blob: f2a377b471a0554291f15252a759d830a1ef2cdb [file] [log] [blame]
// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2024 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: keir@google.com (Keir Mierle)
// sameeragarwal@google.com (Sameer Agarwal)
//
// Create CostFunctions as needed by the least squares framework with jacobians
// computed via numeric (a.k.a. finite) differentiation. For more details see
// http://en.wikipedia.org/wiki/Numerical_differentiation.
//
// To get an numerically differentiated cost function, you must define
// a class with a operator() (a functor) that computes the residuals.
//
// The function must write the computed value in the last argument
// (the only non-const one) and return true to indicate success.
// Please see cost_function.h for details on how the return value
// maybe used to impose simple constraints on the parameter block.
//
// For example, consider a scalar error e = k - x'y, where both x and y are
// two-dimensional column vector parameters, the prime sign indicates
// transposition, and k is a constant. The form of this error, which is the
// difference between a constant and an expression, is a common pattern in least
// squares problems. For example, the value x'y might be the model expectation
// for a series of measurements, where there is an instance of the cost function
// for each measurement k.
//
// The actual cost added to the total problem is e^2, or (k - x'k)^2; however,
// the squaring is implicitly done by the optimization framework.
//
// To write an numerically-differentiable cost function for the above model,
// first define the object
//
// class MyScalarCostFunctor {
// explicit MyScalarCostFunctor(double k): k_(k) {}
//
// bool operator()(const double* const x,
// const double* const y,
// double* residuals) const {
// residuals[0] = k_ - x[0] * y[0] - x[1] * y[1];
// return true;
// }
//
// private:
// double k_;
// };
//
// Note that in the declaration of operator() the input parameters x
// and y come first, and are passed as const pointers to arrays of
// doubles. If there were three input parameters, then the third input
// parameter would come after y. The output is always the last
// parameter, and is also a pointer to an array. In the example above,
// the residual is a scalar, so only residuals[0] is set.
//
// Then given this class definition, the numerically differentiated
// cost function with central differences used for computing the
// derivative can be constructed as follows.
//
// CostFunction* cost_function
// = new NumericDiffCostFunction<MyScalarCostFunctor, CENTRAL, 1, 2, 2>(
// new MyScalarCostFunctor(1.0)); ^ ^ ^ ^
// | | | |
// Finite Differencing Scheme -+ | | |
// Dimension of residual ------------+ | |
// Dimension of x ----------------------+ |
// Dimension of y -------------------------+
//
// In this example, there is usually an instance for each measurement of k.
//
// In the instantiation above, the template parameters following
// "MyScalarCostFunctor", "1, 2, 2", describe the functor as computing
// a 1-dimensional output from two arguments, both 2-dimensional.
//
// NumericDiffCostFunction also supports cost functions with a
// runtime-determined number of residuals. For example:
//
// clang-format off
//
// CostFunction* cost_function
// = new NumericDiffCostFunction<MyScalarCostFunctor, CENTRAL, DYNAMIC, 2, 2>(
// new CostFunctorWithDynamicNumResiduals(1.0), ^ ^ ^
// TAKE_OWNERSHIP, | | |
// runtime_number_of_residuals); <----+ | | |
// | | | |
// | | | |
// Actual number of residuals ------+ | | |
// Indicate dynamic number of residuals --------------------+ | |
// Dimension of x ------------------------------------------------+ |
// Dimension of y ---------------------------------------------------+
// clang-format on
//
//
// The central difference method is considerably more accurate at the cost of
// twice as many function evaluations than forward difference. Consider using
// central differences begin with, and only after that works, trying forward
// difference to improve performance.
//
// WARNING #1: A common beginner's error when first using
// NumericDiffCostFunction is to get the sizing wrong. In particular,
// there is a tendency to set the template parameters to (dimension of
// residual, number of parameters) instead of passing a dimension
// parameter for *every parameter*. In the example above, that would
// be <MyScalarCostFunctor, 1, 2>, which is missing the last '2'
// argument. Please be careful when setting the size parameters.
//
////////////////////////////////////////////////////////////////////////////
////////////////////////////////////////////////////////////////////////////
//
// ALTERNATE INTERFACE
//
// For a variety of reasons, including compatibility with legacy code,
// NumericDiffCostFunction can also take CostFunction objects as
// input. The following describes how.
//
// To get a numerically differentiated cost function, define a
// subclass of CostFunction such that the Evaluate() function ignores
// the jacobian parameter. The numeric differentiation wrapper will
// fill in the jacobian parameter if necessary by repeatedly calling
// the Evaluate() function with small changes to the appropriate
// parameters, and computing the slope. For performance, the numeric
// differentiation wrapper class is templated on the concrete cost
// function, even though it could be implemented only in terms of the
// virtual CostFunction interface.
//
// The numerically differentiated version of a cost function for a cost function
// can be constructed as follows:
//
// auto* cost_function
// = new NumericDiffCostFunction<MyCostFunction, CENTRAL, 1, 4, 8>();
//
// where MyCostFunction has 1 residual and 2 parameter blocks with sizes 4 and 8
// respectively. Look at the tests for a more detailed example.
//
// TODO(keir): Characterize accuracy; mention pitfalls; provide alternatives.
#ifndef CERES_PUBLIC_NUMERIC_DIFF_COST_FUNCTION_H_
#define CERES_PUBLIC_NUMERIC_DIFF_COST_FUNCTION_H_
#include <array>
#include <memory>
#include <type_traits>
#include "Eigen/Dense"
#include "ceres/cost_function.h"
#include "ceres/internal/numeric_diff.h"
#include "ceres/internal/parameter_dims.h"
#include "ceres/numeric_diff_options.h"
#include "ceres/sized_cost_function.h"
#include "ceres/types.h"
namespace ceres {
template <typename CostFunctor,
NumericDiffMethodType kMethod = CENTRAL,
int kNumResiduals = 0, // Number of residuals, or ceres::DYNAMIC
int... Ns> // Parameters dimensions for each block.
class NumericDiffCostFunction final
: public SizedCostFunction<kNumResiduals, Ns...> {
public:
explicit NumericDiffCostFunction(
CostFunctor* functor,
Ownership ownership = TAKE_OWNERSHIP,
int num_residuals = kNumResiduals,
const NumericDiffOptions& options = NumericDiffOptions())
: NumericDiffCostFunction{std::unique_ptr<CostFunctor>{functor},
ownership,
num_residuals,
options} {}
explicit NumericDiffCostFunction(
std::unique_ptr<CostFunctor> functor,
int num_residuals = kNumResiduals,
const NumericDiffOptions& options = NumericDiffOptions())
: NumericDiffCostFunction{
std::move(functor), TAKE_OWNERSHIP, num_residuals, options} {}
// Constructs the CostFunctor on the heap and takes the ownership.
// Invocable only if the number of residuals is known at compile-time.
template <class... Args,
bool kIsDynamic = kNumResiduals == DYNAMIC,
std::enable_if_t<!kIsDynamic &&
std::is_constructible_v<CostFunctor, Args&&...>>* =
nullptr>
explicit NumericDiffCostFunction(Args&&... args)
// NOTE We explicitly use direct initialization using parentheses instead
// of uniform initialization using braces to avoid narrowing conversion
// warnings.
: NumericDiffCostFunction{
std::make_unique<CostFunctor>(std::forward<Args>(args)...),
TAKE_OWNERSHIP} {}
NumericDiffCostFunction(NumericDiffCostFunction&& other) noexcept = default;
NumericDiffCostFunction& operator=(NumericDiffCostFunction&& other) noexcept =
default;
NumericDiffCostFunction(const NumericDiffCostFunction&) = delete;
NumericDiffCostFunction& operator=(const NumericDiffCostFunction&) = delete;
~NumericDiffCostFunction() override {
if (ownership_ != TAKE_OWNERSHIP) {
functor_.release();
}
}
bool Evaluate(double const* const* parameters,
double* residuals,
double** jacobians) const override {
using internal::FixedArray;
using internal::NumericDiff;
using ParameterDims =
typename SizedCostFunction<kNumResiduals, Ns...>::ParameterDims;
constexpr int kNumParameters = ParameterDims::kNumParameters;
constexpr int kNumParameterBlocks = ParameterDims::kNumParameterBlocks;
// Get the function value (residuals) at the the point to evaluate.
if (!internal::VariadicEvaluate<ParameterDims>(
*functor_, parameters, residuals)) {
return false;
}
if (jacobians == nullptr) {
return true;
}
// Create a copy of the parameters which will get mutated.
FixedArray<double> parameters_copy(kNumParameters);
std::array<double*, kNumParameterBlocks> parameters_reference_copy =
ParameterDims::GetUnpackedParameters(parameters_copy.data());
for (int block = 0; block < kNumParameterBlocks; ++block) {
memcpy(parameters_reference_copy[block],
parameters[block],
sizeof(double) * ParameterDims::GetDim(block));
}
internal::EvaluateJacobianForParameterBlocks<ParameterDims>::
template Apply<kMethod, kNumResiduals>(
functor_.get(),
residuals,
options_,
SizedCostFunction<kNumResiduals, Ns...>::num_residuals(),
parameters_reference_copy.data(),
jacobians);
return true;
}
const CostFunctor& functor() const { return *functor_; }
private:
explicit NumericDiffCostFunction(std::unique_ptr<CostFunctor> functor,
Ownership ownership,
[[maybe_unused]] int num_residuals,
const NumericDiffOptions& options)
: functor_(std::move(functor)), ownership_(ownership), options_(options) {
if constexpr (kNumResiduals == DYNAMIC) {
SizedCostFunction<kNumResiduals, Ns...>::set_num_residuals(num_residuals);
}
}
std::unique_ptr<CostFunctor> functor_;
Ownership ownership_;
NumericDiffOptions options_;
};
} // namespace ceres
#endif // CERES_PUBLIC_NUMERIC_DIFF_COST_FUNCTION_H_