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// Ceres Solver - A fast non-linear least squares minimizer
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// http://ceres-solver.org/
//
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//
// Author: sameeragarwal@google.com (Sameer Agarwal)
//
// Create CostFunctions as needed by the least squares framework, with
// Jacobians computed via automatic differentiation. For more
// information on automatic differentiation, see the wikipedia article
// at http://en.wikipedia.org/wiki/Automatic_differentiation
//
// To get an auto differentiated cost function, you must define a class with a
// templated operator() (a functor) that computes the cost function in terms of
// the template parameter T. The autodiff framework substitutes appropriate
// "jet" objects for T in order to compute the derivative when necessary, but
// this is hidden, and you should write the function as if T were a scalar type
// (e.g. a double-precision floating point number).
//
// 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'y)^2; however,
// the squaring is implicitly done by the optimization framework.
//
// To write an auto-differentiable cost function for the above model, first
// define the object
//
// class MyScalarCostFunctor {
// MyScalarCostFunctor(double k): k_(k) {}
//
// template <typename T>
// bool operator()(const T* const x , const T* const y, T* e) const {
// e[0] = T(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 T. 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, e is a scalar, so only e[0] is set.
//
// Then given this class definition, the auto differentiated cost function for
// it can be constructed as follows.
//
// auto* cost_function
// = new AutoDiffCostFunction<MyScalarCostFunctor, 1, 2, 2>(1.0);
// ^ ^ ^
// | | |
// 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.
//
// AutoDiffCostFunction also supports cost functions with a
// runtime-determined number of residuals. For example:
//
// auto functor = std::make_unique<CostFunctorWithDynamicNumResiduals>(1.0);
// auto* cost_function
// = new AutoDiffCostFunction<CostFunctorWithDynamicNumResiduals,
// DYNAMIC, 2, 2>(
// std::move(functor), ^ ^ ^
// runtime_number_of_residuals); <----+ | | |
// | | | |
// | | | |
// Actual number of residuals ------+ | | |
// Indicate dynamic number of residuals --------+ | |
// Dimension of x ------------------------------------+ |
// Dimension of y ---------------------------------------+
//
// WARNING #1: Since the functor will get instantiated with different types for
// T, you must convert from other numeric types to T before mixing
// computations with other variables of type T. In the example above, this is
// seen where instead of using k_ directly, k_ is wrapped with T(k_).
//
// WARNING #2: A common beginner's error when first using autodiff cost
// functions 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.
#ifndef CERES_PUBLIC_AUTODIFF_COST_FUNCTION_H_
#define CERES_PUBLIC_AUTODIFF_COST_FUNCTION_H_
#include <memory>
#include <type_traits>
#include "ceres/internal/autodiff.h"
#include "ceres/sized_cost_function.h"
#include "ceres/types.h"
namespace ceres {
// A cost function which computes the derivative of the cost with respect to
// the parameters (a.k.a. the jacobian) using an auto differentiation framework.
// The first template argument is the functor object, described in the header
// comment. The second argument is the dimension of the residual (or
// ceres::DYNAMIC to indicate it will be set at runtime), and subsequent
// arguments describe the size of the Nth parameter, one per parameter.
//
// The constructors take ownership of the cost functor.
//
// If the number of residuals (argument kNumResiduals below) is
// ceres::DYNAMIC, then the two-argument constructor must be used. The
// second constructor takes a number of residuals (in addition to the
// templated number of residuals). This allows for varying the number
// of residuals for a single autodiff cost function at runtime.
template <typename CostFunctor,
int kNumResiduals, // Number of residuals, or ceres::DYNAMIC.
int... Ns> // Number of parameters in each parameter block.
class AutoDiffCostFunction final
: public SizedCostFunction<kNumResiduals, Ns...> {
public:
// Takes ownership of functor by default. Uses the template-provided
// value for the number of residuals ("kNumResiduals").
explicit AutoDiffCostFunction(std::unique_ptr<CostFunctor> functor)
: AutoDiffCostFunction{std::move(functor), TAKE_OWNERSHIP, FIXED_INIT} {}
// 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 AutoDiffCostFunction(Args&&... args)
// NOTE We explicitly use direct initialization using parentheses instead
// of uniform initialization using braces to avoid narrowing conversion
// warnings.
: AutoDiffCostFunction{
std::make_unique<CostFunctor>(std::forward<Args>(args)...)} {}
AutoDiffCostFunction(std::unique_ptr<CostFunctor> functor, int num_residuals)
: AutoDiffCostFunction{
std::move(functor), num_residuals, TAKE_OWNERSHIP, DYNAMIC_INIT} {}
explicit AutoDiffCostFunction(CostFunctor* functor,
Ownership ownership = TAKE_OWNERSHIP)
: AutoDiffCostFunction{
std::unique_ptr<CostFunctor>{functor}, ownership, FIXED_INIT} {}
// Takes ownership of functor by default. Ignores the template-provided
// kNumResiduals in favor of the "num_residuals" argument provided.
//
// This allows for having autodiff cost functions which return varying
// numbers of residuals at runtime.
AutoDiffCostFunction(CostFunctor* functor,
int num_residuals,
Ownership ownership = TAKE_OWNERSHIP)
: AutoDiffCostFunction{std::unique_ptr<CostFunctor>{functor},
num_residuals,
ownership,
DYNAMIC_INIT} {}
AutoDiffCostFunction(AutoDiffCostFunction&& other) noexcept = default;
AutoDiffCostFunction& operator=(AutoDiffCostFunction&& other) noexcept =
default;
AutoDiffCostFunction(const AutoDiffCostFunction& other) = delete;
AutoDiffCostFunction& operator=(const AutoDiffCostFunction& other) = delete;
~AutoDiffCostFunction() override {
// Manually release pointer if configured to not take ownership rather than
// deleting only if ownership is taken.
// This is to stay maximally compatible to old user code which may have
// forgotten to implement a virtual destructor, from when the
// AutoDiffCostFunction always took ownership.
if (ownership_ == DO_NOT_TAKE_OWNERSHIP) {
functor_.release();
}
}
// Implementation details follow; clients of the autodiff cost function should
// not have to examine below here.
//
// To handle variadic cost functions, some template magic is needed. It's
// mostly hidden inside autodiff.h.
bool Evaluate(double const* const* parameters,
double* residuals,
double** jacobians) const override {
using ParameterDims =
typename SizedCostFunction<kNumResiduals, Ns...>::ParameterDims;
if (jacobians == nullptr) {
return internal::VariadicEvaluate<ParameterDims>(
*functor_, parameters, residuals);
}
return internal::AutoDifferentiate<kNumResiduals, ParameterDims>(
*functor_,
parameters,
SizedCostFunction<kNumResiduals, Ns...>::num_residuals(),
residuals,
jacobians);
};
const CostFunctor& functor() const { return *functor_; }
private:
// Tags used to differentiate between dynamic and fixed size constructor
// delegate invocations.
static constexpr std::integral_constant<int, DYNAMIC> DYNAMIC_INIT{};
static constexpr std::integral_constant<int, kNumResiduals> FIXED_INIT{};
template <class InitTag>
AutoDiffCostFunction(std::unique_ptr<CostFunctor> functor,
int num_residuals,
Ownership ownership,
InitTag /*unused*/)
: functor_{std::move(functor)}, ownership_{ownership} {
static_assert(kNumResiduals == FIXED_INIT,
"Can't run the fixed-size constructor if the number of "
"residuals is set to ceres::DYNAMIC.");
if constexpr (InitTag::value == DYNAMIC_INIT) {
SizedCostFunction<kNumResiduals, Ns...>::set_num_residuals(num_residuals);
}
}
template <class InitTag>
AutoDiffCostFunction(std::unique_ptr<CostFunctor> functor,
Ownership ownership,
InitTag tag)
: AutoDiffCostFunction{
std::move(functor), kNumResiduals, ownership, tag} {}
std::unique_ptr<CostFunctor> functor_;
Ownership ownership_;
};
} // namespace ceres
#endif // CERES_PUBLIC_AUTODIFF_COST_FUNCTION_H_