// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2013 Google Inc. All rights reserved.
// http://code.google.com/p/ceres-solver/
//
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// modification, are permitted provided that the following conditions are met:
//
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// 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_