diff options
Diffstat (limited to 'extern/ceres/internal/ceres/line_search_minimizer.cc')
| -rw-r--r-- | extern/ceres/internal/ceres/line_search_minimizer.cc | 122 |
1 files changed, 63 insertions, 59 deletions
diff --git a/extern/ceres/internal/ceres/line_search_minimizer.cc b/extern/ceres/internal/ceres/line_search_minimizer.cc index fdde1ca9c86..931f56c960c 100644 --- a/extern/ceres/internal/ceres/line_search_minimizer.cc +++ b/extern/ceres/internal/ceres/line_search_minimizer.cc @@ -43,6 +43,7 @@ #include <algorithm> #include <cstdlib> #include <cmath> +#include <memory> #include <string> #include <vector> @@ -51,7 +52,6 @@ #include "ceres/evaluator.h" #include "ceres/internal/eigen.h" #include "ceres/internal/port.h" -#include "ceres/internal/scoped_ptr.h" #include "ceres/line_search.h" #include "ceres/line_search_direction.h" #include "ceres/stringprintf.h" @@ -63,27 +63,14 @@ namespace ceres { namespace internal { namespace { -// TODO(sameeragarwal): I think there is a small bug here, in that if -// the evaluation fails, then the state can contain garbage. Look at -// this more carefully. -bool Evaluate(Evaluator* evaluator, - const Vector& x, - LineSearchMinimizer::State* state, - std::string* message) { - if (!evaluator->Evaluate(x.data(), - &(state->cost), - NULL, - state->gradient.data(), - NULL)) { - *message = "Gradient evaluation failed."; - return false; - } - +bool EvaluateGradientNorms(Evaluator* evaluator, + const Vector& x, + LineSearchMinimizer::State* state, + std::string* message) { Vector negative_gradient = -state->gradient; Vector projected_gradient_step(x.size()); - if (!evaluator->Plus(x.data(), - negative_gradient.data(), - projected_gradient_step.data())) { + if (!evaluator->Plus( + x.data(), negative_gradient.data(), projected_gradient_step.data())) { *message = "projected_gradient_step = Plus(x, -gradient) failed."; return false; } @@ -103,7 +90,8 @@ void LineSearchMinimizer::Minimize(const Minimizer::Options& options, double start_time = WallTimeInSeconds(); double iteration_start_time = start_time; - Evaluator* evaluator = CHECK_NOTNULL(options.evaluator.get()); + CHECK(options.evaluator != nullptr); + Evaluator* evaluator = options.evaluator.get(); const int num_parameters = evaluator->NumParameters(); const int num_effective_parameters = evaluator->NumEffectiveParameters(); @@ -116,9 +104,6 @@ void LineSearchMinimizer::Minimize(const Minimizer::Options& options, State current_state(num_parameters, num_effective_parameters); State previous_state(num_parameters, num_effective_parameters); - Vector delta(num_effective_parameters); - Vector x_plus_delta(num_parameters); - IterationSummary iteration_summary; iteration_summary.iteration = 0; iteration_summary.step_is_valid = false; @@ -130,8 +115,19 @@ void LineSearchMinimizer::Minimize(const Minimizer::Options& options, iteration_summary.linear_solver_iterations = 0; iteration_summary.step_solver_time_in_seconds = 0; - // Do initial cost and Jacobian evaluation. - if (!Evaluate(evaluator, x, ¤t_state, &summary->message)) { + // Do initial cost and gradient evaluation. + if (!evaluator->Evaluate(x.data(), + &(current_state.cost), + nullptr, + current_state.gradient.data(), + nullptr)) { + summary->termination_type = FAILURE; + summary->message = "Initial cost and jacobian evaluation failed."; + LOG_IF(WARNING, is_not_silent) << "Terminating: " << summary->message; + return; + } + + if (!EvaluateGradientNorms(evaluator, x, ¤t_state, &summary->message)) { summary->termination_type = FAILURE; summary->message = "Initial cost and jacobian evaluation failed. " "More details: " + summary->message; @@ -142,9 +138,8 @@ void LineSearchMinimizer::Minimize(const Minimizer::Options& options, summary->initial_cost = current_state.cost + summary->fixed_cost; iteration_summary.cost = current_state.cost + summary->fixed_cost; - iteration_summary.gradient_max_norm = current_state.gradient_max_norm; iteration_summary.gradient_norm = sqrt(current_state.gradient_squared_norm); - + iteration_summary.gradient_max_norm = current_state.gradient_max_norm; if (iteration_summary.gradient_max_norm <= options.gradient_tolerance) { summary->message = StringPrintf("Gradient tolerance reached. " "Gradient max norm: %e <= %e", @@ -170,7 +165,7 @@ void LineSearchMinimizer::Minimize(const Minimizer::Options& options, line_search_direction_options.max_lbfgs_rank = options.max_lbfgs_rank; line_search_direction_options.use_approximate_eigenvalue_bfgs_scaling = options.use_approximate_eigenvalue_bfgs_scaling; - scoped_ptr<LineSearchDirection> line_search_direction( + std::unique_ptr<LineSearchDirection> line_search_direction( LineSearchDirection::Create(line_search_direction_options)); LineSearchFunction line_search_function(evaluator); @@ -194,11 +189,11 @@ void LineSearchMinimizer::Minimize(const Minimizer::Options& options, line_search_options.is_silent = options.is_silent; line_search_options.function = &line_search_function; - scoped_ptr<LineSearch> + std::unique_ptr<LineSearch> line_search(LineSearch::Create(options.line_search_type, line_search_options, &summary->message)); - if (line_search.get() == NULL) { + if (line_search.get() == nullptr) { summary->termination_type = FAILURE; LOG_IF(ERROR, is_not_silent) << "Terminating: " << summary->message; return; @@ -326,28 +321,37 @@ void LineSearchMinimizer::Minimize(const Minimizer::Options& options, break; } - current_state.step_size = line_search_summary.optimal_step_size; - delta = current_state.step_size * current_state.search_direction; - + const FunctionSample& optimal_point = line_search_summary.optimal_point; + CHECK(optimal_point.vector_x_is_valid) + << "Congratulations, you found a bug in Ceres. Please report it."; + current_state.step_size = optimal_point.x; previous_state = current_state; iteration_summary.step_solver_time_in_seconds = WallTimeInSeconds() - iteration_start_time; - const double x_norm = x.norm(); - - if (!evaluator->Plus(x.data(), delta.data(), x_plus_delta.data())) { - summary->termination_type = FAILURE; - summary->message = - "x_plus_delta = Plus(x, delta) failed. This should not happen " - "as the step was valid when it was selected by the line search."; - LOG_IF(WARNING, is_not_silent) << "Terminating: " << summary->message; - break; + if (optimal_point.vector_gradient_is_valid) { + current_state.cost = optimal_point.value; + current_state.gradient = optimal_point.vector_gradient; + } else { + Evaluator::EvaluateOptions evaluate_options; + evaluate_options.new_evaluation_point = false; + if (!evaluator->Evaluate(evaluate_options, + optimal_point.vector_x.data(), + &(current_state.cost), + nullptr, + current_state.gradient.data(), + nullptr)) { + summary->termination_type = FAILURE; + summary->message = "Cost and jacobian evaluation failed."; + LOG_IF(WARNING, is_not_silent) << "Terminating: " << summary->message; + return; + } } - if (!Evaluate(evaluator, - x_plus_delta, - ¤t_state, - &summary->message)) { + if (!EvaluateGradientNorms(evaluator, + optimal_point.vector_x, + ¤t_state, + &summary->message)) { summary->termination_type = FAILURE; summary->message = "Step failed to evaluate. This should not happen as the step was " @@ -358,8 +362,9 @@ void LineSearchMinimizer::Minimize(const Minimizer::Options& options, } // Compute the norm of the step in the ambient space. - iteration_summary.step_norm = (x_plus_delta - x).norm(); - x = x_plus_delta; + iteration_summary.step_norm = (optimal_point.vector_x - x).norm(); + const double x_norm = x.norm(); + x = optimal_point.vector_x; iteration_summary.gradient_max_norm = current_state.gradient_max_norm; iteration_summary.gradient_norm = sqrt(current_state.gradient_squared_norm); @@ -380,6 +385,7 @@ void LineSearchMinimizer::Minimize(const Minimizer::Options& options, iteration_summary.cumulative_time_in_seconds = WallTimeInSeconds() - start_time + summary->preprocessor_time_in_seconds; + summary->iterations.push_back(iteration_summary); // Iterations inside the line search algorithm are considered // 'steps' in the broader context, to distinguish these inner @@ -423,20 +429,18 @@ void LineSearchMinimizer::Minimize(const Minimizer::Options& options, } const double absolute_function_tolerance = - options.function_tolerance * previous_state.cost; - if (fabs(iteration_summary.cost_change) <= absolute_function_tolerance) { - summary->message = - StringPrintf("Function tolerance reached. " - "|cost_change|/cost: %e <= %e", - fabs(iteration_summary.cost_change) / - previous_state.cost, - options.function_tolerance); + options.function_tolerance * std::abs(previous_state.cost); + if (std::abs(iteration_summary.cost_change) <= + absolute_function_tolerance) { + summary->message = StringPrintf( + "Function tolerance reached. " + "|cost_change|/cost: %e <= %e", + std::abs(iteration_summary.cost_change) / previous_state.cost, + options.function_tolerance); summary->termination_type = CONVERGENCE; VLOG_IF(1, is_not_silent) << "Terminating: " << summary->message; break; } - - summary->iterations.push_back(iteration_summary); } } |