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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2023 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
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// 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
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// 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)
#include "ceres/compressed_row_jacobian_writer.h"
#include <algorithm>
#include <iterator>
#include <memory>
#include <string>
#include <utility>
#include <vector>
#include "ceres/casts.h"
#include "ceres/compressed_row_sparse_matrix.h"
#include "ceres/parameter_block.h"
#include "ceres/program.h"
#include "ceres/residual_block.h"
#include "ceres/scratch_evaluate_preparer.h"
namespace ceres::internal {
void CompressedRowJacobianWriter::PopulateJacobianRowAndColumnBlockVectors(
const Program* program, CompressedRowSparseMatrix* jacobian) {
const auto& parameter_blocks = program->parameter_blocks();
auto& col_blocks = *(jacobian->mutable_col_blocks());
col_blocks.resize(parameter_blocks.size());
int col_pos = 0;
for (int i = 0; i < parameter_blocks.size(); ++i) {
col_blocks[i].size = parameter_blocks[i]->TangentSize();
col_blocks[i].position = col_pos;
col_pos += col_blocks[i].size;
}
const auto& residual_blocks = program->residual_blocks();
auto& row_blocks = *(jacobian->mutable_row_blocks());
row_blocks.resize(residual_blocks.size());
int row_pos = 0;
for (int i = 0; i < residual_blocks.size(); ++i) {
row_blocks[i].size = residual_blocks[i]->NumResiduals();
row_blocks[i].position = row_pos;
row_pos += row_blocks[i].size;
}
}
void CompressedRowJacobianWriter::GetOrderedParameterBlocks(
const Program* program,
int residual_id,
std::vector<std::pair<int, int>>* evaluated_jacobian_blocks) {
auto residual_block = program->residual_blocks()[residual_id];
const int num_parameter_blocks = residual_block->NumParameterBlocks();
for (int j = 0; j < num_parameter_blocks; ++j) {
auto parameter_block = residual_block->parameter_blocks()[j];
if (!parameter_block->IsConstant()) {
evaluated_jacobian_blocks->push_back(
std::make_pair(parameter_block->index(), j));
}
}
std::sort(evaluated_jacobian_blocks->begin(),
evaluated_jacobian_blocks->end());
}
std::unique_ptr<SparseMatrix> CompressedRowJacobianWriter::CreateJacobian()
const {
const auto& residual_blocks = program_->residual_blocks();
const int total_num_residuals = program_->NumResiduals();
const int total_num_effective_parameters = program_->NumEffectiveParameters();
// Count the number of jacobian nonzeros.
//
// We used an unsigned int here, so that we can compare it INT_MAX without
// triggering overflow behaviour.
unsigned int num_jacobian_nonzeros = total_num_effective_parameters;
for (auto* residual_block : residual_blocks) {
const int num_residuals = residual_block->NumResiduals();
const int num_parameter_blocks = residual_block->NumParameterBlocks();
for (int j = 0; j < num_parameter_blocks; ++j) {
auto parameter_block = residual_block->parameter_blocks()[j];
if (!parameter_block->IsConstant()) {
num_jacobian_nonzeros += num_residuals * parameter_block->TangentSize();
if (num_jacobian_nonzeros > std::numeric_limits<int>::max()) {
LOG(ERROR) << "Unable to create Jacobian matrix: Too many entries in "
"the Jacobian matrix. num_jacobian_nonzeros = "
<< num_jacobian_nonzeros;
return nullptr;
}
}
}
}
// Allocate storage for the jacobian with some extra space at the end.
// Allocate more space than needed to store the jacobian so that when the LM
// algorithm adds the diagonal, no reallocation is necessary. This reduces
// peak memory usage significantly.
auto jacobian = std::make_unique<CompressedRowSparseMatrix>(
total_num_residuals,
total_num_effective_parameters,
static_cast<int>(num_jacobian_nonzeros));
// At this stage, the CompressedRowSparseMatrix is an invalid state. But
// this seems to be the only way to construct it without doing a memory
// copy.
int* rows = jacobian->mutable_rows();
int* cols = jacobian->mutable_cols();
int row_pos = 0;
rows[0] = 0;
for (auto* residual_block : residual_blocks) {
const int num_parameter_blocks = residual_block->NumParameterBlocks();
// Count the number of derivatives for a row of this residual block and
// build a list of active parameter block indices.
int num_derivatives = 0;
std::vector<int> parameter_indices;
for (int j = 0; j < num_parameter_blocks; ++j) {
auto parameter_block = residual_block->parameter_blocks()[j];
if (!parameter_block->IsConstant()) {
parameter_indices.push_back(parameter_block->index());
num_derivatives += parameter_block->TangentSize();
}
}
// Sort the parameters by their position in the state vector.
std::sort(parameter_indices.begin(), parameter_indices.end());
if (adjacent_find(parameter_indices.begin(), parameter_indices.end()) !=
parameter_indices.end()) {
std::string parameter_block_description;
for (int j = 0; j < num_parameter_blocks; ++j) {
auto parameter_block = residual_block->parameter_blocks()[j];
parameter_block_description += parameter_block->ToString() + "\n";
}
LOG(FATAL) << "Ceres internal error: "
<< "Duplicate parameter blocks detected in a cost function. "
<< "This should never happen. Please report this to "
<< "the Ceres developers.\n"
<< "Residual Block: " << residual_block->ToString() << "\n"
<< "Parameter Blocks: " << parameter_block_description;
}
// Update the row indices.
const int num_residuals = residual_block->NumResiduals();
for (int j = 0; j < num_residuals; ++j) {
rows[row_pos + j + 1] = rows[row_pos + j] + num_derivatives;
}
// Iterate over parameter blocks in the order which they occur in the
// parameter vector. This code mirrors that in Write(), where jacobian
// values are updated.
int col_pos = 0;
for (int parameter_index : parameter_indices) {
auto parameter_block = program_->parameter_blocks()[parameter_index];
const int parameter_block_size = parameter_block->TangentSize();
for (int r = 0; r < num_residuals; ++r) {
// This is the position in the values array of the jacobian where this
// row of the jacobian block should go.
const int column_block_begin = rows[row_pos + r] + col_pos;
for (int c = 0; c < parameter_block_size; ++c) {
cols[column_block_begin + c] = parameter_block->delta_offset() + c;
}
}
col_pos += parameter_block_size;
}
row_pos += num_residuals;
}
CHECK_EQ(num_jacobian_nonzeros - total_num_effective_parameters,
rows[total_num_residuals]);
PopulateJacobianRowAndColumnBlockVectors(program_, jacobian.get());
return jacobian;
}
void CompressedRowJacobianWriter::Write(int residual_id,
int residual_offset,
double** jacobians,
SparseMatrix* base_jacobian) {
auto* jacobian = down_cast<CompressedRowSparseMatrix*>(base_jacobian);
double* jacobian_values = jacobian->mutable_values();
const int* jacobian_rows = jacobian->rows();
auto residual_block = program_->residual_blocks()[residual_id];
const int num_residuals = residual_block->NumResiduals();
std::vector<std::pair<int, int>> evaluated_jacobian_blocks;
GetOrderedParameterBlocks(program_, residual_id, &evaluated_jacobian_blocks);
// Where in the current row does the jacobian for a parameter block begin.
int col_pos = 0;
// Iterate over the jacobian blocks in increasing order of their
// positions in the reduced parameter vector.
for (auto& evaluated_jacobian_block : evaluated_jacobian_blocks) {
auto parameter_block =
program_->parameter_blocks()[evaluated_jacobian_block.first];
const int argument = evaluated_jacobian_block.second;
const int parameter_block_size = parameter_block->TangentSize();
// Copy one row of the jacobian block at a time.
for (int r = 0; r < num_residuals; ++r) {
// Position of the r^th row of the current jacobian block.
const double* block_row_begin =
jacobians[argument] + r * parameter_block_size;
// Position in the values array of the jacobian where this
// row of the jacobian block should go.
double* column_block_begin =
jacobian_values + jacobian_rows[residual_offset + r] + col_pos;
std::copy(block_row_begin,
block_row_begin + parameter_block_size,
column_block_begin);
}
col_pos += parameter_block_size;
}
}
} // namespace ceres::internal