include/ceres/dynamic_autodiff_cost_function.h - ceres-solver - Git at Google

 // Ceres Solver - A fast non-linear least squares minimizer
 // Copyright 2015 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)
 //         mierle@gmail.com (Keir Mierle)

 #ifndef CERES_PUBLIC_DYNAMIC_AUTODIFF_COST_FUNCTION_H_
 #define CERES_PUBLIC_DYNAMIC_AUTODIFF_COST_FUNCTION_H_

 #include <cmath>
 #include <numeric>
 #include <vector>

 #include "ceres/dynamic_cost_function.h"
 #include "ceres/internal/scoped_ptr.h"
 #include "ceres/jet.h"
 #include "glog/logging.h"

 namespace ceres {

 // This autodiff implementation differs from the one found in
 // autodiff_cost_function.h by supporting autodiff on cost functions
 // with variable numbers of parameters with variable sizes. With the
 // other implementation, all the sizes (both the number of parameter
 // blocks and the size of each block) must be fixed at compile time.
 //
 // The functor API differs slightly from the API for fixed size
 // autodiff; the expected interface for the cost functors is:
 //
 //   struct MyCostFunctor {
 //     template<typename T>
 //     bool operator()(T const* const* parameters, T* residuals) const {
 //       // Use parameters[i] to access the i'th parameter block.
 //     }
 //   }
 //
 // Since the sizing of the parameters is done at runtime, you must
 // also specify the sizes after creating the dynamic autodiff cost
 // function. For example:
 //
 //   DynamicAutoDiffCostFunction<MyCostFunctor, 3> cost_function(
 //       new MyCostFunctor());
 //   cost_function.AddParameterBlock(5);
 //   cost_function.AddParameterBlock(10);
 //   cost_function.SetNumResiduals(21);
 //
 // Under the hood, the implementation evaluates the cost function
 // multiple times, computing a small set of the derivatives (four by
 // default, controlled by the Stride template parameter) with each
 // pass. There is a tradeoff with the size of the passes; you may want
 // to experiment with the stride.
 template <typename CostFunctor, int Stride = 4>
 class DynamicAutoDiffCostFunction : public DynamicCostFunction {
  public:
   explicit DynamicAutoDiffCostFunction(CostFunctor* functor)
     : functor_(functor) {}

   virtual ~DynamicAutoDiffCostFunction() {}

   virtual bool Evaluate(double const* const* parameters,
                         double* residuals,
                         double** jacobians) const {
     CHECK_GT(num_residuals(), 0)
         << "You must call DynamicAutoDiffCostFunction::SetNumResiduals() "
         << "before DynamicAutoDiffCostFunction::Evaluate().";

     if (jacobians == NULL) {
       return (*functor_)(parameters, residuals);
     }

     // The difficulty with Jets, as implemented in Ceres, is that they were
     // originally designed for strictly compile-sized use. At this point, there
     // is a large body of code that assumes inside a cost functor it is
     // acceptable to do e.g. T(1.5) and get an appropriately sized jet back.
     //
     // Unfortunately, it is impossible to communicate the expected size of a
     // dynamically sized jet to the static instantiations that existing code
     // depends on.
     //
     // To work around this issue, the solution here is to evaluate the
     // jacobians in a series of passes, each one computing Stripe *
     // num_residuals() derivatives. This is done with small, fixed-size jets.
     const int num_parameter_blocks = parameter_block_sizes().size();
     const int num_parameters = std::accumulate(parameter_block_sizes().begin(),
                                                parameter_block_sizes().end(),
                                                0);

     // Allocate scratch space for the strided evaluation.
     std::vector<Jet<double, Stride> > input_jets(num_parameters);
     std::vector<Jet<double, Stride> > output_jets(num_residuals());

     // Make the parameter pack that is sent to the functor (reused).
     std::vector<Jet<double, Stride>* > jet_parameters(num_parameter_blocks,
         static_cast<Jet<double, Stride>* >(NULL));
     int num_active_parameters = 0;

     // To handle constant parameters between non-constant parameter blocks, the
     // start position --- a raw parameter index --- of each contiguous block of
     // non-constant parameters is recorded in start_derivative_section.
     std::vector<int> start_derivative_section;
     bool in_derivative_section = false;
     int parameter_cursor = 0;

     // Discover the derivative sections and set the parameter values.
     for (int i = 0; i < num_parameter_blocks; ++i) {
       jet_parameters[i] = &input_jets[parameter_cursor];

       const int parameter_block_size = parameter_block_sizes()[i];
       if (jacobians[i] != NULL) {
         if (!in_derivative_section) {
           start_derivative_section.push_back(parameter_cursor);
           in_derivative_section = true;
         }

         num_active_parameters += parameter_block_size;
       } else {
         in_derivative_section = false;
       }

       for (int j = 0; j < parameter_block_size; ++j, parameter_cursor++) {
         input_jets[parameter_cursor].a = parameters[i][j];
       }
     }

     // When `num_active_parameters % Stride != 0` then it can be the case
     // that `active_parameter_count < Stride` while parameter_cursor is less
     // than the total number of parameters and with no remaining non-constant
     // parameter blocks. Pushing parameter_cursor (the total number of
     // parameters) as a final entry to start_derivative_section is required
     // because if a constant parameter block is encountered after the
     // last non-constant block then current_derivative_section is incremented
     // and would otherwise index an invalid position in
     // start_derivative_section. Setting the final element to the total number
     // of parameters means that this can only happen at most once in the loop
     // below.
     start_derivative_section.push_back(parameter_cursor);

     // Evaluate all of the strides. Each stride is a chunk of the derivative to
     // evaluate, typically some size proportional to the size of the SIMD
     // registers of the CPU.
     int num_strides = static_cast<int>(ceil(num_active_parameters /
                                             static_cast<float>(Stride)));

     int current_derivative_section = 0;
     int current_derivative_section_cursor = 0;

     for (int pass = 0; pass < num_strides; ++pass) {
       // Set most of the jet components to zero, except for
       // non-constant #Stride parameters.
       const int initial_derivative_section = current_derivative_section;
       const int initial_derivative_section_cursor =
         current_derivative_section_cursor;

       int active_parameter_count = 0;
       parameter_cursor = 0;

       for (int i = 0; i < num_parameter_blocks; ++i) {
         for (int j = 0; j < parameter_block_sizes()[i];
              ++j, parameter_cursor++) {
           input_jets[parameter_cursor].v.setZero();
           if (active_parameter_count < Stride &&
               parameter_cursor >= (
                 start_derivative_section[current_derivative_section] +
                 current_derivative_section_cursor)) {
             if (jacobians[i] != NULL) {
               input_jets[parameter_cursor].v[active_parameter_count] = 1.0;
               ++active_parameter_count;
               ++current_derivative_section_cursor;
             } else {
               ++current_derivative_section;
               current_derivative_section_cursor = 0;
             }
           }
         }
       }

       if (!(*functor_)(&jet_parameters[0], &output_jets[0])) {
         return false;
       }

       // Copy the pieces of the jacobians into their final place.
       active_parameter_count = 0;

       current_derivative_section = initial_derivative_section;
       current_derivative_section_cursor = initial_derivative_section_cursor;

       for (int i = 0, parameter_cursor = 0; i < num_parameter_blocks; ++i) {
         for (int j = 0; j < parameter_block_sizes()[i];
              ++j, parameter_cursor++) {
           if (active_parameter_count < Stride &&
               parameter_cursor >= (
                 start_derivative_section[current_derivative_section] +
                 current_derivative_section_cursor)) {
             if (jacobians[i] != NULL) {
               for (int k = 0; k < num_residuals(); ++k) {
                 jacobians[i][k * parameter_block_sizes()[i] + j] =
                     output_jets[k].v[active_parameter_count];
               }
               ++active_parameter_count;
               ++current_derivative_section_cursor;
             } else {
               ++current_derivative_section;
               current_derivative_section_cursor = 0;
             }
           }
         }
       }

       // Only copy the residuals over once (even though we compute them on
       // every loop).
       if (pass == num_strides - 1) {
         for (int k = 0; k < num_residuals(); ++k) {
           residuals[k] = output_jets[k].a;
         }
       }
     }
     return true;
   }

  private:
   internal::scoped_ptr<CostFunctor> functor_;
 };

 }  // namespace ceres

 #endif  // CERES_PUBLIC_DYNAMIC_AUTODIFF_COST_FUNCTION_H_
	// Ceres Solver - A fast non-linear least squares minimizer
	// Copyright 2015 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)
	// mierle@gmail.com (Keir Mierle)

	#ifndef CERES_PUBLIC_DYNAMIC_AUTODIFF_COST_FUNCTION_H_
	#define CERES_PUBLIC_DYNAMIC_AUTODIFF_COST_FUNCTION_H_

	#include <cmath>
	#include <numeric>
	#include <vector>

	#include "ceres/dynamic_cost_function.h"
	#include "ceres/internal/scoped_ptr.h"
	#include "ceres/jet.h"
	#include "glog/logging.h"

	namespace ceres {

	// This autodiff implementation differs from the one found in
	// autodiff_cost_function.h by supporting autodiff on cost functions
	// with variable numbers of parameters with variable sizes. With the
	// other implementation, all the sizes (both the number of parameter
	// blocks and the size of each block) must be fixed at compile time.
	//
	// The functor API differs slightly from the API for fixed size
	// autodiff; the expected interface for the cost functors is:
	//
	// struct MyCostFunctor {
	// template<typename T>
	// bool operator()(T const* const* parameters, T* residuals) const {
	// // Use parameters[i] to access the i'th parameter block.
	// }
	// }
	//
	// Since the sizing of the parameters is done at runtime, you must
	// also specify the sizes after creating the dynamic autodiff cost
	// function. For example:
	//
	// DynamicAutoDiffCostFunction<MyCostFunctor, 3> cost_function(
	// new MyCostFunctor());
	// cost_function.AddParameterBlock(5);
	// cost_function.AddParameterBlock(10);
	// cost_function.SetNumResiduals(21);
	//
	// Under the hood, the implementation evaluates the cost function
	// multiple times, computing a small set of the derivatives (four by
	// default, controlled by the Stride template parameter) with each
	// pass. There is a tradeoff with the size of the passes; you may want
	// to experiment with the stride.
	template <typename CostFunctor, int Stride = 4>
	class DynamicAutoDiffCostFunction : public DynamicCostFunction {
	public:
	explicit DynamicAutoDiffCostFunction(CostFunctor* functor)
	: functor_(functor) {}

	virtual ~DynamicAutoDiffCostFunction() {}

	virtual bool Evaluate(double const* const* parameters,
	double* residuals,
	double** jacobians) const {
	CHECK_GT(num_residuals(), 0)
	<< "You must call DynamicAutoDiffCostFunction::SetNumResiduals() "
	<< "before DynamicAutoDiffCostFunction::Evaluate().";

	if (jacobians == NULL) {
	return (*functor_)(parameters, residuals);
	}

	// The difficulty with Jets, as implemented in Ceres, is that they were
	// originally designed for strictly compile-sized use. At this point, there
	// is a large body of code that assumes inside a cost functor it is
	// acceptable to do e.g. T(1.5) and get an appropriately sized jet back.
	//
	// Unfortunately, it is impossible to communicate the expected size of a
	// dynamically sized jet to the static instantiations that existing code
	// depends on.
	//
	// To work around this issue, the solution here is to evaluate the
	// jacobians in a series of passes, each one computing Stripe *
	// num_residuals() derivatives. This is done with small, fixed-size jets.
	const int num_parameter_blocks = parameter_block_sizes().size();
	const int num_parameters = std::accumulate(parameter_block_sizes().begin(),
	parameter_block_sizes().end(),
	0);

	// Allocate scratch space for the strided evaluation.
	std::vector<Jet<double, Stride> > input_jets(num_parameters);
	std::vector<Jet<double, Stride> > output_jets(num_residuals());

	// Make the parameter pack that is sent to the functor (reused).
	std::vector<Jet<double, Stride>* > jet_parameters(num_parameter_blocks,
	static_cast<Jet<double, Stride>* >(NULL));
	int num_active_parameters = 0;

	// To handle constant parameters between non-constant parameter blocks, the
	// start position --- a raw parameter index --- of each contiguous block of
	// non-constant parameters is recorded in start_derivative_section.
	std::vector<int> start_derivative_section;
	bool in_derivative_section = false;
	int parameter_cursor = 0;

	// Discover the derivative sections and set the parameter values.
	for (int i = 0; i < num_parameter_blocks; ++i) {
	jet_parameters[i] = &input_jets[parameter_cursor];

	const int parameter_block_size = parameter_block_sizes()[i];
	if (jacobians[i] != NULL) {
	if (!in_derivative_section) {
	start_derivative_section.push_back(parameter_cursor);
	in_derivative_section = true;
	}

	num_active_parameters += parameter_block_size;
	} else {
	in_derivative_section = false;
	}

	for (int j = 0; j < parameter_block_size; ++j, parameter_cursor++) {
	input_jets[parameter_cursor].a = parameters[i][j];
	}
	}

	// When `num_active_parameters % Stride != 0` then it can be the case
	// that `active_parameter_count < Stride` while parameter_cursor is less
	// than the total number of parameters and with no remaining non-constant
	// parameter blocks. Pushing parameter_cursor (the total number of
	// parameters) as a final entry to start_derivative_section is required
	// because if a constant parameter block is encountered after the
	// last non-constant block then current_derivative_section is incremented
	// and would otherwise index an invalid position in
	// start_derivative_section. Setting the final element to the total number
	// of parameters means that this can only happen at most once in the loop
	// below.
	start_derivative_section.push_back(parameter_cursor);

	// Evaluate all of the strides. Each stride is a chunk of the derivative to
	// evaluate, typically some size proportional to the size of the SIMD
	// registers of the CPU.
	int num_strides = static_cast<int>(ceil(num_active_parameters /
	static_cast<float>(Stride)));

	int current_derivative_section = 0;
	int current_derivative_section_cursor = 0;

	for (int pass = 0; pass < num_strides; ++pass) {
	// Set most of the jet components to zero, except for
	// non-constant #Stride parameters.
	const int initial_derivative_section = current_derivative_section;
	const int initial_derivative_section_cursor =
	current_derivative_section_cursor;

	int active_parameter_count = 0;
	parameter_cursor = 0;

	for (int i = 0; i < num_parameter_blocks; ++i) {
	for (int j = 0; j < parameter_block_sizes()[i];
	++j, parameter_cursor++) {
	input_jets[parameter_cursor].v.setZero();
	if (active_parameter_count < Stride &&
	parameter_cursor >= (
	start_derivative_section[current_derivative_section] +
	current_derivative_section_cursor)) {
	if (jacobians[i] != NULL) {
	input_jets[parameter_cursor].v[active_parameter_count] = 1.0;
	++active_parameter_count;
	++current_derivative_section_cursor;
	} else {
	++current_derivative_section;
	current_derivative_section_cursor = 0;
	}
	}
	}
	}

	if (!(*functor_)(&jet_parameters[0], &output_jets[0])) {
	return false;
	}

	// Copy the pieces of the jacobians into their final place.
	active_parameter_count = 0;

	current_derivative_section = initial_derivative_section;
	current_derivative_section_cursor = initial_derivative_section_cursor;

	for (int i = 0, parameter_cursor = 0; i < num_parameter_blocks; ++i) {
	for (int j = 0; j < parameter_block_sizes()[i];
	++j, parameter_cursor++) {
	if (active_parameter_count < Stride &&
	parameter_cursor >= (
	start_derivative_section[current_derivative_section] +
	current_derivative_section_cursor)) {
	if (jacobians[i] != NULL) {
	for (int k = 0; k < num_residuals(); ++k) {
	jacobians[i][k * parameter_block_sizes()[i] + j] =
	output_jets[k].v[active_parameter_count];
	}
	++active_parameter_count;
	++current_derivative_section_cursor;
	} else {
	++current_derivative_section;
	current_derivative_section_cursor = 0;
	}
	}
	}
	}

	// Only copy the residuals over once (even though we compute them on
	// every loop).
	if (pass == num_strides - 1) {
	for (int k = 0; k < num_residuals(); ++k) {
	residuals[k] = output_jets[k].a;
	}
	}
	}
	return true;
	}

	private:
	internal::scoped_ptr<CostFunctor> functor_;
	};

	} // namespace ceres

	#endif // CERES_PUBLIC_DYNAMIC_AUTODIFF_COST_FUNCTION_H_