159 lines
5.4 KiB
C++
159 lines
5.4 KiB
C++
// 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/internal/eigen.h"
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#include "ceres/internal/scoped_ptr.h"
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#include "ceres/levenberg_marquardt_strategy.h"
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#include "ceres/linear_solver.h"
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#include "ceres/trust_region_strategy.h"
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#include "glog/logging.h"
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#include "gmock/gmock.h"
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#include "gmock/mock-log.h"
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#include "gtest/gtest.h"
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using testing::AllOf;
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using testing::AnyNumber;
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using testing::HasSubstr;
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using testing::ScopedMockLog;
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using testing::_;
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namespace ceres {
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namespace internal {
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const double kTolerance = 1e-16;
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// Linear solver that takes as input a vector and checks that the
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// caller passes the same vector as LinearSolver::PerSolveOptions.D.
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class RegularizationCheckingLinearSolver : public DenseSparseMatrixSolver {
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public:
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RegularizationCheckingLinearSolver(const int num_cols, const double* diagonal)
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: num_cols_(num_cols),
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diagonal_(diagonal) {
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}
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virtual ~RegularizationCheckingLinearSolver() {}
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private:
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virtual LinearSolver::Summary SolveImpl(
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DenseSparseMatrix* A,
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const double* b,
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const LinearSolver::PerSolveOptions& per_solve_options,
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double* x) {
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CHECK_NOTNULL(per_solve_options.D);
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for (int i = 0; i < num_cols_; ++i) {
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EXPECT_NEAR(per_solve_options.D[i], diagonal_[i], kTolerance)
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<< i << " " << per_solve_options.D[i] << " " << diagonal_[i];
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}
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return LinearSolver::Summary();
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}
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const int num_cols_;
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const double* diagonal_;
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};
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TEST(LevenbergMarquardtStrategy, AcceptRejectStepRadiusScaling) {
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TrustRegionStrategy::Options options;
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options.initial_radius = 2.0;
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options.max_radius = 20.0;
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options.min_lm_diagonal = 1e-8;
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options.max_lm_diagonal = 1e8;
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// We need a non-null pointer here, so anything should do.
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scoped_ptr<LinearSolver> linear_solver(
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new RegularizationCheckingLinearSolver(0, NULL));
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options.linear_solver = linear_solver.get();
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LevenbergMarquardtStrategy lms(options);
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EXPECT_EQ(lms.Radius(), options.initial_radius);
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lms.StepRejected(0.0);
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EXPECT_EQ(lms.Radius(), 1.0);
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lms.StepRejected(-1.0);
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EXPECT_EQ(lms.Radius(), 0.25);
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lms.StepAccepted(1.0);
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EXPECT_EQ(lms.Radius(), 0.25 * 3.0);
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lms.StepAccepted(1.0);
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EXPECT_EQ(lms.Radius(), 0.25 * 3.0 * 3.0);
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lms.StepAccepted(0.25);
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EXPECT_EQ(lms.Radius(), 0.25 * 3.0 * 3.0 / 1.125);
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lms.StepAccepted(1.0);
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EXPECT_EQ(lms.Radius(), 0.25 * 3.0 * 3.0 / 1.125 * 3.0);
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lms.StepAccepted(1.0);
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EXPECT_EQ(lms.Radius(), 0.25 * 3.0 * 3.0 / 1.125 * 3.0 * 3.0);
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lms.StepAccepted(1.0);
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EXPECT_EQ(lms.Radius(), options.max_radius);
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}
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TEST(LevenbergMarquardtStrategy, CorrectDiagonalToLinearSolver) {
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Matrix jacobian(2, 3);
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jacobian.setZero();
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jacobian(0, 0) = 0.0;
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jacobian(0, 1) = 1.0;
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jacobian(1, 1) = 1.0;
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jacobian(0, 2) = 100.0;
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double residual = 1.0;
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double x[3];
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DenseSparseMatrix dsm(jacobian);
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TrustRegionStrategy::Options options;
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options.initial_radius = 2.0;
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options.max_radius = 20.0;
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options.min_lm_diagonal = 1e-2;
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options.max_lm_diagonal = 1e2;
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double diagonal[3];
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diagonal[0] = options.min_lm_diagonal;
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diagonal[1] = 2.0;
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diagonal[2] = options.max_lm_diagonal;
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for (int i = 0; i < 3; ++i) {
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diagonal[i] = sqrt(diagonal[i] / options.initial_radius);
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}
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RegularizationCheckingLinearSolver linear_solver(3, diagonal);
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options.linear_solver = &linear_solver;
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LevenbergMarquardtStrategy lms(options);
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TrustRegionStrategy::PerSolveOptions pso;
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{
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ScopedMockLog log;
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EXPECT_CALL(log, Log(_, _, _)).Times(AnyNumber());
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EXPECT_CALL(log, Log(WARNING, _,
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HasSubstr("Failed to compute a step")));
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TrustRegionStrategy::Summary summary =
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lms.ComputeStep(pso, &dsm, &residual, x);
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EXPECT_EQ(summary.termination_type, LINEAR_SOLVER_FAILURE);
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}
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}
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} // namespace internal
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} // namespace ceres
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