mirror of
https://github.com/ceres-solver/ceres-solver.git
synced 2026-08-29 08:34:37 +08:00
Sergiu Deitsch
91773746be
If arguments are passed to a cost function that can be used to construct the functor, the latter will be instantiated by the cost function using std::make_unique to ensure exception safety. This not only avoids static analysis warnings caused by calling new but also spelling the cost functor type name multiple times. Also expand deduction guides for instantiating Dynamic(Auto|Numeric)DiffCostFunction from std::unique_ptr enabled constructor overloads. Finally, make CostFunction default move constructible and assignable but only through derived classes. This in turn allows derived classes to be movable without relying on custom implementations of corresponding operators. Change-Id: Idee8b9871d862bc9f9f8b5a8d0bedc52863e93c0
200 lines
6.5 KiB
C++
200 lines
6.5 KiB
C++
// Ceres Solver - A fast non-linear least squares minimizer
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// Copyright 2023 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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//
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// This example is a variant of curve_fitting.cc where we use an
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// IterationCallback to implement custom logging which prints out the values of
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// the parameter blocks as they evolve over the course of the optimization. This
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// also requires the use of Solver::Options::update_state_every_iteration.
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#include <iostream>
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#include "ceres/ceres.h"
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#include "glog/logging.h"
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// Data generated using the following octave code.
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// randn('seed', 23497);
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// m = 0.3;
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// c = 0.1;
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// x=[0:0.075:5];
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// y = exp(m * x + c);
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// noise = randn(size(x)) * 0.2;
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// y_observed = y + noise;
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// data = [x', y_observed'];
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const int kNumObservations = 67;
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// clang-format off
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const double data[] = {
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0.000000e+00, 1.133898e+00,
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7.500000e-02, 1.334902e+00,
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1.500000e-01, 1.213546e+00,
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2.250000e-01, 1.252016e+00,
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3.000000e-01, 1.392265e+00,
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3.750000e-01, 1.314458e+00,
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4.500000e-01, 1.472541e+00,
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5.250000e-01, 1.536218e+00,
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6.000000e-01, 1.355679e+00,
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6.750000e-01, 1.463566e+00,
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7.500000e-01, 1.490201e+00,
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8.250000e-01, 1.658699e+00,
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9.000000e-01, 1.067574e+00,
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9.750000e-01, 1.464629e+00,
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1.050000e+00, 1.402653e+00,
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1.125000e+00, 1.713141e+00,
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1.200000e+00, 1.527021e+00,
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1.275000e+00, 1.702632e+00,
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1.350000e+00, 1.423899e+00,
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1.425000e+00, 1.543078e+00,
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1.500000e+00, 1.664015e+00,
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1.575000e+00, 1.732484e+00,
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1.650000e+00, 1.543296e+00,
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1.725000e+00, 1.959523e+00,
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1.800000e+00, 1.685132e+00,
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1.875000e+00, 1.951791e+00,
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1.950000e+00, 2.095346e+00,
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2.025000e+00, 2.361460e+00,
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2.100000e+00, 2.169119e+00,
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2.175000e+00, 2.061745e+00,
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2.250000e+00, 2.178641e+00,
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2.325000e+00, 2.104346e+00,
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2.400000e+00, 2.584470e+00,
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2.475000e+00, 1.914158e+00,
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2.550000e+00, 2.368375e+00,
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2.625000e+00, 2.686125e+00,
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2.700000e+00, 2.712395e+00,
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2.775000e+00, 2.499511e+00,
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2.850000e+00, 2.558897e+00,
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2.925000e+00, 2.309154e+00,
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3.000000e+00, 2.869503e+00,
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3.075000e+00, 3.116645e+00,
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3.150000e+00, 3.094907e+00,
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3.225000e+00, 2.471759e+00,
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3.300000e+00, 3.017131e+00,
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3.375000e+00, 3.232381e+00,
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3.450000e+00, 2.944596e+00,
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3.525000e+00, 3.385343e+00,
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3.600000e+00, 3.199826e+00,
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3.675000e+00, 3.423039e+00,
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3.750000e+00, 3.621552e+00,
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3.825000e+00, 3.559255e+00,
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3.900000e+00, 3.530713e+00,
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3.975000e+00, 3.561766e+00,
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4.050000e+00, 3.544574e+00,
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4.125000e+00, 3.867945e+00,
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4.200000e+00, 4.049776e+00,
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4.275000e+00, 3.885601e+00,
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4.350000e+00, 4.110505e+00,
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4.425000e+00, 4.345320e+00,
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4.500000e+00, 4.161241e+00,
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4.575000e+00, 4.363407e+00,
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4.650000e+00, 4.161576e+00,
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4.725000e+00, 4.619728e+00,
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4.800000e+00, 4.737410e+00,
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4.875000e+00, 4.727863e+00,
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4.950000e+00, 4.669206e+00,
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};
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// clang-format on
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struct ExponentialResidual {
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ExponentialResidual(double x, double y) : x(x), y(y) {}
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template <typename T>
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bool operator()(const T* const m, const T* const c, T* residual) const {
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residual[0] = y - exp(m[0] * x + c[0]);
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return true;
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}
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private:
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const double x;
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const double y;
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};
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// MyIterationCallback prints the iteration number, the cost and the value of
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// the parameter blocks every iteration.
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class MyIterationCallback : public ceres::IterationCallback {
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public:
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MyIterationCallback(const double* m, const double* c) : m_(m), c_(c) {}
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~MyIterationCallback() override = default;
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ceres::CallbackReturnType operator()(
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const ceres::IterationSummary& summary) final {
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std::cout << "Iteration: " << summary.iteration << " cost: " << summary.cost
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<< " m: " << *m_ << " c: " << *c_ << std::endl;
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return ceres::SOLVER_CONTINUE;
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}
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private:
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const double* m_ = nullptr;
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const double* c_ = nullptr;
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};
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int main(int argc, char** argv) {
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google::InitGoogleLogging(argv[0]);
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const double initial_m = 0.0;
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const double initial_c = 0.0;
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double m = initial_m;
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double c = initial_c;
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ceres::Problem problem;
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for (int i = 0; i < kNumObservations; ++i) {
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problem.AddResidualBlock(
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new ceres::AutoDiffCostFunction<ExponentialResidual, 1, 1, 1>(
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data[2 * i], data[2 * i + 1]),
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nullptr,
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&m,
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&c);
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}
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ceres::Solver::Options options;
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options.max_num_iterations = 25;
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options.linear_solver_type = ceres::DENSE_QR;
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// Turn off the default logging from Ceres so that it does not interfere with
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// MyIterationCallback.
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options.minimizer_progress_to_stdout = false;
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MyIterationCallback callback(&m, &c);
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options.callbacks.push_back(&callback);
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// Tell Ceres to update the value of the parameter blocks on each each
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// iteration (successful or not) so that MyIterationCallback will be able to
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// see them when called.
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options.update_state_every_iteration = true;
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ceres::Solver::Summary summary;
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ceres::Solve(options, &problem, &summary);
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std::cout << summary.BriefReport() << "\n";
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std::cout << "Initial m: " << initial_m << " c: " << initial_c << "\n";
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std::cout << "Final m: " << m << " c: " << c << "\n";
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return 0;
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}
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