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NumericDiffFunctor.
A wrapper class that takes a variadic functor evaluating a function, numerically differentiates it and makes it available as a templated functor so that it can be easily used as part of Ceres' automatic differentiation framework. The tests for NumericDiffCostFunction and NumericDiffFunctor have a lot of stuff that is common, so refactor them to reduce code. Change-Id: I83b01e58b05e575fb2530d15cbd611928298646a
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// Ceres Solver - A fast non-linear least squares minimizer
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// Copyright 2013 Google Inc. All rights reserved.
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// http://code.google.com/p/ceres-solver/
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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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// A wrapper class that takes a variadic functor evaluating a
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// function, numerically differentiates it and makes it available as a
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// templated functor so that it can be easily used as part of Ceres'
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// automatic differentiation framework.
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//
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// For example:
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//
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// For example, let us assume that
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//
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// struct IntrinsicProjection
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// IntrinsicProjection(const double* observations);
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// bool operator()(const double* calibration,
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// const double* point,
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// double* residuals);
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// };
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//
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// is a functor that implements the projection of a point in its local
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// coordinate system onto its image plane and subtracts it from the
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// observed point projection.
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//
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// Now we would like to compose the action of this functor with the
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// action of camera extrinsics, i.e., rotation and translation, which
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// is given by the following templated function
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//
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// template<typename T>
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// void RotateAndTranslatePoint(const T* rotation,
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// const T* translation,
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// const T* point,
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// T* result);
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//
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// To compose the extrinsics and intrinsics, we can construct a
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// CameraProjection functor as follows.
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//
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// struct CameraProjection {
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// typedef NumericDiffFunctor<IntrinsicProjection, CENTRAL, 2, 5, 3>
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// IntrinsicProjectionFunctor;
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//
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// CameraProjection(double* observation) {
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// intrinsic_projection_.reset(
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// new IntrinsicProjectionFunctor(observation)) {
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// }
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//
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// template <typename T>
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// bool operator(const T* rotation,
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// const T* translation,
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// const T* intrinsics,
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// const T* point,
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// T* residuals) const {
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// T transformed_point[3];
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// RotateAndTranslatePoint(rotation, translation, point, transformed_point);
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// return (*intrinsic_projection_)(intrinsics, transformed_point, residual);
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// }
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//
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// private:
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// scoped_ptr<IntrinsicProjectionFunctor> intrinsic_projection_;
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// };
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//
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// Here, we made the choice of using CENTRAL differences to compute
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// the jacobian of IntrinsicProjection.
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//
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// Now, we are ready to construct an automatically differentiated cost
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// function as
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//
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// CostFunction* cost_function =
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// new AutoDiffCostFunction<CameraProjection, 2, 3, 3, 5>(
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// new CameraProjection(observations));
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//
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// cost_function now seamlessly integrates automatic differentiation
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// of RotateAndTranslatePoint with a numerically differentiated
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// version of IntrinsicProjection.
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#include "ceres/numeric_diff_cost_function.h"
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#include "ceres/types.h"
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#include "ceres/cost_function_to_functor.h"
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namespace ceres {
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template<typename Functor,
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NumericDiffMethod kMethod = CENTRAL,
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int kNumResiduals = 0,
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int N0 = 0, int N1 = 0 , int N2 = 0, int N3 = 0, int N4 = 0,
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int N5 = 0, int N6 = 0 , int N7 = 0, int N8 = 0, int N9 = 0>
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class NumericDiffFunctor {
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public:
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// relative_step_size controls the step size used by the numeric
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// differentiation process.
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NumericDiffFunctor(double relative_step_size = 1e-6)
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: functor_(new NumericDiffCostFunction<Functor,
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kMethod,
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kNumResiduals,
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N0, N1, N2, N3, N4,
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N5, N6, N7, N8, N9>(
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new Functor, relative_step_size)) {
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}
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NumericDiffFunctor(Functor* functor, double relative_step_size = 1e-6)
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: functor_(new NumericDiffCostFunction<Functor,
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kMethod,
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kNumResiduals,
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N0, N1, N2, N3, N4,
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N5, N6, N7, N8, N9>(
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functor, relative_step_size)) {
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}
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bool operator()(const double* x0, double* residuals) const {
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functor_(x0, residuals);
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}
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bool operator()(const double* x0,
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const double* x1,
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double* residuals) const {
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return functor_(x0, x1, residuals);
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}
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bool operator()(const double* x0,
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const double* x1,
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const double* x2,
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double* residuals) const {
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return functor_(x0, x1, x2, residuals);
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}
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bool operator()(const double* x0,
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const double* x1,
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const double* x2,
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const double* x3,
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double* residuals) const {
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return functor_(x0, x1, x2, x3, residuals);
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}
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bool operator()(const double* x0,
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const double* x1,
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const double* x2,
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const double* x3,
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const double* x4,
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double* residuals) const {
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return functor_(x0, x1, x2, x3, x4, residuals);
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}
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bool operator()(const double* x0,
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const double* x1,
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const double* x2,
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const double* x3,
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const double* x4,
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const double* x5,
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double* residuals) const {
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return functor_(x0, x1, x2, x3, x4, x5, residuals);
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}
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bool operator()(const double* x0,
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const double* x1,
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const double* x2,
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const double* x3,
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const double* x4,
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const double* x5,
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const double* x6,
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double* residuals) const {
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return functor_(x0, x1, x2, x3, x4, x5, x6, residuals);
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}
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bool operator()(const double* x0,
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const double* x1,
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const double* x2,
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const double* x3,
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const double* x4,
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const double* x5,
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const double* x6,
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const double* x7,
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double* residuals) const {
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return functor_(x0, x1, x2, x3, x4, x5, x6, x7, residuals);
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}
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bool operator()(const double* x0,
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const double* x1,
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const double* x2,
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const double* x3,
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const double* x4,
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const double* x5,
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const double* x6,
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const double* x7,
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const double* x8,
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double* residuals) const {
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return functor_(x0, x1, x2, x3, x4, x5, x6, x7, x8, residuals);
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}
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bool operator()(const double* x0,
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const double* x1,
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const double* x2,
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const double* x3,
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const double* x4,
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const double* x5,
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const double* x6,
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const double* x7,
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const double* x8,
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const double* x9,
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double* residuals) const {
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return functor_(x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, residuals);
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}
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template <typename T>
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bool operator()(const T* x0, T* residuals) const {
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functor_(x0, residuals);
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}
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template <typename T>
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bool operator()(const T* x0,
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const T* x1,
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T* residuals) const {
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return functor_(x0, x1, residuals);
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}
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template <typename T>
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bool operator()(const T* x0,
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const T* x1,
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const T* x2,
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T* residuals) const {
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return functor_(x0, x1, x2, residuals);
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}
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template <typename T>
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bool operator()(const T* x0,
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const T* x1,
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const T* x2,
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const T* x3,
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T* residuals) const {
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return functor_(x0, x1, x2, x3, residuals);
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}
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template <typename T>
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bool operator()(const T* x0,
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const T* x1,
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const T* x2,
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const T* x3,
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const T* x4,
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T* residuals) const {
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return functor_(x0, x1, x2, x3, x4, residuals);
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}
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template <typename T>
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bool operator()(const T* x0,
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const T* x1,
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const T* x2,
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const T* x3,
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const T* x4,
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const T* x5,
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T* residuals) const {
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return functor_(x0, x1, x2, x3, x4, x5, residuals);
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}
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template <typename T>
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bool operator()(const T* x0,
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const T* x1,
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const T* x2,
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const T* x3,
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const T* x4,
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const T* x5,
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const T* x6,
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T* residuals) const {
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return functor_(x0, x1, x2, x3, x4, x5, x6, residuals);
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}
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template <typename T>
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bool operator()(const T* x0,
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const T* x1,
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const T* x2,
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const T* x3,
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const T* x4,
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const T* x5,
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const T* x6,
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const T* x7,
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T* residuals) const {
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return functor_(x0, x1, x2, x3, x4, x5, x6, x7, residuals);
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}
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template <typename T>
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bool operator()(const T* x0,
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const T* x1,
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const T* x2,
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const T* x3,
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const T* x4,
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const T* x5,
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const T* x6,
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const T* x7,
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const T* x8,
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T* residuals) const {
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return functor_(x0, x1, x2, x3, x4, x5, x6, x7, x8, residuals);
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}
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template <typename T>
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bool operator()(const T* x0,
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const T* x1,
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const T* x2,
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const T* x3,
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const T* x4,
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const T* x5,
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const T* x6,
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const T* x7,
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const T* x8,
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const T* x9,
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T* residuals) const {
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return functor_(x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, residuals);
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}
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private:
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CostFunctionToFunctor<kNumResiduals,
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N0, N1, N2, N3, N4,
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N5, N6, N7, N8, N9> functor_;
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};
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} // namespace ceres
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