; |
| if (options.check_gradients) { |
| VLOG(1) << "Checking Gradients"; |
| gradient_checking_problem_impl.reset( |
| CreateGradientCheckingProblemImpl( |
| problem_impl, |
| options.numeric_derivative_relative_step_size, |
| options.gradient_check_relative_precision)); |
| |
| // From here on, problem_impl will point to the gradient checking |
| // version. |
| problem_impl = gradient_checking_problem_impl.get(); |
| } |
| |
| if (original_options.linear_solver_ordering != NULL) { |
| if (!IsOrderingValid(original_options, problem_impl, &summary->error)) { |
| LOG(ERROR) << summary->error; |
| return; |
| } |
| options.linear_solver_ordering = |
| new ParameterBlockOrdering(*original_options.linear_solver_ordering); |
| } else { |
| options.linear_solver_ordering = new ParameterBlockOrdering; |
| const ProblemImpl::ParameterMap& parameter_map = |
| problem_impl->parameter_map(); |
| for (ProblemImpl::ParameterMap::const_iterator it = parameter_map.begin(); |
| it != parameter_map.end(); |
| ++it) { |
| options.linear_solver_ordering->AddElementToGroup(it->first, 0); |
| } |
| } |
| |
| // Create the three objects needed to minimize: the transformed program, the |
| // evaluator, and the linear solver. |
| scoped_ptr<Program> reduced_program(CreateReducedProgram(&options, |
| problem_impl, |
| &summary->fixed_cost, |
| &summary->error)); |
| if (reduced_program == NULL) { |
| return; |
| } |
| |
| summary->num_parameter_blocks_reduced = reduced_program->NumParameterBlocks(); |
| summary->num_parameters_reduced = reduced_program->NumParameters(); |
| summary->num_residual_blocks_reduced = reduced_program->NumResidualBlocks(); |
| summary->num_residuals_reduced = reduced_program->NumResiduals(); |
| |
| if (summary->num_parameter_blocks_reduced == 0) { |
| summary->preprocessor_time_in_seconds = |
| WallTimeInSeconds() - solver_start_time; |
| |
| LOG(INFO) << "Terminating: FUNCTION_TOLERANCE reached. " |
| << "No non-constant parameter blocks found."; |
| |
| // FUNCTION_TOLERANCE is the right convergence here, as we know |
| // that the objective function is constant and cannot be changed |
| // any further. |
| summary->termination_type = FUNCTION_TOLERANCE; |
| |
| double post_process_start_time = WallTimeInSeconds(); |
| // Evaluate the final cost, residual vector and the jacobian |
| // matrix if requested by the user. |
| if (!Evaluator::Evaluate(original_program, |
| options.num_threads, |
| &summary->final_cost, |
| options.return_final_residuals |
| ? &summary->final_residuals |
| : NULL, |
| options.return_final_gradient |
| ? &summary->final_gradient |
| : NULL, |
| options.return_final_jacobian |
| ? &summary->final_jacobian |
| : NULL)) { |
| summary->termination_type = NUMERICAL_FAILURE; |
| summary->error = "Unable to evaluate the final cost."; |
| LOG(ERROR) << summary->error; |
| return; |
| } |
| |
| // Ensure the program state is set to the user parameters on the way out. |
| original_program->SetParameterBlockStatePtrsToUserStatePtrs(); |
| |
| summary->postprocessor_time_in_seconds = |
| WallTimeInSeconds() - post_process_start_time; |
| return; |
| } |
| |
| scoped_ptr<LinearSolver> |
| linear_solver(CreateLinearSolver(&options, &summary->error)); |
| if (linear_solver == NULL) { |
| return; |
| } |
| |
| summary->linear_solver_type_given = original_options.linear_solver_type; |
| summary->linear_solver_type_used = options.linear_solver_type; |
| |
| summary->preconditioner_type = options.preconditioner_type; |
| |
| summary->num_linear_solver_threads_given = |
| original_options.num_linear_solver_threads; |
| summary->num_linear_solver_threads_used = options.num_linear_solver_threads; |
| |
| summary->sparse_linear_algebra_library = |
| options.sparse_linear_algebra_library; |
| |
| summary->trust_region_strategy_type = options.trust_region_strategy_type; |
| summary->dogleg_type = options.dogleg_type; |
| |
| // Only Schur types require the lexicographic reordering. |
| if (IsSchurType(options.linear_solver_type)) { |
| const int num_eliminate_blocks = |
| options.linear_solver_ordering |
| ->group_to_elements().begin() |
| ->second.size(); |
| if (!LexicographicallyOrderResidualBlocks(num_eliminate_blocks, |
| reduced_program.get(), |
| &summary->error)) { |
| return; |
| } |
| } |
| |
| scoped_ptr<Evaluator> evaluator(CreateEvaluator(options, |
| problem_impl->parameter_map(), |
| reduced_program.get(), |
| &summary->error)); |
| if (evaluator == NULL) { |
| return; |
| } |
| |
| scoped_ptr<CoordinateDescentMinimizer> inner_iteration_minimizer; |
| if (options.use_inner_iterations) { |
| if (reduced_program->parameter_blocks().size() < 2) { |
| LOG(WARNING) << "Reduced problem only contains one parameter block." |
| << "Disabling inner iterations."; |
| } else { |
| inner_iteration_minimizer.reset( |
| CreateInnerIterationMinimizer(original_options, |
| *reduced_program, |
| problem_impl->parameter_map(), |
| &summary->error)); |
| if (inner_iteration_minimizer == NULL) { |
| LOG(ERROR) << summary->error; |
| return; |
| } |
| } |
| } |
| |
| // The optimizer works on contiguous parameter vectors; allocate some. |
| Vector parameters(reduced_program->NumParameters()); |
| |
| // Collect the discontiguous parameters into a contiguous state vector. |
| reduced_program->ParameterBlocksToStateVector(parameters.data()); |
| |
| Vector original_parameters = parameters; |
| |
| double minimizer_start_time = WallTimeInSeconds(); |
| summary->preprocessor_time_in_seconds = |
| minimizer_start_time - solver_start_time; |
| |
| // Run the optimization. |
| Minimize(options, |
| reduced_program.get(), |
| inner_iteration_minimizer.get(), |
| evaluator.get(), |
| linear_solver.get(), |
| parameters.data(), |
| summary); |
| |
| // If the user aborted mid-optimization or the optimization |
| // terminated because of a numerical failure, then return without |
| // updating user state. |
| if (summary->termination_type == USER_ABORT || |
| summary->termination_type == NUMERICAL_FAILURE) { |
| return; |
| } |
| |
| double post_process_start_time = WallTimeInSeconds(); |
| |
| // Push the contiguous optimized parameters back to the user's parameters. |
| reduced_program->StateVectorToParameterBlocks(parameters.data()); |
| reduced_program->CopyParameterBlockStateToUserState(); |
| |
| // Evaluate the final cost, residual vector and the jacobian |
| // matrix if requested by the user. |
| if (!Evaluator::Evaluate(original_program, |
| options.num_threads, |
| &summary->final_cost, |
| options.return_final_residuals |
| ? &summary->final_residuals |
| : NULL, |
| options.return_final_gradient |
| ? &summary->final_gradient |
| : NULL, |
| options.return_final_jacobian |
| ? &summary->final_jacobian |
| : NULL)) { |
| // This failure requires careful handling. |
| // |
| // At this point, we have modified the user's state, but the |
| // evaluation failed and we inform him of NUMERICAL_FAILURE. Ceres |
| // guarantees that user's state is not modified if the solver |
| // returns with NUMERICAL_FAILURE. Thus, we need to restore the |
| // user's state to their original values. |
| |
| reduced_program->StateVectorToParameterBlocks(original_parameters.data()); |
| reduced_program->CopyParameterBlockStateToUserState(); |
| |
| summary->termination_type = NUMERICAL_FAILURE; |
| summary->error = "Unable to evaluate the final cost."; |
| LOG(ERROR) << summary->error; |
| return; |
| } |
| |
| // Ensure the program state is set to the user parameters on the way out. |
| original_program->SetParameterBlockStatePtrsToUserStatePtrs(); |
| |
| // Stick a fork in it, we're done. |
| summary->postprocessor_time_in_seconds = |
| WallTimeInSeconds() - post_process_start_time; |
| } |
| |
| bool SolverImpl::IsOrderingValid(const Solver::Options& options, |
| const ProblemImpl* problem_impl, |
| string* error) { |
| if (options.linear_solver_ordering->NumElements() != |
| problem_impl->NumParameterBlocks()) { |
| *error = "Number of parameter blocks in user supplied ordering " |
| "does not match the number of parameter blocks in the problem"; |
| return false; |
| } |
| |
| const Program& program = problem_impl->program(); |
| const vector<ParameterBlock*>& parameter_blocks = program.parameter_blocks(); |
| for (vector<ParameterBlock*>::const_iterator it = parameter_blocks.begin(); |
| it != parameter_blocks.end(); |
| ++it) { |
| if (!options.linear_solver_ordering |
| ->IsMember(const_cast<double*>((*it)->user_state()))) { |
| *error = "Problem contains a parameter block that is not in " |
| "the user specified ordering."; |
| return false; |
| } |
| } |
| |
| if (IsSchurType(options.linear_solver_type) && |
| options.linear_solver_ordering->NumGroups() > 1) { |
| const vector<ResidualBlock*>& residual_blocks = program.residual_blocks(); |
| const set<double*>& e_blocks = |
| options.linear_solver_ordering->group_to_elements().begin()->second; |
| if (!IsParameterBlockSetIndependent(e_blocks, residual_blocks)) { |
| *error = "The user requested the use of a Schur type solver. " |
| "But the first elimination group in the ordering is not an " |
| "independent set."; |
| return false; |
| } |
| } |
| return true; |
| } |
| |
| bool SolverImpl::IsParameterBlockSetIndependent(const set<double*>& parameter_block_ptrs, |
| const vector<ResidualBlock*>& residual_blocks) { |
| // Loop over each residual block and ensure that no two parameter |
| // blocks in the same residual block are part of |
| // parameter_block_ptrs as that would violate the assumption that it |
| // is an independent set in the Hessian matrix. |
| for (vector<ResidualBlock*>::const_iterator it = residual_blocks.begin(); |
| it != residual_blocks.end(); |
| ++it) { |
| ParameterBlock* const* parameter_blocks = (*it)->parameter_blocks(); |
| const int num_parameter_blocks = (*it)->NumParameterBlocks(); |
| int count = 0; |
| for (int i = 0; i < num_parameter_blocks; ++i) { |
| count += parameter_block_ptrs.count( |
| parameter_blocks[i]->mutable_user_state()); |
| } |
| if (count > 1) { |
| return false; |
| } |
| } |
| return true; |
| } |
| |
| |
| // Strips varying parameters and residuals, maintaining order, and updating |
| // num_eliminate_blocks. |
| bool SolverImpl::RemoveFixedBlocksFromProgram(Program* program, |
| ParameterBlockOrdering* ordering, |
| double* fixed_cost, |
| string* error) { |
| vector<ParameterBlock*>* parameter_blocks = |
| program->mutable_parameter_blocks(); |
| |
| scoped_array<double> residual_block_evaluate_scratch; |
| if (fixed_cost != NULL) { |
| residual_block_evaluate_scratch.reset( |
| new double[program->MaxScratchDoublesNeededForEvaluate()]); |
| *fixed_cost = 0.0; |
| } |
| |
| // Mark all the parameters as unused. Abuse the index member of the parameter |
| // blocks for the marking. |
| for (int i = 0; i < parameter_blocks->size(); ++i) { |
| (*parameter_blocks)[i]->set_index(-1); |
| } |
| |
| // Filter out residual that have all-constant parameters, and mark all the |
| // parameter blocks that appear in residuals. |
| { |
| vector<ResidualBlock*>* residual_blocks = |
| program->mutable_residual_blocks(); |
| int j = 0; |
| for (int i = 0; i < residual_blocks->size(); ++i) { |
| ResidualBlock* residual_block = (*residual_blocks)[i]; |
| int num_parameter_blocks = residual_block->NumParameterBlocks(); |
| |
| // Determine if the residual block is fixed, and also mark varying |
| // parameters that appear in the residual block. |
| bool all_constant = true; |
| for (int k = 0; k < num_parameter_blocks; k++) { |
| ParameterBlock* parameter_block = residual_block->parameter_blocks()[k]; |
| if (!parameter_block->IsConstant()) { |
| all_constant = false; |
| parameter_block->set_index(1); |
| } |
| } |
| |
| if (!all_constant) { |
| (*residual_blocks)[j++] = (*residual_blocks)[i]; |
| } else if (fixed_cost != NULL) { |
| // The residual is constant and will be removed, so its cost is |
| // added to the variable fixed_cost. |
| double cost = 0.0; |
| if (!residual_block->Evaluate( |
| &cost, NULL, NULL, residual_block_evaluate_scratch.get())) { |
| *error = StringPrintf("Evaluation of the residual %d failed during " |
| "removal of fixed residual blocks.", i); |
| return false; |
| } |
| *fixed_cost += cost; |
| } |
| } |
| residual_blocks->resize(j); |
| } |
| |
| // Filter out unused or fixed parameter blocks, and update |
| // the ordering. |
| { |
| vector<ParameterBlock*>* parameter_blocks = |
| program->mutable_parameter_blocks(); |
| int j = 0; |
| for (int i = 0; i < parameter_blocks->size(); ++i) { |
| ParameterBlock* parameter_block = (*parameter_blocks)[i]; |
| if (parameter_block->index() == 1) { |
| (*parameter_blocks)[j++] = parameter_block; |
| } else { |
| ordering->Remove(parameter_block->mutable_user_state()); |
| } |
| } |
| parameter_blocks->resize(j); |
| } |
| |
| CHECK(((program->NumResidualBlocks() == 0) && |
| (program->NumParameterBlocks() == 0)) || |
| ((program->NumResidualBlocks() != 0) && |
| (program->NumParameterBlocks() != 0))) |
| << "Congratulations, you found a bug in Ceres. Please report it."; |
| return true; |
| } |
| |
| Program* SolverImpl::CreateReducedProgram(Solver::Options* options, |
| ProblemImpl* problem_impl, |
| double* fixed_cost, |
| string* error) { |
| CHECK_NOTNULL(options->linear_solver_ordering); |
| Program* original_program = problem_impl->mutable_program(); |
| scoped_ptr<Program> transformed_program(new Program(*original_program)); |
| ParameterBlockOrdering* linear_solver_ordering = |
| options->linear_solver_ordering; |
| |
| const int min_group_id = |
| linear_solver_ordering->group_to_elements().begin()->first; |
| const int original_num_groups = linear_solver_ordering->NumGroups(); |
| |
| if (!RemoveFixedBlocksFromProgram(transformed_program.get(), |
| linear_solver_ordering, |
| fixed_cost, |
| error)) { |
| return NULL; |
| } |
| |
| if (transformed_program->NumParameterBlocks() == 0) { |
| if (transformed_program->NumResidualBlocks() > 0) { |
| *error = "Zero parameter blocks but non-zero residual blocks" |
| " in the reduced program. Congratulations, you found a " |
| "Ceres bug! Please report this error to the developers."; |
| return NULL; |
| } |
| |
| LOG(WARNING) << "No varying parameter blocks to optimize; " |
| << "bailing early."; |
| return transformed_program.release(); |
| } |
| |
| // If the user supplied an linear_solver_ordering with just one |
| // group, it is equivalent to the user supplying NULL as |
| // ordering. Ceres is completely free to choose the parameter block |
| // ordering as it sees fit. For Schur type solvers, this means that |
| // the user wishes for Ceres to identify the e_blocks, which we do |
| // by computing a maximal independent set. |
| if (original_num_groups == 1 && IsSchurType(options->linear_solver_type)) { |
| vector<ParameterBlock*> schur_ordering; |
| const int num_eliminate_blocks = ComputeSchurOrdering(*transformed_program, |
| &schur_ordering); |
| CHECK_EQ(schur_ordering.size(), transformed_program->NumParameterBlocks()) |
| << "Congratulations, you found a Ceres bug! Please report this error " |
| << "to the developers."; |
| |
| for (int i = 0; i < schur_ordering.size(); ++i) { |
| linear_solver_ordering->AddElementToGroup( |
| schur_ordering[i]->mutable_user_state(), |
| (i < num_eliminate_blocks) ? 0 : 1); |
| } |
| } |
| |
| if (!ApplyUserOrdering(problem_impl->parameter_map(), |
| linear_solver_ordering, |
| transformed_program.get(), |
| error)) { |
| return NULL; |
| } |
| |
| // If the user requested the use of a Schur type solver, and |
| // supplied a non-NULL linear_solver_ordering object with more than |
| // one elimination group, then it can happen that after all the |
| // parameter blocks which are fixed or unused have been removed from |
| // the program and the ordering, there are no more parameter blocks |
| // in the first elimination group. |
| // |
| // In such a case, the use of a Schur type solver is not possible, |
| // as they assume there is at least one e_block. Thus, we |
| // automatically switch to one of the other solvers, depending on |
| // the user's indicated preferences. |
| if (IsSchurType(options->linear_solver_type) && |
| original_num_groups > 1 && |
| linear_solver_ordering->GroupSize(min_group_id) == 0) { |
| string msg = "No e_blocks remaining. Switching from "; |
| if (options->linear_solver_type == SPARSE_SCHUR) { |
| options->linear_solver_type = SPARSE_NORMAL_CHOLESKY; |
| msg += "SPARSE_SCHUR to SPARSE_NORMAL_CHOLESKY."; |
| } else if (options->linear_solver_type == DENSE_SCHUR) { |
| // TODO(sameeragarwal): This is probably not a great choice. |
| // Ideally, we should have a DENSE_NORMAL_CHOLESKY, that can |
| // take a BlockSparseMatrix as input. |
| options->linear_solver_type = DENSE_QR; |
| msg += "DENSE_SCHUR to DENSE_QR."; |
| } else if (options->linear_solver_type == ITERATIVE_SCHUR) { |
| msg += StringPrintf("ITERATIVE_SCHUR with %s preconditioner " |
| "to CGNR with JACOBI preconditioner.", |
| PreconditionerTypeToString( |
| options->preconditioner_type)); |
| options->linear_solver_type = CGNR; |
| if (options->preconditioner_type != IDENTITY) { |
| // CGNR currently only supports the JACOBI preconditioner. |
| options->preconditioner_type = JACOBI; |
| } |
| } |
| |
| LOG(WARNING) << msg; |
| } |
| |
| // Since the transformed program is the "active" program, and it is mutated, |
| // update the parameter offsets and indices. |
| transformed_program->SetParameterOffsetsAndIndex(); |
| return transformed_program.release(); |
| } |
| |
| LinearSolver* SolverImpl::CreateLinearSolver(Solver::Options* options, |
| string* error) { |
| CHECK_NOTNULL(options); |
| CHECK_NOTNULL(options->linear_solver_ordering); |
| CHECK_NOTNULL(error); |
| |
| if (options->trust_region_strategy_type == DOGLEG) { |
| if (options->linear_solver_type == ITERATIVE_SCHUR || |
| options->linear_solver_type == CGNR) { |
| *error = "DOGLEG only supports exact factorization based linear " |
| "solvers. If you want to use an iterative solver please " |
| "use LEVENBERG_MARQUARDT as the trust_region_strategy_type"; |
| return NULL; |
| } |
| } |
| |
| #ifdef CERES_NO_SUITESPARSE |
| if (options->linear_solver_type == SPARSE_NORMAL_CHOLESKY && |
| options->sparse_linear_algebra_library == SUITE_SPARSE) { |
| *error = "Can't use SPARSE_NORMAL_CHOLESKY with SUITESPARSE because " |
| "SuiteSparse was not enabled when Ceres was built."; |
| return NULL; |
| } |
| |
| if (options->preconditioner_type == SCHUR_JACOBI) { |
| *error = "SCHUR_JACOBI preconditioner not suppored. Please build Ceres " |
| "with SuiteSparse support."; |
| return NULL; |
| } |
| |
| if (options->preconditioner_type == CLUSTER_JACOBI) { |
| *error = "CLUSTER_JACOBI preconditioner not suppored. Please build Ceres " |
| "with SuiteSparse support."; |
| return NULL; |
| } |
| |
| if (options->preconditioner_type == CLUSTER_TRIDIAGONAL) { |
| *error = "CLUSTER_TRIDIAGONAL preconditioner not suppored. Please build " |
| "Ceres with SuiteSparse support."; |
| return NULL; |
| } |
| #endif |
| |
| #ifdef CERES_NO_CXSPARSE |
| if (options->linear_solver_type == SPARSE_NORMAL_CHOLESKY && |
| options->sparse_linear_algebra_library == CX_SPARSE) { |
| *error = "Can't use SPARSE_NORMAL_CHOLESKY with CXSPARSE because " |
| "CXSparse was not enabled when Ceres was built."; |
| return NULL; |
| } |
| #endif |
| |
| #if defined(CERES_NO_SUITESPARSE) && defined(CERES_NO_CXSPARSE) |
| if (options->linear_solver_type == SPARSE_SCHUR) { |
| *error = "Can't use SPARSE_SCHUR because neither SuiteSparse nor" |
| "CXSparse was enabled when Ceres was compiled."; |
| return NULL; |
| } |
| #endif |
| |
| if (options->linear_solver_max_num_iterations <= 0) { |
| *error = "Solver::Options::linear_solver_max_num_iterations is 0."; |
| return NULL; |
| } |
| if (options->linear_solver_min_num_iterations <= 0) { |
| *error = "Solver::Options::linear_solver_min_num_iterations is 0."; |
| return NULL; |
| } |
| if (options->linear_solver_min_num_iterations > |
| options->linear_solver_max_num_iterations) { |
| *error = "Solver::Options::linear_solver_min_num_iterations > " |
| "Solver::Options::linear_solver_max_num_iterations."; |
| return NULL; |
| } |
| |
| LinearSolver::Options linear_solver_options; |
| linear_solver_options.min_num_iterations = |
| options->linear_solver_min_num_iterations; |
| linear_solver_options.max_num_iterations = |
| options->linear_solver_max_num_iterations; |
| linear_solver_options.type = options->linear_solver_type; |
| linear_solver_options.preconditioner_type = options->preconditioner_type; |
| linear_solver_options.sparse_linear_algebra_library = |
| options->sparse_linear_algebra_library; |
| |
| linear_solver_options.num_threads = options->num_linear_solver_threads; |
| // The matrix used for storing the dense Schur complement has a |
| // single lock guarding the whole matrix. Running the |
| // SchurComplementSolver with multiple threads leads to maximum |
| // contention and slowdown. If the problem is large enough to |
| // benefit from a multithreaded schur eliminator, you should be |
| // using a SPARSE_SCHUR solver anyways. |
| if ((linear_solver_options.num_threads > 1) && |
| (linear_solver_options.type == DENSE_SCHUR)) { |
| LOG(WARNING) << "Warning: Solver::Options::num_linear_solver_threads = " |
| << options->num_linear_solver_threads |
| << " with DENSE_SCHUR will result in poor performance; " |
| << "switching to single-threaded."; |
| linear_solver_options.num_threads = 1; |
| } |
| options->num_linear_solver_threads = linear_solver_options.num_threads; |
| |
| linear_solver_options.use_block_amd = options->use_block_amd; |
| const map<int, set<double*> >& groups = |
| options->linear_solver_ordering->group_to_elements(); |
| for (map<int, set<double*> >::const_iterator it = groups.begin(); |
| it != groups.end(); |
| ++it) { |
| linear_solver_options.elimination_groups.push_back(it->second.size()); |
| } |
| // Schur type solvers, expect at least two elimination groups. If |
| // there is only one elimination group, then CreateReducedProgram |
| // guarantees that this group only contains e_blocks. Thus we add a |
| // dummy elimination group with zero blocks in it. |
| if (IsSchurType(linear_solver_options.type) && |
| linear_solver_options.elimination_groups.size() == 1) { |
| linear_solver_options.elimination_groups.push_back(0); |
| } |
| |
| return LinearSolver::Create(linear_solver_options); |
| } |
| |
| bool SolverImpl::ApplyUserOrdering(const ProblemImpl::ParameterMap& parameter_map, |
| const ParameterBlockOrdering* ordering, |
| Program* program, |
| string* error) { |
| if (ordering->NumElements() != program->NumParameterBlocks()) { |
| *error = StringPrintf("User specified ordering does not have the same " |
| "number of parameters as the problem. The problem" |
| "has %d blocks while the ordering has %d blocks.", |
| program->NumParameterBlocks(), |
| ordering->NumElements()); |
| return false; |
| } |
| |
| vector<ParameterBlock*>* parameter_blocks = |
| program->mutable_parameter_blocks(); |
| parameter_blocks->clear(); |
| |
| const map<int, set<double*> >& groups = |
| ordering->group_to_elements(); |
| |
| for (map<int, set<double*> >::const_iterator group_it = groups.begin(); |
| group_it != groups.end(); |
| ++group_it) { |
| const set<double*>& group = group_it->second; |
| for (set<double*>::const_iterator parameter_block_ptr_it = group.begin(); |
| parameter_block_ptr_it != group.end(); |
| ++parameter_block_ptr_it) { |
| ProblemImpl::ParameterMap::const_iterator parameter_block_it = |
| parameter_map.find(*parameter_block_ptr_it); |
| if (parameter_block_it == parameter_map.end()) { |
| *error = StringPrintf("User specified ordering contains a pointer " |
| "to a double that is not a parameter block in the " |
| "problem. The invalid double is in group: %d", |
| group_it->first); |
| return false; |
| } |
| parameter_blocks->push_back(parameter_block_it->second); |
| } |
| } |
| return true; |
| } |
| |
| // Find the minimum index of any parameter block to the given residual. |
| // Parameter blocks that have indices greater than num_eliminate_blocks are |
| // considered to have an index equal to num_eliminate_blocks. |
| int MinParameterBlock(const ResidualBlock* residual_block, |
| int num_eliminate_blocks) { |
| int min_parameter_block_position = num_eliminate_blocks; |
| for (int i = 0; i < residual_block->NumParameterBlocks(); ++i) { |
| ParameterBlock* parameter_block = residual_block->parameter_blocks()[i]; |
| if (!parameter_block->IsConstant()) { |
| CHECK_NE(parameter_block->index(), -1) |
| << "Did you forget to call Program::SetParameterOffsetsAndIndex()? " |
| << "This is a Ceres bug; please contact the developers!"; |
| min_parameter_block_position = std::min(parameter_block->index(), |
| min_parameter_block_position); |
| } |
| } |
| return min_parameter_block_position; |
| } |
| |
| // Reorder the residuals for program, if necessary, so that the residuals |
| // involving each E block occur together. This is a necessary condition for the |
| // Schur eliminator, which works on these "row blocks" in the jacobian. |
| bool SolverImpl::LexicographicallyOrderResidualBlocks(const int num_eliminate_blocks, |
| Program* program, |
| string* error) { |
| CHECK_GE(num_eliminate_blocks, 1) |
| << "Congratulations, you found a Ceres bug! Please report this error " |
| << "to the developers."; |
| |
| // Create a histogram of the number of residuals for each E block. There is an |
| // extra bucket at the end to catch all non-eliminated F blocks. |
| vector<int> residual_blocks_per_e_block(num_eliminate_blocks + 1); |
| vector<ResidualBlock*>* residual_blocks = program->mutable_residual_blocks(); |
| vector<int> min_position_per_residual(residual_blocks->size()); |
| for (int i = 0; i < residual_blocks->size(); ++i) { |
| ResidualBlock* residual_block = (*residual_blocks)[i]; |
| int position = MinParameterBlock(residual_block, num_eliminate_blocks); |
| min_position_per_residual[i] = position; |
| DCHECK_LE(position, num_eliminate_blocks); |
| residual_blocks_per_e_block[position]++; |
| } |
| |
| // Run a cumulative sum on the histogram, to obtain offsets to the start of |
| // each histogram bucket (where each bucket is for the residuals for that |
| // E-block). |
| vector<int> offsets(num_eliminate_blocks + 1); |
| std::partial_sum(residual_blocks_per_e_block.begin(), |
| residual_blocks_per_e_block.end(), |
| offsets.begin()); |
| CHECK_EQ(offsets.back(), residual_blocks->size()) |
| << "Congratulations, you found a Ceres bug! Please report this error " |
| << "to the developers."; |
| |
| CHECK(find(residual_blocks_per_e_block.begin(), |
| residual_blocks_per_e_block.end() - 1, 0) != |
| residual_blocks_per_e_block.end()) |
| << "Congratulations, you found a Ceres bug! Please report this error " |
| << "to the developers."; |
| |
| // Fill in each bucket with the residual blocks for its corresponding E block. |
| // Each bucket is individually filled from the back of the bucket to the front |
| // of the bucket. The filling order among the buckets is dictated by the |
| // residual blocks. This loop uses the offsets as counters; subtracting one |
| // from each offset as a residual block is placed in the bucket. When the |
| // filling is finished, the offset pointerts should have shifted down one |
| // entry (this is verified below). |
| vector<ResidualBlock*> reordered_residual_blocks( |
| (*residual_blocks).size(), static_cast<ResidualBlock*>(NULL)); |
| for (int i = 0; i < residual_blocks->size(); ++i) { |
| int bucket = min_position_per_residual[i]; |
| |
| // Decrement the cursor, which should now point at the next empty position. |
| offsets[bucket]--; |
| |
| // Sanity. |
| CHECK(reordered_residual_blocks[offsets[bucket]] == NULL) |
| << "Congratulations, you found a Ceres bug! Please report this error " |
| << "to the developers."; |
| |
| reordered_residual_blocks[offsets[bucket]] = (*residual_blocks)[i]; |
| } |
| |
| // Sanity check #1: The difference in bucket offsets should match the |
| // histogram sizes. |
| for (int i = 0; i < num_eliminate_blocks; ++i) { |
| CHECK_EQ(residual_blocks_per_e_block[i], offsets[i + 1] - offsets[i]) |
| << "Congratulations, you found a Ceres bug! Please report this error " |
| << "to the developers."; |
| } |
| // Sanity check #2: No NULL's left behind. |
| for (int i = 0; i < reordered_residual_blocks.size(); ++i) { |
| CHECK(reordered_residual_blocks[i] != NULL) |
| << "Congratulations, you found a Ceres bug! Please report this error " |
| << "to the developers."; |
| } |
| |
| // Now that the residuals are collected by E block, swap them in place. |
| swap(*program->mutable_residual_blocks(), reordered_residual_blocks); |
| return true; |
| } |
| |
| Evaluator* SolverImpl::CreateEvaluator(const Solver::Options& options, |
| const ProblemImpl::ParameterMap& parameter_map, |
| Program* program, |
| string* error) { |
| Evaluator::Options evaluator_options; |
| evaluator_options.linear_solver_type = options.linear_solver_type; |
| evaluator_options.num_eliminate_blocks = |
| (options.linear_solver_ordering->NumGroups() > 0 && |
| IsSchurType(options.linear_solver_type)) |
| ? (options.linear_solver_ordering |
| ->group_to_elements().begin() |
| ->second.size()) |
| : 0; |
| evaluator_options.num_threads = options.num_threads; |
| return Evaluator::Create(evaluator_options, program, error); |
| } |
| |
| CoordinateDescentMinimizer* SolverImpl::CreateInnerIterationMinimizer( |
| const Solver::Options& options, |
| const Program& program, |
| const ProblemImpl::ParameterMap& parameter_map, |
| string* error) { |
| scoped_ptr<CoordinateDescentMinimizer> inner_iteration_minimizer( |
| new CoordinateDescentMinimizer); |
| scoped_ptr<ParameterBlockOrdering> inner_iteration_ordering; |
| ParameterBlockOrdering* ordering_ptr = NULL; |
| |
| if (options.inner_iteration_ordering == NULL) { |
| // Find a recursive decomposition of the Hessian matrix as a set |
| // of independent sets of decreasing size and invert it. This |
| // seems to work better in practice, i.e., Cameras before |
| // points. |
| inner_iteration_ordering.reset(new ParameterBlockOrdering); |
| ComputeRecursiveIndependentSetOrdering(program, |
| inner_iteration_ordering.get()); |
| inner_iteration_ordering->Reverse(); |
| ordering_ptr = inner_iteration_ordering.get(); |
| } else { |
| const map<int, set<double*> >& group_to_elements = |
| options.inner_iteration_ordering->group_to_elements(); |
| |
| // Iterate over each group and verify that it is an independent |
| // set. |
| map<int, set<double*> >::const_iterator it = group_to_elements.begin(); |
| for ( ;it != group_to_elements.end(); ++it) { |
| if (!IsParameterBlockSetIndependent(it->second, |
| program.residual_blocks())) { |
| *error = |
| StringPrintf("The user-provided " |
| "parameter_blocks_for_inner_iterations does not " |
| "form an independent set. Group Id: %d", it->first); |
| return NULL; |
| } |
| } |
| ordering_ptr = options.inner_iteration_ordering; |
| } |
| |
| if (!inner_iteration_minimizer->Init(program, |
| parameter_map, |
| *ordering_ptr, |
| error)) { |
| return NULL; |
| } |
| |
| return inner_iteration_minimizer.release(); |
| } |
| |
| } // namespace internal |
| } // namespace ceres |
