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diff --git a/include/ceres/dynamic_autodiff_cost_function.h b/include/ceres/dynamic_autodiff_cost_function.h
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+// 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 <memory>
+#include "ceres/dynamic_cost_function.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 Stride *
+ // num_residuals() derivatives. This is done with small, fixed-size jets.
+ const int num_parameter_blocks =
+ static_cast<int>(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:
+ std::unique_ptr<CostFunctor> functor_;
+};
+
+} // namespace ceres
+
+#endif // CERES_PUBLIC_DYNAMIC_AUTODIFF_COST_FUNCTION_H_