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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2017 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/tiny_solver_autodiff_function.h"
#include <algorithm>
#include <cmath>
#include <limits>
#include "ceres/tiny_solver.h"
#include "ceres/tiny_solver_test_util.h"
#include "gtest/gtest.h"
namespace ceres {
struct AutoDiffTestFunctor {
template <typename T>
bool operator()(const T* const parameters, T* residuals) const {
// Shift the parameters so the solution is not at the origin, to prevent
// accidentally showing "PASS".
const T& a = parameters[0] - T(1.0);
const T& b = parameters[1] - T(2.0);
const T& c = parameters[2] - T(3.0);
residuals[0] = 2. * a + 0. * b + 1. * c;
residuals[1] = 0. * a + 4. * b + 6. * c;
return true;
}
};
// Leave a factor of 10 slop since these tests tend to mysteriously break on
// other compilers or architectures if the tolerance is too tight.
static double const kTolerance = std::numeric_limits<double>::epsilon() * 10;
TEST(TinySolverAutoDiffFunction, SimpleFunction) {
using AutoDiffTestFunction =
TinySolverAutoDiffFunction<AutoDiffTestFunctor, 2, 3>;
AutoDiffTestFunctor autodiff_test_functor;
AutoDiffTestFunction f(autodiff_test_functor);
Eigen::Vector3d x(2.0, 1.0, 4.0);
Eigen::Vector2d residuals;
// Check the case with cost-only evaluation.
residuals.setConstant(555); // Arbitrary.
EXPECT_TRUE(f(&x(0), &residuals(0), nullptr));
EXPECT_NEAR(3.0, residuals(0), kTolerance);
EXPECT_NEAR(2.0, residuals(1), kTolerance);
// Check the case with cost and Jacobian evaluation.
Eigen::Matrix<double, 2, 3> jacobian;
residuals.setConstant(555); // Arbitrary.
jacobian.setConstant(555);
EXPECT_TRUE(f(&x(0), &residuals(0), &jacobian(0, 0)));
// Verify cost.
EXPECT_NEAR(3.0, residuals(0), kTolerance);
EXPECT_NEAR(2.0, residuals(1), kTolerance);
// Verify Jacobian Row 1.
EXPECT_NEAR(2.0, jacobian(0, 0), kTolerance);
EXPECT_NEAR(0.0, jacobian(0, 1), kTolerance);
EXPECT_NEAR(1.0, jacobian(0, 2), kTolerance);
// Verify Jacobian row 2.
EXPECT_NEAR(0.0, jacobian(1, 0), kTolerance);
EXPECT_NEAR(4.0, jacobian(1, 1), kTolerance);
EXPECT_NEAR(6.0, jacobian(1, 2), kTolerance);
}
class DynamicResidualsFunctor {
public:
using Scalar = double;
enum {
NUM_RESIDUALS = Eigen::Dynamic,
NUM_PARAMETERS = 3,
};
int NumResiduals() const { return 2; }
template <typename T>
bool operator()(const T* parameters, T* residuals) const {
// Jacobian is not evaluated by cost function, but by autodiff.
T* jacobian = nullptr;
return EvaluateResidualsAndJacobians(parameters, residuals, jacobian);
}
};
template <typename Function, typename Vector>
void TestHelper(const Function& f, const Vector& x0) {
Vector x = x0;
Eigen::Vector2d residuals;
f(x.data(), residuals.data(), nullptr);
EXPECT_GT(residuals.squaredNorm() / 2.0, 1e-10);
TinySolver<Function> solver;
solver.Solve(f, &x);
EXPECT_NEAR(0.0, solver.summary.final_cost, 1e-10);
}
// A test case for when the number of residuals is
// dynamically sized and we use autodiff
TEST(TinySolverAutoDiffFunction, ResidualsDynamicAutoDiff) {
Eigen::Vector3d x0(0.76026643, -30.01799744, 0.55192142);
DynamicResidualsFunctor f;
using AutoDiffCostFunctor = ceres::
TinySolverAutoDiffFunction<DynamicResidualsFunctor, Eigen::Dynamic, 3>;
AutoDiffCostFunctor f_autodiff(f);
Eigen::Vector2d residuals;
f_autodiff(x0.data(), residuals.data(), nullptr);
EXPECT_GT(residuals.squaredNorm() / 2.0, 1e-10);
TinySolver<AutoDiffCostFunctor> solver;
solver.Solve(f_autodiff, &x0);
EXPECT_NEAR(0.0, solver.summary.final_cost, 1e-10);
}
} // namespace ceres
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