| 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188189190191192193194195196197198199200201202203204205206207208209210211212213214215216217 | // 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: keir@google.com (Keir Mierle)//         sameeragarwal@google.com (Sameer Agarwal)#include "ceres/residual_block.h"#include <algorithm>#include <cstddef>#include <vector>#include "ceres/corrector.h"#include "ceres/cost_function.h"#include "ceres/internal/eigen.h"#include "ceres/internal/fixed_array.h"#include "ceres/local_parameterization.h"#include "ceres/loss_function.h"#include "ceres/parameter_block.h"#include "ceres/residual_block_utils.h"#include "ceres/small_blas.h"using Eigen::Dynamic;namespace ceres {namespace internal {ResidualBlock::ResidualBlock(    const CostFunction* cost_function, const LossFunction* loss_function,    const std::vector<ParameterBlock*>& parameter_blocks, int index)    : cost_function_(cost_function),      loss_function_(loss_function),      parameter_blocks_(          new ParameterBlock*[cost_function->parameter_block_sizes().size()]),      index_(index) {  CHECK(cost_function_ != nullptr);  std::copy(parameter_blocks.begin(),            parameter_blocks.end(),            parameter_blocks_.get());}bool ResidualBlock::Evaluate(const bool apply_loss_function,                             double* cost,                             double* residuals,                             double** jacobians,                             double* scratch) const {  const int num_parameter_blocks = NumParameterBlocks();  const int num_residuals = cost_function_->num_residuals();  // Collect the parameters from their blocks. This will rarely allocate, since  // residuals taking more than 8 parameter block arguments are rare.  FixedArray<const double*, 8> parameters(num_parameter_blocks);  for (int i = 0; i < num_parameter_blocks; ++i) {    parameters[i] = parameter_blocks_[i]->state();  }  // Put pointers into the scratch space into global_jacobians as appropriate.  FixedArray<double*, 8> global_jacobians(num_parameter_blocks);  if (jacobians != nullptr) {    for (int i = 0; i < num_parameter_blocks; ++i) {      const ParameterBlock* parameter_block = parameter_blocks_[i];      if (jacobians[i] != nullptr &&          parameter_block->LocalParameterizationJacobian() != nullptr) {        global_jacobians[i] = scratch;        scratch += num_residuals * parameter_block->Size();      } else {        global_jacobians[i] = jacobians[i];      }    }  }  // If the caller didn't request residuals, use the scratch space for them.  bool outputting_residuals = (residuals != nullptr);  if (!outputting_residuals) {    residuals = scratch;  }  // Invalidate the evaluation buffers so that we can check them after  // the CostFunction::Evaluate call, to see if all the return values  // that were required were written to and that they are finite.  double** eval_jacobians =      (jacobians != nullptr) ? global_jacobians.data() : nullptr;  InvalidateEvaluation(*this, cost, residuals, eval_jacobians);  if (!cost_function_->Evaluate(parameters.data(), residuals, eval_jacobians)) {    return false;  }  if (!IsEvaluationValid(*this,                         parameters.data(),                         cost,                         residuals,                         eval_jacobians)) {    std::string message =        "\n\n"        "Error in evaluating the ResidualBlock.\n\n"        "There are two possible reasons. Either the CostFunction did not evaluate and fill all    \n"  // NOLINT        "residual and jacobians that were requested or there was a non-finite value (nan/infinite)\n"  // NOLINT        "generated during the or jacobian computation. \n\n" +        EvaluationToString(*this,                           parameters.data(),                           cost,                           residuals,                           eval_jacobians);    LOG(WARNING) << message;    return false;  }  double squared_norm = VectorRef(residuals, num_residuals).squaredNorm();  // Update the jacobians with the local parameterizations.  if (jacobians != nullptr) {    for (int i = 0; i < num_parameter_blocks; ++i) {      if (jacobians[i] != nullptr) {        const ParameterBlock* parameter_block = parameter_blocks_[i];        // Apply local reparameterization to the jacobians.        if (parameter_block->LocalParameterizationJacobian() != nullptr) {          // jacobians[i] = global_jacobians[i] * global_to_local_jacobian.          MatrixMatrixMultiply<Dynamic, Dynamic, Dynamic, Dynamic, 0>(              global_jacobians[i],              num_residuals,              parameter_block->Size(),              parameter_block->LocalParameterizationJacobian(),              parameter_block->Size(),              parameter_block->LocalSize(),              jacobians[i], 0, 0,  num_residuals, parameter_block->LocalSize());        }      }    }  }  if (loss_function_ == nullptr || !apply_loss_function) {    *cost = 0.5 * squared_norm;    return true;  }  double rho[3];  loss_function_->Evaluate(squared_norm, rho);  *cost = 0.5 * rho[0];  // No jacobians and not outputting residuals? All done. Doing an early exit  // here avoids constructing the "Corrector" object below in a common case.  if (jacobians == nullptr && !outputting_residuals) {    return true;  }  // Correct for the effects of the loss function. The jacobians need to be  // corrected before the residuals, since they use the uncorrected residuals.  Corrector correct(squared_norm, rho);  if (jacobians != nullptr) {    for (int i = 0; i < num_parameter_blocks; ++i) {      if (jacobians[i] != nullptr) {        const ParameterBlock* parameter_block = parameter_blocks_[i];        // Correct the jacobians for the loss function.        correct.CorrectJacobian(num_residuals,                                parameter_block->LocalSize(),                                residuals,                                jacobians[i]);      }    }  }  // Correct the residuals with the loss function.  if (outputting_residuals) {    correct.CorrectResiduals(num_residuals, residuals);  }  return true;}int ResidualBlock::NumScratchDoublesForEvaluate() const {  // Compute the amount of scratch space needed to store the full-sized  // jacobians. For parameters that have no local parameterization  no storage  // is needed and the passed-in jacobian array is used directly. Also include  // space to store the residuals, which is needed for cost-only evaluations.  // This is slightly pessimistic, since both won't be needed all the time, but  // the amount of excess should not cause problems for the caller.  int num_parameters = NumParameterBlocks();  int scratch_doubles = 1;  for (int i = 0; i < num_parameters; ++i) {    const ParameterBlock* parameter_block = parameter_blocks_[i];    if (parameter_block->LocalParameterizationJacobian() != nullptr) {      scratch_doubles += parameter_block->Size();    }  }  scratch_doubles *= NumResiduals();  return scratch_doubles;}}  // namespace internal}  // namespace ceres
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