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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2019 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: sameeragarwal@google.com (Sameer Agarwal)
// dgossow@google.com (David Gossow)
#ifndef CERES_PUBLIC_DYNAMIC_COST_FUNCTION_TO_FUNCTOR_H_
#define CERES_PUBLIC_DYNAMIC_COST_FUNCTION_TO_FUNCTOR_H_
#include <memory>
#include <numeric>
#include <vector>
#include "ceres/dynamic_cost_function.h"
#include "ceres/internal/fixed_array.h"
#include "ceres/internal/port.h"
namespace ceres {
// DynamicCostFunctionToFunctor allows users to use CostFunction
// objects in templated functors which are to be used for automatic
// differentiation. It works similar to CostFunctionToFunctor, with the
// difference that it allows you to wrap a cost function with dynamic numbers
// of parameters and residuals.
//
// For example, let us assume that
//
// class IntrinsicProjection : public CostFunction {
// public:
// IntrinsicProjection(const double* observation);
// bool Evaluate(double const* const* parameters,
// double* residuals,
// double** jacobians) const override;
// };
//
// is a cost function that implements the projection of a point in its
// local coordinate system onto its image plane and subtracts it from
// the observed point projection. It can compute its residual and
// either via analytic or numerical differentiation can compute its
// jacobians. The intrinsics are passed in as parameters[0] and the point as
// parameters[1].
//
// Now we would like to compose the action of this CostFunction with
// the action of camera extrinsics, i.e., rotation and
// translation. Say we have a templated function
//
// template<typename T>
// void RotateAndTranslatePoint(double const* const* parameters,
// double* residuals);
//
// Then we can now do the following,
//
// struct CameraProjection {
// CameraProjection(const double* observation)
// : intrinsic_projection_.(new IntrinsicProjection(observation)) {
// }
// template <typename T>
// bool operator()(T const* const* parameters,
// T* residual) const {
// const T* rotation = parameters[0];
// const T* translation = parameters[1];
// const T* intrinsics = parameters[2];
// const T* point = parameters[3];
// T transformed_point[3];
// RotateAndTranslatePoint(rotation, translation, point, transformed_point);
//
// // Note that we call intrinsic_projection_, just like it was
// // any other templated functor.
// const T* projection_parameters[2];
// projection_parameters[0] = intrinsics;
// projection_parameters[1] = transformed_point;
// return intrinsic_projection_(projection_parameters, residual);
// }
//
// private:
// DynamicCostFunctionToFunctor intrinsic_projection_;
// };
class DynamicCostFunctionToFunctor {
public:
// Takes ownership of cost_function.
explicit DynamicCostFunctionToFunctor(CostFunction* cost_function)
: cost_function_(cost_function) {
CHECK(cost_function != nullptr);
}
bool operator()(double const* const* parameters, double* residuals) const {
return cost_function_->Evaluate(parameters, residuals, NULL);
}
template <typename JetT>
bool operator()(JetT const* const* inputs, JetT* output) const {
const std::vector<int32_t>& parameter_block_sizes =
cost_function_->parameter_block_sizes();
const int num_parameter_blocks =
static_cast<int>(parameter_block_sizes.size());
const int num_residuals = cost_function_->num_residuals();
const int num_parameters = std::accumulate(
parameter_block_sizes.begin(), parameter_block_sizes.end(), 0);
internal::FixedArray<double> parameters(num_parameters);
internal::FixedArray<double*> parameter_blocks(num_parameter_blocks);
internal::FixedArray<double> jacobians(num_residuals * num_parameters);
internal::FixedArray<double*> jacobian_blocks(num_parameter_blocks);
internal::FixedArray<double> residuals(num_residuals);
// Build a set of arrays to get the residuals and jacobians from
// the CostFunction wrapped by this functor.
double* parameter_ptr = parameters.data();
double* jacobian_ptr = jacobians.data();
for (int i = 0; i < num_parameter_blocks; ++i) {
parameter_blocks[i] = parameter_ptr;
jacobian_blocks[i] = jacobian_ptr;
for (int j = 0; j < parameter_block_sizes[i]; ++j) {
*parameter_ptr++ = inputs[i][j].a;
}
jacobian_ptr += num_residuals * parameter_block_sizes[i];
}
if (!cost_function_->Evaluate(parameter_blocks.data(),
residuals.data(),
jacobian_blocks.data())) {
return false;
}
// Now that we have the incoming Jets, which are carrying the
// partial derivatives of each of the inputs w.r.t to some other
// underlying parameters. The derivative of the outputs of the
// cost function w.r.t to the same underlying parameters can now
// be computed by applying the chain rule.
//
// d output[i] d output[i] d input[j]
// -------------- = sum_j ----------- * ------------
// d parameter[k] d input[j] d parameter[k]
//
// d input[j]
// -------------- = inputs[j], so
// d parameter[k]
//
// outputJet[i] = sum_k jacobian[i][k] * inputJet[k]
//
// The following loop, iterates over the residuals, computing one
// output jet at a time.
for (int i = 0; i < num_residuals; ++i) {
output[i].a = residuals[i];
output[i].v.setZero();
for (int j = 0; j < num_parameter_blocks; ++j) {
const int32_t block_size = parameter_block_sizes[j];
for (int k = 0; k < parameter_block_sizes[j]; ++k) {
output[i].v +=
jacobian_blocks[j][i * block_size + k] * inputs[j][k].v;
}
}
}
return true;
}
private:
std::unique_ptr<CostFunction> cost_function_;
};
} // namespace ceres
#endif // CERES_PUBLIC_DYNAMIC_COST_FUNCTION_TO_FUNCTOR_H_
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