diff options
Diffstat (limited to 'internal/ceres/trust_region_minimizer.cc')
| -rw-r--r-- | internal/ceres/trust_region_minimizer.cc | 171 |
1 files changed, 68 insertions, 103 deletions
diff --git a/internal/ceres/trust_region_minimizer.cc b/internal/ceres/trust_region_minimizer.cc index db2641d..d6ae0ab 100644 --- a/internal/ceres/trust_region_minimizer.cc +++ b/internal/ceres/trust_region_minimizer.cc @@ -41,6 +41,7 @@ #include "Eigen/Core" #include "ceres/array_utils.h" #include "ceres/evaluator.h" +#include "ceres/file.h" #include "ceres/internal/eigen.h" #include "ceres/internal/scoped_ptr.h" #include "ceres/linear_least_squares_problems.h" @@ -58,21 +59,6 @@ namespace { const double kEpsilon = 1e-12; } // namespace -// Execute the list of IterationCallbacks sequentially. If any one of -// the callbacks does not return SOLVER_CONTINUE, then stop and return -// its status. -CallbackReturnType TrustRegionMinimizer::RunCallbacks( - const IterationSummary& iteration_summary) { - for (int i = 0; i < options_.callbacks.size(); ++i) { - const CallbackReturnType status = - (*options_.callbacks[i])(iteration_summary); - if (status != SOLVER_CONTINUE) { - return status; - } - } - return SOLVER_CONTINUE; -} - // Compute a scaling vector that is used to improve the conditioning // of the Jacobian. void TrustRegionMinimizer::EstimateScale(const SparseMatrix& jacobian, @@ -85,25 +71,8 @@ void TrustRegionMinimizer::EstimateScale(const SparseMatrix& jacobian, void TrustRegionMinimizer::Init(const Minimizer::Options& options) { options_ = options; - sort(options_.lsqp_iterations_to_dump.begin(), - options_.lsqp_iterations_to_dump.end()); -} - -bool TrustRegionMinimizer::MaybeDumpLinearLeastSquaresProblem( - const int iteration, - const SparseMatrix* jacobian, - const double* residuals, - const double* step) const { - // TODO(sameeragarwal): Since the use of trust_region_radius has - // moved inside TrustRegionStrategy, its not clear how we dump the - // regularization vector/matrix anymore. - // - // Also num_eliminate_blocks is not visible to the trust region - // minimizer either. - // - // Both of these indicate that this is the wrong place for this - // code, and going forward this should needs fixing/refactoring. - return true; + sort(options_.trust_region_minimizer_iterations_to_dump.begin(), + options_.trust_region_minimizer_iterations_to_dump.end()); } void TrustRegionMinimizer::Minimize(const Minimizer::Options& options, @@ -154,14 +123,16 @@ void TrustRegionMinimizer::Minimize(const Minimizer::Options& options, // Do initial cost and Jacobian evaluation. double cost = 0.0; - if (!evaluator->Evaluate(x.data(), &cost, residuals.data(), NULL, jacobian)) { + if (!evaluator->Evaluate(x.data(), + &cost, + residuals.data(), + gradient.data(), + jacobian)) { LOG(WARNING) << "Terminating: Residual and Jacobian evaluation failed."; summary->termination_type = NUMERICAL_FAILURE; return; } - iteration_summary.cost = cost + summary->fixed_cost; - int num_consecutive_nonmonotonic_steps = 0; double minimum_cost = cost; double reference_cost = cost; @@ -169,29 +140,22 @@ void TrustRegionMinimizer::Minimize(const Minimizer::Options& options, double candidate_cost = cost; double accumulated_candidate_model_cost_change = 0.0; - gradient.setZero(); - jacobian->LeftMultiply(residuals.data(), gradient.data()); + summary->initial_cost = cost + summary->fixed_cost; + iteration_summary.cost = cost + summary->fixed_cost; iteration_summary.gradient_max_norm = gradient.lpNorm<Eigen::Infinity>(); - if (options_.jacobi_scaling) { - EstimateScale(*jacobian, scale.data()); - jacobian->ScaleColumns(scale.data()); - } else { - scale.setOnes(); - } - // The initial gradient max_norm is bounded from below so that we do // not divide by zero. - const double gradient_max_norm_0 = + const double initial_gradient_max_norm = max(iteration_summary.gradient_max_norm, kEpsilon); const double absolute_gradient_tolerance = - options_.gradient_tolerance * gradient_max_norm_0; + options_.gradient_tolerance * initial_gradient_max_norm; if (iteration_summary.gradient_max_norm <= absolute_gradient_tolerance) { summary->termination_type = GRADIENT_TOLERANCE; VLOG(1) << "Terminating: Gradient tolerance reached." << "Relative gradient max norm: " - << iteration_summary.gradient_max_norm / gradient_max_norm_0 + << iteration_summary.gradient_max_norm / initial_gradient_max_norm << " <= " << options_.gradient_tolerance; return; } @@ -203,24 +167,20 @@ void TrustRegionMinimizer::Minimize(const Minimizer::Options& options, + summary->preprocessor_time_in_seconds; summary->iterations.push_back(iteration_summary); - // Call the various callbacks. - switch (RunCallbacks(iteration_summary)) { - case SOLVER_TERMINATE_SUCCESSFULLY: - summary->termination_type = USER_SUCCESS; - VLOG(1) << "Terminating: User callback returned USER_SUCCESS."; - return; - case SOLVER_ABORT: - summary->termination_type = USER_ABORT; - VLOG(1) << "Terminating: User callback returned USER_ABORT."; - return; - case SOLVER_CONTINUE: - break; - default: - LOG(FATAL) << "Unknown type of user callback status"; + if (options_.jacobi_scaling) { + EstimateScale(*jacobian, scale.data()); + jacobian->ScaleColumns(scale.data()); + } else { + scale.setOnes(); } int num_consecutive_invalid_steps = 0; + bool inner_iterations_are_enabled = options.inner_iteration_minimizer != NULL; while (true) { + if (!RunCallbacks(options.callbacks, iteration_summary, summary)) { + return; + } + iteration_start_time = WallTimeInSeconds(); if (iteration_summary.iteration >= options_.max_num_iterations) { summary->termination_type = NO_CONVERGENCE; @@ -236,33 +196,38 @@ void TrustRegionMinimizer::Minimize(const Minimizer::Options& options, break; } - iteration_summary = IterationSummary(); - iteration_summary = summary->iterations.back(); - iteration_summary.iteration = summary->iterations.back().iteration + 1; - iteration_summary.step_is_valid = false; - iteration_summary.step_is_successful = false; - const double strategy_start_time = WallTimeInSeconds(); TrustRegionStrategy::PerSolveOptions per_solve_options; per_solve_options.eta = options_.eta; + if (find(options_.trust_region_minimizer_iterations_to_dump.begin(), + options_.trust_region_minimizer_iterations_to_dump.end(), + iteration_summary.iteration) != + options_.trust_region_minimizer_iterations_to_dump.end()) { + per_solve_options.dump_format_type = + options_.trust_region_problem_dump_format_type; + per_solve_options.dump_filename_base = + JoinPath(options_.trust_region_problem_dump_directory, + StringPrintf("ceres_solver_iteration_%03d", + iteration_summary.iteration)); + } else { + per_solve_options.dump_format_type = TEXTFILE; + per_solve_options.dump_filename_base.clear(); + } + TrustRegionStrategy::Summary strategy_summary = strategy->ComputeStep(per_solve_options, jacobian, residuals.data(), trust_region_step.data()); + iteration_summary = IterationSummary(); + iteration_summary.iteration = summary->iterations.back().iteration + 1; iteration_summary.step_solver_time_in_seconds = WallTimeInSeconds() - strategy_start_time; iteration_summary.linear_solver_iterations = strategy_summary.num_iterations; - - if (!MaybeDumpLinearLeastSquaresProblem(iteration_summary.iteration, - jacobian, - residuals.data(), - trust_region_step.data())) { - LOG(FATAL) << "Tried writing linear least squares problem: " - << options.lsqp_dump_directory << "but failed."; - } + iteration_summary.step_is_valid = false; + iteration_summary.step_is_successful = false; double model_cost_change = 0.0; if (strategy_summary.termination_type != FAILURE) { @@ -342,27 +307,43 @@ void TrustRegionMinimizer::Minimize(const Minimizer::Options& options, new_cost = numeric_limits<double>::max(); } else { // Check if performing an inner iteration will make it better. - if (options.inner_iteration_minimizer != NULL) { + if (inner_iterations_are_enabled) { + ++summary->num_inner_iteration_steps; + double inner_iteration_start_time = WallTimeInSeconds(); const double x_plus_delta_cost = new_cost; Vector inner_iteration_x = x_plus_delta; Solver::Summary inner_iteration_summary; options.inner_iteration_minimizer->Minimize(options, inner_iteration_x.data(), &inner_iteration_summary); - if(!evaluator->Evaluate(inner_iteration_x.data(), - &new_cost, - NULL, NULL, NULL)) { + if (!evaluator->Evaluate(inner_iteration_x.data(), + &new_cost, + NULL, NULL, NULL)) { VLOG(2) << "Inner iteration failed."; new_cost = x_plus_delta_cost; } else { x_plus_delta = inner_iteration_x; - // Bost the model_cost_change, since the inner iteration + // Boost the model_cost_change, since the inner iteration // improvements are not accounted for by the trust region. model_cost_change += x_plus_delta_cost - new_cost; VLOG(2) << "Inner iteration succeeded; current cost: " << cost << " x_plus_delta_cost: " << x_plus_delta_cost << " new_cost: " << new_cost; + const double inner_iteration_relative_progress = + (x_plus_delta_cost - new_cost) / x_plus_delta_cost; + inner_iterations_are_enabled = + (inner_iteration_relative_progress > + options.inner_iteration_tolerance); + + // Disable inner iterations once the relative improvement + // drops below tolerance. + if (!inner_iterations_are_enabled) { + VLOG(2) << "Disabling inner iterations. Progress : " + << inner_iteration_relative_progress; + } } + summary->inner_iteration_time_in_seconds += + WallTimeInSeconds() - inner_iteration_start_time; } } @@ -381,7 +362,6 @@ void TrustRegionMinimizer::Minimize(const Minimizer::Options& options, return; } - VLOG(2) << "old cost: " << cost << " new cost: " << new_cost; iteration_summary.cost_change = cost - new_cost; const double absolute_function_tolerance = options_.function_tolerance * cost; @@ -445,23 +425,23 @@ void TrustRegionMinimizer::Minimize(const Minimizer::Options& options, if (!evaluator->Evaluate(x.data(), &cost, residuals.data(), - NULL, + gradient.data(), jacobian)) { summary->termination_type = NUMERICAL_FAILURE; - summary->error = "Terminating: Residual and Jacobian evaluation failed."; + summary->error = + "Terminating: Residual and Jacobian evaluation failed."; LOG(WARNING) << summary->error; return; } - gradient.setZero(); - jacobian->LeftMultiply(residuals.data(), gradient.data()); iteration_summary.gradient_max_norm = gradient.lpNorm<Eigen::Infinity>(); if (iteration_summary.gradient_max_norm <= absolute_gradient_tolerance) { summary->termination_type = GRADIENT_TOLERANCE; VLOG(1) << "Terminating: Gradient tolerance reached." << "Relative gradient max norm: " - << iteration_summary.gradient_max_norm / gradient_max_norm_0 + << (iteration_summary.gradient_max_norm / + initial_gradient_max_norm) << " <= " << options_.gradient_tolerance; return; } @@ -534,21 +514,6 @@ void TrustRegionMinimizer::Minimize(const Minimizer::Options& options, WallTimeInSeconds() - start_time + summary->preprocessor_time_in_seconds; summary->iterations.push_back(iteration_summary); - - switch (RunCallbacks(iteration_summary)) { - case SOLVER_TERMINATE_SUCCESSFULLY: - summary->termination_type = USER_SUCCESS; - VLOG(1) << "Terminating: User callback returned USER_SUCCESS."; - return; - case SOLVER_ABORT: - summary->termination_type = USER_ABORT; - VLOG(1) << "Terminating: User callback returned USER_ABORT."; - return; - case SOLVER_CONTINUE: - break; - default: - LOG(FATAL) << "Unknown type of user callback status"; - } } } |