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Diffstat (limited to 'extern/libmv/third_party/ceres/include/ceres/dynamic_autodiff_cost_function.h')
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diff --git a/extern/libmv/third_party/ceres/include/ceres/dynamic_autodiff_cost_function.h b/extern/libmv/third_party/ceres/include/ceres/dynamic_autodiff_cost_function.h
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--- a/extern/libmv/third_party/ceres/include/ceres/dynamic_autodiff_cost_function.h
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-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2013 Google Inc. All rights reserved.
-// http://code.google.com/p/ceres-solver/
-//
-// 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)
-//
-// 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.
-
-#ifndef CERES_PUBLIC_DYNAMIC_AUTODIFF_COST_FUNCTION_H_
-#define CERES_PUBLIC_DYNAMIC_AUTODIFF_COST_FUNCTION_H_
-
-#include <cmath>
-#include <numeric>
-#include <vector>
-
-#include "ceres/cost_function.h"
-#include "ceres/internal/scoped_ptr.h"
-#include "ceres/jet.h"
-#include "glog/logging.h"
-
-namespace ceres {
-
-template <typename CostFunctor, int Stride = 4>
-class DynamicAutoDiffCostFunction : public CostFunction {
- public:
- explicit DynamicAutoDiffCostFunction(CostFunctor* functor)
- : functor_(functor) {}
-
- virtual ~DynamicAutoDiffCostFunction() {}
-
- void AddParameterBlock(int size) {
- mutable_parameter_block_sizes()->push_back(size);
- }
-
- void SetNumResiduals(int num_residuals) {
- set_num_residuals(num_residuals);
- }
-
- 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.
- vector<Jet<double, Stride> > input_jets(num_parameters);
- vector<Jet<double, Stride> > output_jets(num_residuals());
-
- // Make the parameter pack that is sent to the functor (reused).
- 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.
- 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_