Pipe minimizer termination messages to Solver::Summary.
All minimizer termination messages are now available as
Solver::Summary::error.
This is part of the ongoing refactoring or
Change-Id: I4514c3c042645bbd1471bcde9bd3dbf81d9ee8b0
diff --git a/internal/ceres/line_search_minimizer.cc b/internal/ceres/line_search_minimizer.cc
index 1093bad..4590afe 100644
--- a/internal/ceres/line_search_minimizer.cc
+++ b/internal/ceres/line_search_minimizer.cc
@@ -126,9 +126,9 @@
// Do initial cost and Jacobian evaluation.
if (!Evaluate(evaluator, x, ¤t_state)) {
- LOG_IF(WARNING, is_not_silent)
- << "Terminating: Cost and gradient evaluation failed.";
+ summary->error = "Terminating: Cost and gradient evaluation failed.";
summary->termination_type = NUMERICAL_FAILURE;
+ LOG_IF(WARNING, is_not_silent) << summary->error;
return;
}
@@ -146,12 +146,14 @@
options.gradient_tolerance * initial_gradient_max_norm;
if (iteration_summary.gradient_max_norm <= absolute_gradient_tolerance) {
- VLOG_IF(1, is_not_silent)
- << "Terminating: Gradient tolerance reached."
- << "Relative gradient max norm: "
- << iteration_summary.gradient_max_norm / initial_gradient_max_norm
- << " <= " << options.gradient_tolerance;
+ summary->error =
+ StringPrintf("Terminating: Gradient tolerance reached. "
+ "Relative gradient max norm: %e <= %e",
+ iteration_summary.gradient_max_norm /
+ initial_gradient_max_norm,
+ options.gradient_tolerance);
summary->termination_type = GRADIENT_TOLERANCE;
+ VLOG_IF(1, is_not_silent) << summary->error;
return;
}
@@ -198,10 +200,8 @@
line_search_options,
&summary->error));
if (line_search.get() == NULL) {
- LOG_IF(ERROR, is_not_silent)
- << "Ceres bug: Unable to create a LineSearch object, please "
- << "contact the developers!, error: " << summary->error;
summary->termination_type = DID_NOT_RUN;
+ LOG_IF(ERROR, is_not_silent) << summary->error;
return;
}
@@ -215,17 +215,18 @@
iteration_start_time = WallTimeInSeconds();
if (iteration_summary.iteration >= options.max_num_iterations) {
- VLOG_IF(1, is_not_silent)
- << "Terminating: Maximum number of iterations reached.";
+ summary->error = "Terminating: Maximum number of iterations reached.";
summary->termination_type = NO_CONVERGENCE;
+ VLOG_IF(1, is_not_silent) << summary->error;
break;
}
const double total_solver_time = iteration_start_time - start_time +
summary->preprocessor_time_in_seconds;
if (total_solver_time >= options.max_solver_time_in_seconds) {
- VLOG_IF(1, is_not_silent) << "Terminating: Maximum solver time reached.";
+ summary->error = "Terminating: Maximum solver time reached.";
summary->termination_type = NO_CONVERGENCE;
+ VLOG_IF(1, is_not_silent) << summary->error;
break;
}
@@ -251,12 +252,11 @@
// have already reached our specified maximum number of restarts,
// terminate optimization.
summary->error =
- StringPrintf("Line search direction failure: specified "
+ StringPrintf("Termination: Line search direction failure: specified "
"max_num_line_search_direction_restarts: %d reached.",
options.max_num_line_search_direction_restarts);
- LOG_IF(WARNING, is_not_silent) << summary->error
- << " terminating optimization.";
summary->termination_type = NUMERICAL_FAILURE;
+ LOG_IF(WARNING, is_not_silent) << summary->error;
break;
} else if (!line_search_status) {
// Restart line search direction with gradient descent on first iteration
@@ -305,8 +305,8 @@
"(current_cost - previous_cost): %.5e",
initial_step_size, current_state.directional_derivative,
(current_state.cost - previous_state.cost));
- LOG_IF(WARNING, is_not_silent) << summary->error;
summary->termination_type = NUMERICAL_FAILURE;
+ LOG_IF(WARNING, is_not_silent) << summary->error;
break;
}
@@ -355,13 +355,14 @@
iteration_summary.gradient_norm = sqrt(current_state.gradient_squared_norm);
if (iteration_summary.gradient_max_norm <= absolute_gradient_tolerance) {
- VLOG_IF(1, is_not_silent)
- << "Terminating: Gradient tolerance reached."
- << "Relative gradient max norm: "
- << (iteration_summary.gradient_max_norm /
- initial_gradient_max_norm)
- << " <= " << options.gradient_tolerance;
+ summary->error =
+ StringPrintf("Terminating: Gradient tolerance reached. "
+ "Relative gradient max norm: %e <= %e. ",
+ (iteration_summary.gradient_max_norm /
+ initial_gradient_max_norm),
+ options.gradient_tolerance);
summary->termination_type = GRADIENT_TOLERANCE;
+ VLOG_IF(1, is_not_silent) << summary->error;
break;
}
@@ -369,12 +370,14 @@
const double absolute_function_tolerance =
options.function_tolerance * previous_state.cost;
if (fabs(iteration_summary.cost_change) < absolute_function_tolerance) {
- VLOG_IF(1, is_not_silent)
- << "Terminating. Function tolerance reached. "
- << "|cost_change|/cost: "
- << fabs(iteration_summary.cost_change) / previous_state.cost
- << " <= " << options.function_tolerance;
+ summary->error =
+ StringPrintf("Terminating. Function tolerance reached. "
+ "|cost_change|/cost: %e <= %e",
+ fabs(iteration_summary.cost_change) /
+ previous_state.cost,
+ options.function_tolerance);
summary->termination_type = FUNCTION_TOLERANCE;
+ VLOG_IF(1, is_not_silent) << summary->error;
return;
}
diff --git a/internal/ceres/trust_region_minimizer.cc b/internal/ceres/trust_region_minimizer.cc
index e09c008..9b630e1 100644
--- a/internal/ceres/trust_region_minimizer.cc
+++ b/internal/ceres/trust_region_minimizer.cc
@@ -117,8 +117,6 @@
iteration_summary.step_norm = 0.0;
iteration_summary.relative_decrease = 0.0;
iteration_summary.trust_region_radius = strategy->Radius();
- // TODO(sameeragarwal): Rename eta to linear_solver_accuracy or
- // something similar across the board.
iteration_summary.eta = options_.eta;
iteration_summary.linear_solver_iterations = 0;
iteration_summary.step_solver_time_in_seconds = 0;
@@ -130,9 +128,9 @@
residuals.data(),
gradient.data(),
jacobian)) {
- LOG_IF(WARNING, is_not_silent)
- << "Terminating: Residual and Jacobian evaluation failed.";
+ summary->error = "Terminating: Residual and Jacobian evaluation failed.";
summary->termination_type = NUMERICAL_FAILURE;
+ LOG_IF(WARNING, is_not_silent) << summary->error;
return;
}
@@ -156,12 +154,13 @@
options_.gradient_tolerance * initial_gradient_max_norm;
if (iteration_summary.gradient_max_norm <= absolute_gradient_tolerance) {
- VLOG_IF(1, is_not_silent) << "Terminating: Gradient tolerance reached."
- << "Relative gradient max norm: "
- << (iteration_summary.gradient_max_norm /
- initial_gradient_max_norm)
- << " <= " << options_.gradient_tolerance;
+ summary->error = StringPrintf("Terminating: Gradient tolerance reached. "
+ "Relative gradient max norm: %e <= %e",
+ (iteration_summary.gradient_max_norm /
+ initial_gradient_max_norm),
+ options_.gradient_tolerance);
summary->termination_type = GRADIENT_TOLERANCE;
+ VLOG_IF(1, is_not_silent) << summary->error;
return;
}
@@ -189,18 +188,18 @@
iteration_start_time = WallTimeInSeconds();
if (iteration_summary.iteration >= options_.max_num_iterations) {
- VLOG_IF(1, is_not_silent)
- << "Terminating: Maximum number of iterations reached.";
+ summary->error = "Terminating: Maximum number of iterations reached.";
summary->termination_type = NO_CONVERGENCE;
+ VLOG_IF(1, is_not_silent) << summary->error;
return;
}
const double total_solver_time = iteration_start_time - start_time +
summary->preprocessor_time_in_seconds;
if (total_solver_time >= options_.max_solver_time_in_seconds) {
- VLOG_IF(1, is_not_silent)
- << "Terminating: Maximum solver time reached.";
+ summary->error = "Terminating: Maximum solver time reached.";
summary->termination_type = NO_CONVERGENCE;
+ VLOG_IF(1, is_not_silent) << summary->error;
return;
}
@@ -237,14 +236,16 @@
iteration_summary.step_is_valid = false;
iteration_summary.step_is_successful = false;
- double model_cost_change = 0.0;
if (strategy_summary.termination_type == FATAL_ERROR) {
- summary->error = "Terminating. Linear solver encountered a fatal error.";
- LOG_IF(WARNING, is_not_silent) << summary->error;
+ summary->error =
+ "Terminating. Linear solver failed due to unrecoverable "
+ "non-numeric causes. Please see the error log for clues. ";
summary->termination_type = NUMERICAL_FAILURE;
+ LOG_IF(WARNING, is_not_silent) << summary->error;
return;
}
+ double model_cost_change = 0.0;
if (strategy_summary.termination_type != FAILURE) {
// new_model_cost
// = 1/2 [f + J * step]^2
@@ -278,8 +279,8 @@
"Terminating. Number of successive invalid steps more "
"than Solver::Options::max_num_consecutive_invalid_steps: %d",
options_.max_num_consecutive_invalid_steps);
- LOG_IF(WARNING, is_not_silent) << summary->error;
summary->termination_type = NUMERICAL_FAILURE;
+ LOG_IF(WARNING, is_not_silent) << summary->error;
return;
}
@@ -366,13 +367,14 @@
const double step_size_tolerance = options_.parameter_tolerance *
(x_norm + options_.parameter_tolerance);
if (iteration_summary.step_norm <= step_size_tolerance) {
- VLOG_IF(1, is_not_silent)
- << "Terminating. Parameter tolerance reached. "
- << "relative step_norm: "
- << (iteration_summary.step_norm /
- (x_norm + options_.parameter_tolerance))
- << " <= " << options_.parameter_tolerance;
+ summary->error =
+ StringPrintf("Terminating. Parameter tolerance reached. "
+ "relative step_norm: %e <= %e.",
+ (iteration_summary.step_norm /
+ (x_norm + options_.parameter_tolerance)),
+ options_.parameter_tolerance);
summary->termination_type = PARAMETER_TOLERANCE;
+ VLOG_IF(1, is_not_silent) << summary->error;
return;
}
@@ -380,11 +382,13 @@
const double absolute_function_tolerance =
options_.function_tolerance * cost;
if (fabs(iteration_summary.cost_change) < absolute_function_tolerance) {
- VLOG_IF(1, is_not_silent) << "Terminating. Function tolerance reached. "
- << "|cost_change|/cost: "
- << fabs(iteration_summary.cost_change) / cost
- << " <= " << options_.function_tolerance;
+ summary->error =
+ StringPrintf("Terminating. Function tolerance reached. "
+ "|cost_change|/cost: %e <= %e",
+ fabs(iteration_summary.cost_change) / cost,
+ options_.function_tolerance);
summary->termination_type = FUNCTION_TOLERANCE;
+ VLOG_IF(1, is_not_silent) << summary->error;
return;
}
@@ -483,8 +487,8 @@
jacobian)) {
summary->error =
"Terminating: Residual and Jacobian evaluation failed.";
- LOG_IF(WARNING, is_not_silent) << summary->error;
summary->termination_type = NUMERICAL_FAILURE;
+ LOG_IF(WARNING, is_not_silent) << summary->error;
return;
}
@@ -492,12 +496,14 @@
iteration_summary.gradient_norm = gradient.norm();
if (iteration_summary.gradient_max_norm <= absolute_gradient_tolerance) {
- VLOG_IF(1, is_not_silent) << "Terminating: Gradient tolerance reached."
- << "Relative gradient max norm: "
- << (iteration_summary.gradient_max_norm /
- initial_gradient_max_norm)
- << " <= " << options_.gradient_tolerance;
+ summary->error =
+ StringPrintf("Terminating: Gradient tolerance reached. "
+ "Relative gradient max norm: %e <= %e",
+ (iteration_summary.gradient_max_norm /
+ initial_gradient_max_norm),
+ options_.gradient_tolerance);
summary->termination_type = GRADIENT_TOLERANCE;
+ VLOG_IF(1, is_not_silent) << summary->error;
return;
}
@@ -560,9 +566,9 @@
iteration_summary.trust_region_radius = strategy->Radius();
if (iteration_summary.trust_region_radius <
options_.min_trust_region_radius) {
- VLOG_IF(1, is_not_silent)
- << "Termination. Minimum trust region radius reached.";
+ summary->error = "Termination. Minimum trust region radius reached.";
summary->termination_type = PARAMETER_TOLERANCE;
+ VLOG_IF(1, is_not_silent) << summary->error;
return;
}
