mirror of
https://github.com/ceres-solver/ceres-solver.git
synced 2026-08-29 16:40:38 +08:00
Sameer Agarwal
202dd9f3a1
The non-linear least squares solver had the ability to update the user's parameters every iteration. Now GradientProblemSolver can do the same. Also a few minor Sphinx markup related cleanup to the documentation which were found in the process of updating Sphinx on my machine and adding the docs for this feature. This fixes https://github.com/ceres-solver/ceres-solver/issues/246 Change-Id: Ib6b90ac22be8bfb60b14f25ad52082ba371af164
301 lines
12 KiB
C++
301 lines
12 KiB
C++
// Ceres Solver - A fast non-linear least squares minimizer
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// Copyright 2015 Google Inc. All rights reserved.
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// http://ceres-solver.org/
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//
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// Redistribution and use in source and binary forms, with or without
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// modification, are permitted provided that the following conditions are met:
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//
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// * Redistributions of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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// * Redistributions in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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// * Neither the name of Google Inc. nor the names of its contributors may be
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// used to endorse or promote products derived from this software without
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// specific prior written permission.
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//
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// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
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// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
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// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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// POSSIBILITY OF SUCH DAMAGE.
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//
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// Author: sameeragarwal@google.com (Sameer Agarwal)
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#include "ceres/gradient_problem_solver.h"
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#include "ceres/callbacks.h"
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#include "ceres/gradient_problem.h"
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#include "ceres/gradient_problem_evaluator.h"
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#include "ceres/internal/eigen.h"
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#include "ceres/internal/port.h"
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#include "ceres/map_util.h"
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#include "ceres/minimizer.h"
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#include "ceres/solver.h"
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#include "ceres/solver_utils.h"
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#include "ceres/stringprintf.h"
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#include "ceres/types.h"
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#include "ceres/wall_time.h"
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namespace ceres {
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using internal::StringPrintf;
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using internal::StringAppendF;
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using std::string;
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namespace {
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Solver::Options GradientProblemSolverOptionsToSolverOptions(
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const GradientProblemSolver::Options& options) {
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#define COPY_OPTION(x) solver_options.x = options.x
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Solver::Options solver_options;
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solver_options.minimizer_type = LINE_SEARCH;
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COPY_OPTION(line_search_direction_type);
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COPY_OPTION(line_search_type);
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COPY_OPTION(nonlinear_conjugate_gradient_type);
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COPY_OPTION(max_lbfgs_rank);
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COPY_OPTION(use_approximate_eigenvalue_bfgs_scaling);
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COPY_OPTION(line_search_interpolation_type);
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COPY_OPTION(min_line_search_step_size);
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COPY_OPTION(line_search_sufficient_function_decrease);
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COPY_OPTION(max_line_search_step_contraction);
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COPY_OPTION(min_line_search_step_contraction);
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COPY_OPTION(max_num_line_search_step_size_iterations);
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COPY_OPTION(max_num_line_search_direction_restarts);
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COPY_OPTION(line_search_sufficient_curvature_decrease);
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COPY_OPTION(max_line_search_step_expansion);
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COPY_OPTION(max_num_iterations);
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COPY_OPTION(max_solver_time_in_seconds);
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COPY_OPTION(parameter_tolerance);
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COPY_OPTION(function_tolerance);
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COPY_OPTION(gradient_tolerance);
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COPY_OPTION(logging_type);
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COPY_OPTION(minimizer_progress_to_stdout);
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COPY_OPTION(callbacks);
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return solver_options;
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#undef COPY_OPTION
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}
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} // namespace
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bool GradientProblemSolver::Options::IsValid(std::string* error) const {
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const Solver::Options solver_options =
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GradientProblemSolverOptionsToSolverOptions(*this);
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return solver_options.IsValid(error);
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}
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GradientProblemSolver::~GradientProblemSolver() {
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}
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void GradientProblemSolver::Solve(const GradientProblemSolver::Options& options,
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const GradientProblem& problem,
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double* parameters_ptr,
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GradientProblemSolver::Summary* summary) {
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using internal::GradientProblemEvaluator;
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using internal::GradientProblemSolverStateUpdatingCallback;
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using internal::LoggingCallback;
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using internal::Minimizer;
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using internal::scoped_ptr;
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using internal::SetSummaryFinalCost;
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using internal::WallTimeInSeconds;
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double start_time = WallTimeInSeconds();
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*CHECK_NOTNULL(summary) = Summary();
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summary->num_parameters = problem.NumParameters();
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summary->num_local_parameters = problem.NumLocalParameters();
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summary->line_search_direction_type = options.line_search_direction_type; // NOLINT
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summary->line_search_interpolation_type = options.line_search_interpolation_type; // NOLINT
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summary->line_search_type = options.line_search_type;
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summary->max_lbfgs_rank = options.max_lbfgs_rank;
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summary->nonlinear_conjugate_gradient_type = options.nonlinear_conjugate_gradient_type; // NOLINT
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// Check validity
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if (!options.IsValid(&summary->message)) {
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LOG(ERROR) << "Terminating: " << summary->message;
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return;
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}
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VectorRef parameters(parameters_ptr, problem.NumParameters());
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Vector solution(problem.NumParameters());
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solution = parameters;
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// TODO(sameeragarwal): This is a bit convoluted, we should be able
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// to convert to minimizer options directly, but this will do for
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// now.
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Minimizer::Options minimizer_options =
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Minimizer::Options(GradientProblemSolverOptionsToSolverOptions(options));
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minimizer_options.evaluator.reset(new GradientProblemEvaluator(problem));
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scoped_ptr<IterationCallback> logging_callback;
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if (options.logging_type != SILENT) {
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logging_callback.reset(
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new LoggingCallback(LINE_SEARCH, options.minimizer_progress_to_stdout));
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minimizer_options.callbacks.insert(minimizer_options.callbacks.begin(),
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logging_callback.get());
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}
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scoped_ptr<IterationCallback> state_updating_callback;
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if (options.update_state_every_iteration) {
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state_updating_callback.reset(
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new GradientProblemSolverStateUpdatingCallback(
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problem.NumParameters(), solution.data(), parameters_ptr));
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minimizer_options.callbacks.insert(minimizer_options.callbacks.begin(),
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state_updating_callback.get());
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}
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scoped_ptr<Minimizer> minimizer(Minimizer::Create(LINE_SEARCH));
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Solver::Summary solver_summary;
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solver_summary.fixed_cost = 0.0;
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solver_summary.preprocessor_time_in_seconds = 0.0;
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solver_summary.postprocessor_time_in_seconds = 0.0;
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solver_summary.line_search_polynomial_minimization_time_in_seconds = 0.0;
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minimizer->Minimize(minimizer_options, solution.data(), &solver_summary);
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summary->termination_type = solver_summary.termination_type;
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summary->message = solver_summary.message;
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summary->initial_cost = solver_summary.initial_cost;
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summary->final_cost = solver_summary.final_cost;
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summary->iterations = solver_summary.iterations;
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summary->line_search_polynomial_minimization_time_in_seconds =
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solver_summary.line_search_polynomial_minimization_time_in_seconds;
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if (summary->IsSolutionUsable()) {
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parameters = solution;
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SetSummaryFinalCost(summary);
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}
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const std::map<string, double>& evaluator_time_statistics =
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minimizer_options.evaluator->TimeStatistics();
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summary->cost_evaluation_time_in_seconds =
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FindWithDefault(evaluator_time_statistics, "Evaluator::Residual", 0.0);
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summary->gradient_evaluation_time_in_seconds =
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FindWithDefault(evaluator_time_statistics, "Evaluator::Jacobian", 0.0);
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const std::map<string, int>& evaluator_call_statistics =
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minimizer_options.evaluator->CallStatistics();
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summary->num_cost_evaluations =
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FindWithDefault(evaluator_call_statistics, "Evaluator::Residual", 0);
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summary->num_gradient_evaluations =
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FindWithDefault(evaluator_call_statistics, "Evaluator::Jacobian", 0);
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summary->total_time_in_seconds = WallTimeInSeconds() - start_time;
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}
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// Invalid values for most fields, to ensure that we are not
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// accidentally reporting default values.
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GradientProblemSolver::Summary::Summary()
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: termination_type(FAILURE),
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message("ceres::GradientProblemSolve was not called."),
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initial_cost(-1.0),
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final_cost(-1.0),
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total_time_in_seconds(-1.0),
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cost_evaluation_time_in_seconds(-1.0),
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gradient_evaluation_time_in_seconds(-1.0),
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line_search_polynomial_minimization_time_in_seconds(-1.0),
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num_parameters(-1),
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num_local_parameters(-1),
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line_search_direction_type(LBFGS),
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line_search_type(ARMIJO),
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line_search_interpolation_type(BISECTION),
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nonlinear_conjugate_gradient_type(FLETCHER_REEVES),
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max_lbfgs_rank(-1) {
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}
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bool GradientProblemSolver::Summary::IsSolutionUsable() const {
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return internal::IsSolutionUsable(*this);
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}
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string GradientProblemSolver::Summary::BriefReport() const {
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return StringPrintf("Ceres GradientProblemSolver Report: "
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"Iterations: %d, "
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"Initial cost: %e, "
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"Final cost: %e, "
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"Termination: %s",
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static_cast<int>(iterations.size()),
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initial_cost,
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final_cost,
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TerminationTypeToString(termination_type));
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}
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string GradientProblemSolver::Summary::FullReport() const {
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using internal::VersionString;
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string report = string("\nSolver Summary (v " + VersionString() + ")\n\n");
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StringAppendF(&report, "Parameters % 25d\n", num_parameters);
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if (num_local_parameters != num_parameters) {
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StringAppendF(&report, "Local parameters % 25d\n",
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num_local_parameters);
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}
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string line_search_direction_string;
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if (line_search_direction_type == LBFGS) {
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line_search_direction_string = StringPrintf("LBFGS (%d)", max_lbfgs_rank);
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} else if (line_search_direction_type == NONLINEAR_CONJUGATE_GRADIENT) {
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line_search_direction_string =
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NonlinearConjugateGradientTypeToString(
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nonlinear_conjugate_gradient_type);
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} else {
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line_search_direction_string =
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LineSearchDirectionTypeToString(line_search_direction_type);
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}
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StringAppendF(&report, "Line search direction %19s\n",
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line_search_direction_string.c_str());
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const string line_search_type_string =
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StringPrintf("%s %s",
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LineSearchInterpolationTypeToString(
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line_search_interpolation_type),
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LineSearchTypeToString(line_search_type));
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StringAppendF(&report, "Line search type %19s\n",
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line_search_type_string.c_str());
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StringAppendF(&report, "\n");
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StringAppendF(&report, "\nCost:\n");
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StringAppendF(&report, "Initial % 30e\n", initial_cost);
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if (termination_type != FAILURE &&
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termination_type != USER_FAILURE) {
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StringAppendF(&report, "Final % 30e\n", final_cost);
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StringAppendF(&report, "Change % 30e\n",
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initial_cost - final_cost);
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}
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StringAppendF(&report, "\nMinimizer iterations % 16d\n",
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static_cast<int>(iterations.size()));
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StringAppendF(&report, "\nTime (in seconds):\n");
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StringAppendF(&report, "\n Cost evaluation %23.6f (%d)\n",
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cost_evaluation_time_in_seconds,
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num_cost_evaluations);
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StringAppendF(&report, " Gradient evaluation %23.6f (%d)\n",
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gradient_evaluation_time_in_seconds,
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num_gradient_evaluations);
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StringAppendF(&report, " Polynomial minimization %17.6f\n",
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line_search_polynomial_minimization_time_in_seconds);
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StringAppendF(&report, "Total %25.6f\n\n",
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total_time_in_seconds);
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StringAppendF(&report, "Termination: %25s (%s)\n",
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TerminationTypeToString(termination_type), message.c_str());
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return report;
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}
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void Solve(const GradientProblemSolver::Options& options,
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const GradientProblem& problem,
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double* parameters,
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GradientProblemSolver::Summary* summary) {
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GradientProblemSolver solver;
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solver.Solve(options, problem, parameters, summary);
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}
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} // namespace ceres
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