blob: 0ca262504fd7b9dc6f3f15657f05cb31f6d1e2c7 [file] [log] [blame]
// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2018 Google Inc. All rights reserved.
// http://ceres-solver.org/
//
// 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: mierle@gmail.com (Keir Mierle)
#include "ceres/evaluation_callback.h"
#include <cmath>
#include <limits>
#include <vector>
#include "ceres/autodiff_cost_function.h"
#include "ceres/problem.h"
#include "ceres/problem_impl.h"
#include "ceres/sized_cost_function.h"
#include "ceres/solver.h"
#include "gtest/gtest.h"
namespace ceres {
namespace internal {
// Use an inline hash function to avoid portability wrangling. Algorithm from
// Daniel Bernstein, known as the "djb2" hash.
template <typename T>
uint64_t Djb2Hash(const T* data, const int size) {
uint64_t hash = 5381;
const uint8_t* data_as_bytes = reinterpret_cast<const uint8_t*>(data);
for (int i = 0; i < sizeof(*data) * size; ++i) {
hash = hash * 33 + data_as_bytes[i];
}
return hash;
}
const double kUninitialized = 0;
// Generally multiple inheritance is a terrible idea, but in this (test)
// case it makes for a relatively elegant test implementation.
struct WigglyBowlCostFunctionAndEvaluationCallback : SizedCostFunction<2, 2>,
EvaluationCallback {
explicit WigglyBowlCostFunctionAndEvaluationCallback(double* parameter)
: EvaluationCallback(),
user_parameter_block(parameter),
prepare_num_calls(0),
prepare_requested_jacobians(false),
prepare_new_evaluation_point(false),
prepare_parameter_hash(kUninitialized),
evaluate_num_calls(0),
evaluate_last_parameter_hash(kUninitialized) {}
virtual ~WigglyBowlCostFunctionAndEvaluationCallback() {}
// Evaluation callback interface. This checks that all the preconditions are
// met at the point that Ceres calls into it.
void PrepareForEvaluation(bool evaluate_jacobians,
bool new_evaluation_point) final {
// At this point, the incoming parameters are implicitly pushed by Ceres
// into the user parameter blocks; in contrast to in Evaluate().
uint64_t incoming_parameter_hash = Djb2Hash(user_parameter_block, 2);
// Check: Prepare() & Evaluate() come in pairs, in that order. Before this
// call, the number of calls excluding this one should match.
EXPECT_EQ(prepare_num_calls, evaluate_num_calls);
// Check: new_evaluation_point indicates that the parameter has changed.
if (new_evaluation_point) {
// If it's a new evaluation point, then the parameter should have
// changed. Technically, it's not required that it must change but
// in practice it does, and that helps with testing.
EXPECT_NE(evaluate_last_parameter_hash, incoming_parameter_hash);
EXPECT_NE(prepare_parameter_hash, incoming_parameter_hash);
} else {
// If this is the same evaluation point as last time, ensure that
// the parameters match both from the previous evaluate, the
// previous prepare, and the current prepare.
EXPECT_EQ(evaluate_last_parameter_hash, prepare_parameter_hash);
EXPECT_EQ(evaluate_last_parameter_hash, incoming_parameter_hash);
}
// Save details for to check at the next call to Evaluate().
prepare_num_calls++;
prepare_requested_jacobians = evaluate_jacobians;
prepare_new_evaluation_point = new_evaluation_point;
prepare_parameter_hash = incoming_parameter_hash;
}
// Cost function interface. This checks that preconditions that were
// set as part of the PrepareForEvaluation() call are met in this one.
bool Evaluate(double const* const* parameters,
double* residuals,
double** jacobians) const final {
// Cost function implementation of the "Wiggly Bowl" function:
//
// 1/2 * [(y - a*sin(x))^2 + x^2],
//
// expressed as a Ceres cost function with two residuals:
//
// r[0] = y - a*sin(x)
// r[1] = x.
//
// This is harder to optimize than the Rosenbrock function because the
// minimizer has to navigate a sine-shaped valley while descending the 1D
// parabola formed along the y axis. Note that the "a" needs to be more
// than 5 to get a strong enough wiggle effect in the cost surface to
// trigger failed iterations in the optimizer.
const double a = 10.0;
double x = (*parameters)[0];
double y = (*parameters)[1];
residuals[0] = y - a * sin(x);
residuals[1] = x;
if (jacobians != NULL) {
(*jacobians)[2 * 0 + 0] = -a * cos(x); // df1/dx
(*jacobians)[2 * 0 + 1] = 1.0; // df1/dy
(*jacobians)[2 * 1 + 0] = 1.0; // df2/dx
(*jacobians)[2 * 1 + 1] = 0.0; // df2/dy
}
uint64_t incoming_parameter_hash = Djb2Hash(*parameters, 2);
// Check: PrepareForEvaluation() & Evaluate() come in pairs, in that order.
EXPECT_EQ(prepare_num_calls, evaluate_num_calls + 1);
// Check: if new_evaluation_point indicates that the parameter has
// changed, it has changed; otherwise it is the same.
if (prepare_new_evaluation_point) {
EXPECT_NE(evaluate_last_parameter_hash, incoming_parameter_hash);
} else {
EXPECT_NE(evaluate_last_parameter_hash, kUninitialized);
EXPECT_EQ(evaluate_last_parameter_hash, incoming_parameter_hash);
}
// Check: Parameter matches value in in parameter blocks during prepare.
EXPECT_EQ(prepare_parameter_hash, incoming_parameter_hash);
// Check: jacobians are requested if they were in PrepareForEvaluation().
EXPECT_EQ(prepare_requested_jacobians, jacobians != NULL);
evaluate_num_calls++;
evaluate_last_parameter_hash = incoming_parameter_hash;
return true;
}
// Pointer to the parameter block associated with this cost function.
// Contents should get set by Ceres before calls to PrepareForEvaluation()
// and Evaluate().
double* user_parameter_block;
// Track state: PrepareForEvaluation().
//
// These track details from the PrepareForEvaluation() call (hence the
// "prepare_" prefix), which are checked for consistency in Evaluate().
int prepare_num_calls;
bool prepare_requested_jacobians;
bool prepare_new_evaluation_point;
uint64_t prepare_parameter_hash;
// Track state: Evaluate().
//
// These track details from the Evaluate() call (hence the "evaluate_"
// prefix), which are then checked for consistency in the calls to
// PrepareForEvaluation(). Mutable is reasonable for this case.
mutable int evaluate_num_calls;
mutable uint64_t evaluate_last_parameter_hash;
};
TEST(EvaluationCallback, WithTrustRegionMinimizer) {
double parameters[2] = {50.0, 50.0};
const uint64_t original_parameters_hash = Djb2Hash(parameters, 2);
WigglyBowlCostFunctionAndEvaluationCallback cost_function(parameters);
Problem::Options problem_options;
problem_options.evaluation_callback = &cost_function;
problem_options.cost_function_ownership = DO_NOT_TAKE_OWNERSHIP;
Problem problem(problem_options);
problem.AddResidualBlock(&cost_function, NULL, parameters);
Solver::Options options;
options.linear_solver_type = DENSE_QR;
options.max_num_iterations = 50;
// Run the solve. Checking is done inside the cost function / callback.
Solver::Summary summary;
Solve(options, &problem, &summary);
// Ensure that this was a hard cost function (not all steps succeed).
EXPECT_GT(summary.num_successful_steps, 10);
EXPECT_GT(summary.num_unsuccessful_steps, 10);
// Ensure PrepareForEvaluation() is called the appropriate number of times.
EXPECT_EQ(
cost_function.prepare_num_calls,
// Unsuccessful steps are evaluated only once (no jacobians).
summary.num_unsuccessful_steps +
// Successful steps are evaluated twice: with and without jacobians.
2 * summary.num_successful_steps
// Final iteration doesn't re-evaluate the jacobian.
// Note: This may be sensitive to tweaks to the TR algorithm; if
// this becomes too brittle, remove this EXPECT_EQ() entirely.
- 1);
// Ensure the callback calls ran a reasonable number of times.
EXPECT_GT(cost_function.prepare_num_calls, 0);
EXPECT_GT(cost_function.evaluate_num_calls, 0);
EXPECT_EQ(cost_function.prepare_num_calls, cost_function.evaluate_num_calls);
// Ensure that the parameters did actually change.
EXPECT_NE(Djb2Hash(parameters, 2), original_parameters_hash);
}
// r = 1 - x
struct LinearResidual {
template <typename T>
bool operator()(const T* x, T* residuals) const {
residuals[0] = 1.0 - x[0];
return true;
}
static CostFunction* Create() {
return new AutoDiffCostFunction<LinearResidual, 1, 1>(new LinearResidual);
};
};
// Increments a counter everytime PrepareForEvaluation is called.
class IncrementingEvaluationCallback : public EvaluationCallback {
public:
void PrepareForEvaluation(bool evaluate_jacobians,
bool new_evaluation_point) final {
(void)evaluate_jacobians;
(void)new_evaluation_point;
counter_ += 1.0;
}
const double counter() const { return counter_; }
private:
double counter_ = -1;
};
// r = IncrementingEvaluationCallback::counter - x
struct EvaluationCallbackResidual {
explicit EvaluationCallbackResidual(
const IncrementingEvaluationCallback& callback)
: callback(callback) {}
template <typename T>
bool operator()(const T* x, T* residuals) const {
residuals[0] = callback.counter() - x[0];
return true;
}
const IncrementingEvaluationCallback& callback;
static CostFunction* Create(IncrementingEvaluationCallback& callback) {
return new AutoDiffCostFunction<EvaluationCallbackResidual, 1, 1>(
new EvaluationCallbackResidual(callback));
};
};
// The following test, constructs a problem with residual blocks all
// of whose parameters are constant, so they are evaluated once
// outside the Minimizer to compute Solver::Summary::fixed_cost.
//
// The cost function for this residual block depends on the
// IncrementingEvaluationCallback::counter_, by checking the value of
// the fixed cost, we can check if the IncrementingEvaluationCallback
// was called.
TEST(EvaluationCallback, EvaluationCallbackIsCalledBeforeFixedCostIsEvaluated) {
double x = 1;
double y = 2;
std::unique_ptr<IncrementingEvaluationCallback> callback(
new IncrementingEvaluationCallback);
Problem::Options problem_options;
problem_options.evaluation_callback = callback.get();
Problem problem(problem_options);
problem.AddResidualBlock(LinearResidual::Create(), nullptr, &x);
problem.AddResidualBlock(
EvaluationCallbackResidual::Create(*callback), nullptr, &y);
problem.SetParameterBlockConstant(&y);
Solver::Options options;
options.linear_solver_type = DENSE_QR;
Solver::Summary summary;
Solve(options, &problem, &summary);
EXPECT_EQ(summary.fixed_cost, 2.0);
EXPECT_EQ(summary.final_cost, summary.fixed_cost);
EXPECT_GT(callback->counter(), 0);
}
static void WithLineSearchMinimizerImpl(
LineSearchType line_search,
LineSearchDirectionType line_search_direction,
LineSearchInterpolationType line_search_interpolation) {
double parameters[2] = {50.0, 50.0};
const uint64_t original_parameters_hash = Djb2Hash(parameters, 2);
WigglyBowlCostFunctionAndEvaluationCallback cost_function(parameters);
Problem::Options problem_options;
problem_options.evaluation_callback = &cost_function;
problem_options.cost_function_ownership = DO_NOT_TAKE_OWNERSHIP;
Problem problem(problem_options);
problem.AddResidualBlock(&cost_function, NULL, parameters);
Solver::Options options;
options.linear_solver_type = DENSE_QR;
options.max_num_iterations = 50;
options.minimizer_type = ceres::LINE_SEARCH;
options.line_search_type = line_search;
options.line_search_direction_type = line_search_direction;
options.line_search_interpolation_type = line_search_interpolation;
// Run the solve. Checking is done inside the cost function / callback.
Solver::Summary summary;
Solve(options, &problem, &summary);
// Ensure the callback calls ran a reasonable number of times.
EXPECT_GT(summary.num_line_search_steps, 10);
EXPECT_GT(cost_function.prepare_num_calls, 30);
EXPECT_EQ(cost_function.prepare_num_calls, cost_function.evaluate_num_calls);
// Ensure that the parameters did actually change.
EXPECT_NE(Djb2Hash(parameters, 2), original_parameters_hash);
}
// Note: These tests omit combinations of Wolfe line search with bisection.
// Due to an implementation quirk in Wolfe line search with bisection, there
// are calls to re-evaluate an existing point with new_point = true. That
// causes the (overly) strict tests to break, since they check the new_point
// preconditions in an if-and-only-if way. Strictly speaking, if new_point =
// true, the interface does not *require* that the point has changed; only that
// if new_point = false, the same point is reused.
//
// Since the strict checking is useful to verify that there aren't missed
// optimizations, omit tests of the Wolfe with bisection cases.
// Wolfe with L-BFGS.
TEST(EvaluationCallback, WithLineSearchMinimizerWolfeLbfgsCubic) {
WithLineSearchMinimizerImpl(WOLFE, LBFGS, CUBIC);
}
TEST(EvaluationCallback, WithLineSearchMinimizerWolfeLbfgsQuadratic) {
WithLineSearchMinimizerImpl(WOLFE, LBFGS, QUADRATIC);
}
// Wolfe with full BFGS.
TEST(EvaluationCallback, WithLineSearchMinimizerWolfeBfgsCubic) {
WithLineSearchMinimizerImpl(WOLFE, BFGS, CUBIC);
}
TEST(EvaluationCallback, WithLineSearchMinimizerWolfeBfgsQuadratic) {
WithLineSearchMinimizerImpl(WOLFE, BFGS, QUADRATIC);
}
// Armijo with nonlinear conjugate gradient.
TEST(EvaluationCallback, WithLineSearchMinimizerArmijoCubic) {
WithLineSearchMinimizerImpl(ARMIJO, NONLINEAR_CONJUGATE_GRADIENT, CUBIC);
}
TEST(EvaluationCallback, WithLineSearchMinimizerArmijoBisection) {
WithLineSearchMinimizerImpl(ARMIJO, NONLINEAR_CONJUGATE_GRADIENT, BISECTION);
}
TEST(EvaluationCallback, WithLineSearchMinimizerArmijoQuadratic) {
WithLineSearchMinimizerImpl(ARMIJO, NONLINEAR_CONJUGATE_GRADIENT, QUADRATIC);
}
} // namespace internal
} // namespace ceres