403 lines
13 KiB
C++
403 lines
13 KiB
C++
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// 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/solver.h"
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#include <limits>
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#include <cmath>
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#include <vector>
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#include "gtest/gtest.h"
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#include "ceres/internal/scoped_ptr.h"
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#include "ceres/autodiff_cost_function.h"
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#include "ceres/sized_cost_function.h"
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#include "ceres/problem.h"
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#include "ceres/problem_impl.h"
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namespace ceres {
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namespace internal {
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using std::string;
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TEST(SolverOptions, DefaultTrustRegionOptionsAreValid) {
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Solver::Options options;
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options.minimizer_type = TRUST_REGION;
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string error;
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EXPECT_TRUE(options.IsValid(&error)) << error;
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}
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TEST(SolverOptions, DefaultLineSearchOptionsAreValid) {
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Solver::Options options;
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options.minimizer_type = LINE_SEARCH;
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string error;
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EXPECT_TRUE(options.IsValid(&error)) << error;
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}
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struct QuadraticCostFunctor {
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template <typename T> bool operator()(const T* const x,
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T* residual) const {
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residual[0] = T(5.0) - *x;
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return true;
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}
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static CostFunction* Create() {
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return new AutoDiffCostFunction<QuadraticCostFunctor, 1, 1>(
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new QuadraticCostFunctor);
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}
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};
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struct RememberingCallback : public IterationCallback {
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explicit RememberingCallback(double *x) : calls(0), x(x) {}
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virtual ~RememberingCallback() {}
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virtual CallbackReturnType operator()(const IterationSummary& summary) {
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x_values.push_back(*x);
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return SOLVER_CONTINUE;
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}
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int calls;
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double *x;
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std::vector<double> x_values;
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};
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TEST(Solver, UpdateStateEveryIterationOption) {
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double x = 50.0;
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const double original_x = x;
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scoped_ptr<CostFunction> cost_function(QuadraticCostFunctor::Create());
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Problem::Options problem_options;
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problem_options.cost_function_ownership = DO_NOT_TAKE_OWNERSHIP;
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Problem problem(problem_options);
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problem.AddResidualBlock(cost_function.get(), NULL, &x);
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Solver::Options options;
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options.linear_solver_type = DENSE_QR;
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RememberingCallback callback(&x);
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options.callbacks.push_back(&callback);
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Solver::Summary summary;
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int num_iterations;
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// First try: no updating.
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Solve(options, &problem, &summary);
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num_iterations = summary.num_successful_steps +
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summary.num_unsuccessful_steps;
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EXPECT_GT(num_iterations, 1);
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for (int i = 0; i < callback.x_values.size(); ++i) {
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EXPECT_EQ(50.0, callback.x_values[i]);
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}
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// Second try: with updating
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x = 50.0;
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options.update_state_every_iteration = true;
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callback.x_values.clear();
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Solve(options, &problem, &summary);
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num_iterations = summary.num_successful_steps +
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summary.num_unsuccessful_steps;
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EXPECT_GT(num_iterations, 1);
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EXPECT_EQ(original_x, callback.x_values[0]);
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EXPECT_NE(original_x, callback.x_values[1]);
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}
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// The parameters must be in separate blocks so that they can be individually
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// set constant or not.
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struct Quadratic4DCostFunction {
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template <typename T> bool operator()(const T* const x,
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const T* const y,
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const T* const z,
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const T* const w,
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T* residual) const {
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// A 4-dimension axis-aligned quadratic.
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residual[0] = T(10.0) - *x +
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T(20.0) - *y +
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T(30.0) - *z +
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T(40.0) - *w;
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return true;
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}
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static CostFunction* Create() {
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return new AutoDiffCostFunction<Quadratic4DCostFunction, 1, 1, 1, 1, 1>(
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new Quadratic4DCostFunction);
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}
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};
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// A cost function that simply returns its argument.
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class UnaryIdentityCostFunction : public SizedCostFunction<1, 1> {
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public:
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virtual bool Evaluate(double const* const* parameters,
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double* residuals,
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double** jacobians) const {
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residuals[0] = parameters[0][0];
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if (jacobians != NULL && jacobians[0] != NULL) {
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jacobians[0][0] = 1.0;
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}
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return true;
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}
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};
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TEST(Solver, TrustRegionProblemHasNoParameterBlocks) {
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Problem problem;
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Solver::Options options;
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options.minimizer_type = TRUST_REGION;
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Solver::Summary summary;
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Solve(options, &problem, &summary);
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EXPECT_EQ(summary.termination_type, CONVERGENCE);
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EXPECT_EQ(summary.message,
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"Function tolerance reached. "
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"No non-constant parameter blocks found.");
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}
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TEST(Solver, LineSearchProblemHasNoParameterBlocks) {
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Problem problem;
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Solver::Options options;
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options.minimizer_type = LINE_SEARCH;
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Solver::Summary summary;
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Solve(options, &problem, &summary);
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EXPECT_EQ(summary.termination_type, CONVERGENCE);
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EXPECT_EQ(summary.message,
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"Function tolerance reached. "
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"No non-constant parameter blocks found.");
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}
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TEST(Solver, TrustRegionProblemHasZeroResiduals) {
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Problem problem;
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double x = 1;
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problem.AddParameterBlock(&x, 1);
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Solver::Options options;
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options.minimizer_type = TRUST_REGION;
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Solver::Summary summary;
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Solve(options, &problem, &summary);
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EXPECT_EQ(summary.termination_type, CONVERGENCE);
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EXPECT_EQ(summary.message,
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"Function tolerance reached. "
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"No non-constant parameter blocks found.");
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}
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TEST(Solver, LineSearchProblemHasZeroResiduals) {
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Problem problem;
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double x = 1;
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problem.AddParameterBlock(&x, 1);
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Solver::Options options;
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options.minimizer_type = LINE_SEARCH;
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Solver::Summary summary;
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Solve(options, &problem, &summary);
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EXPECT_EQ(summary.termination_type, CONVERGENCE);
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EXPECT_EQ(summary.message,
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"Function tolerance reached. "
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"No non-constant parameter blocks found.");
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}
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TEST(Solver, TrustRegionProblemIsConstant) {
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Problem problem;
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double x = 1;
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problem.AddResidualBlock(new UnaryIdentityCostFunction, NULL, &x);
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problem.SetParameterBlockConstant(&x);
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Solver::Options options;
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options.minimizer_type = TRUST_REGION;
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Solver::Summary summary;
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Solve(options, &problem, &summary);
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EXPECT_EQ(summary.termination_type, CONVERGENCE);
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EXPECT_EQ(summary.initial_cost, 1.0 / 2.0);
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EXPECT_EQ(summary.final_cost, 1.0 / 2.0);
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}
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TEST(Solver, LineSearchProblemIsConstant) {
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Problem problem;
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double x = 1;
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problem.AddResidualBlock(new UnaryIdentityCostFunction, NULL, &x);
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problem.SetParameterBlockConstant(&x);
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Solver::Options options;
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options.minimizer_type = LINE_SEARCH;
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Solver::Summary summary;
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Solve(options, &problem, &summary);
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EXPECT_EQ(summary.termination_type, CONVERGENCE);
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EXPECT_EQ(summary.initial_cost, 1.0 / 2.0);
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EXPECT_EQ(summary.final_cost, 1.0 / 2.0);
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}
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#if defined(CERES_NO_SUITESPARSE)
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TEST(Solver, SparseNormalCholeskyNoSuiteSparse) {
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Solver::Options options;
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options.sparse_linear_algebra_library_type = SUITE_SPARSE;
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options.linear_solver_type = SPARSE_NORMAL_CHOLESKY;
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string message;
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EXPECT_FALSE(options.IsValid(&message));
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}
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TEST(Solver, SparseSchurNoSuiteSparse) {
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Solver::Options options;
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options.sparse_linear_algebra_library_type = SUITE_SPARSE;
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options.linear_solver_type = SPARSE_SCHUR;
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string message;
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EXPECT_FALSE(options.IsValid(&message));
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}
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#endif
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#if defined(CERES_NO_CXSPARSE)
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TEST(Solver, SparseNormalCholeskyNoCXSparse) {
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Solver::Options options;
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options.sparse_linear_algebra_library_type = CX_SPARSE;
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options.linear_solver_type = SPARSE_NORMAL_CHOLESKY;
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string message;
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EXPECT_FALSE(options.IsValid(&message));
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}
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TEST(Solver, SparseSchurNoCXSparse) {
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Solver::Options options;
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options.sparse_linear_algebra_library_type = CX_SPARSE;
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options.linear_solver_type = SPARSE_SCHUR;
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string message;
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EXPECT_FALSE(options.IsValid(&message));
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}
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#endif
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#if !defined(CERES_USE_EIGEN_SPARSE)
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TEST(Solver, SparseNormalCholeskyNoEigenSparse) {
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Solver::Options options;
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options.sparse_linear_algebra_library_type = EIGEN_SPARSE;
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options.linear_solver_type = SPARSE_NORMAL_CHOLESKY;
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string message;
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EXPECT_FALSE(options.IsValid(&message));
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}
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TEST(Solver, SparseSchurNoEigenSparse) {
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Solver::Options options;
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options.sparse_linear_algebra_library_type = EIGEN_SPARSE;
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options.linear_solver_type = SPARSE_SCHUR;
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string message;
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EXPECT_FALSE(options.IsValid(&message));
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}
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#endif
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TEST(Solver, SparseNormalCholeskyNoSparseLibrary) {
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Solver::Options options;
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options.sparse_linear_algebra_library_type = NO_SPARSE;
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options.linear_solver_type = SPARSE_NORMAL_CHOLESKY;
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string message;
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EXPECT_FALSE(options.IsValid(&message));
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}
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TEST(Solver, SparseSchurNoSparseLibrary) {
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Solver::Options options;
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options.sparse_linear_algebra_library_type = NO_SPARSE;
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options.linear_solver_type = SPARSE_SCHUR;
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string message;
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EXPECT_FALSE(options.IsValid(&message));
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}
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TEST(Solver, IterativeSchurWithClusterJacobiPerconditionerNoSparseLibrary) {
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Solver::Options options;
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options.sparse_linear_algebra_library_type = NO_SPARSE;
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options.linear_solver_type = ITERATIVE_SCHUR;
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// Requires SuiteSparse.
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options.preconditioner_type = CLUSTER_JACOBI;
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string message;
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EXPECT_FALSE(options.IsValid(&message));
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}
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TEST(Solver, IterativeSchurWithClusterTridiagonalPerconditionerNoSparseLibrary) {
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Solver::Options options;
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options.sparse_linear_algebra_library_type = NO_SPARSE;
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options.linear_solver_type = ITERATIVE_SCHUR;
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// Requires SuiteSparse.
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options.preconditioner_type = CLUSTER_TRIDIAGONAL;
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string message;
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EXPECT_FALSE(options.IsValid(&message));
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}
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TEST(Solver, IterativeLinearSolverForDogleg) {
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Solver::Options options;
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options.trust_region_strategy_type = DOGLEG;
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string message;
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options.linear_solver_type = ITERATIVE_SCHUR;
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EXPECT_FALSE(options.IsValid(&message));
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options.linear_solver_type = CGNR;
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EXPECT_FALSE(options.IsValid(&message));
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}
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TEST(Solver, LinearSolverTypeNormalOperation) {
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Solver::Options options;
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options.linear_solver_type = DENSE_QR;
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string message;
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EXPECT_TRUE(options.IsValid(&message));
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options.linear_solver_type = DENSE_NORMAL_CHOLESKY;
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EXPECT_TRUE(options.IsValid(&message));
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options.linear_solver_type = DENSE_SCHUR;
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EXPECT_TRUE(options.IsValid(&message));
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options.linear_solver_type = SPARSE_SCHUR;
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#if defined(CERES_NO_SUITESPARSE) && \
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defined(CERES_NO_CXSPARSE) && \
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!defined(CERES_USE_EIGEN_SPARSE)
|
||
|
|
EXPECT_FALSE(options.IsValid(&message));
|
||
|
|
#else
|
||
|
|
EXPECT_TRUE(options.IsValid(&message));
|
||
|
|
#endif
|
||
|
|
|
||
|
|
options.linear_solver_type = ITERATIVE_SCHUR;
|
||
|
|
EXPECT_TRUE(options.IsValid(&message));
|
||
|
|
}
|
||
|
|
|
||
|
|
template<int kNumResiduals, int N1 = 0, int N2 = 0, int N3 = 0>
|
||
|
|
class DummyCostFunction : public SizedCostFunction<kNumResiduals, N1, N2, N3> {
|
||
|
|
public:
|
||
|
|
bool Evaluate(double const* const* parameters,
|
||
|
|
double* residuals,
|
||
|
|
double** jacobians) const {
|
||
|
|
for (int i = 0; i < kNumResiduals; ++i) {
|
||
|
|
residuals[i] = kNumResiduals * kNumResiduals + i;
|
||
|
|
}
|
||
|
|
|
||
|
|
return true;
|
||
|
|
}
|
||
|
|
};
|
||
|
|
|
||
|
|
TEST(Solver, FixedCostForConstantProblem) {
|
||
|
|
double x = 1.0;
|
||
|
|
Problem problem;
|
||
|
|
problem.AddResidualBlock(new DummyCostFunction<2, 1>(), NULL, &x);
|
||
|
|
problem.SetParameterBlockConstant(&x);
|
||
|
|
const double expected_cost = 41.0 / 2.0; // 1/2 * ((4 + 0)^2 + (4 + 1)^2)
|
||
|
|
Solver::Options options;
|
||
|
|
Solver::Summary summary;
|
||
|
|
Solve(options, &problem, &summary);
|
||
|
|
EXPECT_TRUE(summary.IsSolutionUsable());
|
||
|
|
EXPECT_EQ(summary.fixed_cost, expected_cost);
|
||
|
|
EXPECT_EQ(summary.initial_cost, expected_cost);
|
||
|
|
EXPECT_EQ(summary.final_cost, expected_cost);
|
||
|
|
EXPECT_EQ(summary.iterations.size(), 0);
|
||
|
|
}
|
||
|
|
|
||
|
|
} // namespace internal
|
||
|
|
} // namespace ceres
|