197 lines
7.1 KiB
C++
197 lines
7.1 KiB
C++
// Ceres Solver - A fast non-linear least squares minimizer
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// Copyright 2015 Google Inc. All rights reserved.
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// http://ceres-solver.org/
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//
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// Redistribution and use in source and binary forms, with or without
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// modification, are permitted provided that the following conditions are met:
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//
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// * Redistributions of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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// * Redistributions in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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// * Neither the name of Google Inc. nor the names of its contributors may be
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// used to endorse or promote products derived from this software without
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// specific prior written permission.
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//
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// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
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// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
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// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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// POSSIBILITY OF SUCH DAMAGE.
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//
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// Author: sameeragarwal@google.com (Sameer Agarwal)
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#include "ceres/block_random_access_sparse_matrix.h"
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#include <algorithm>
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#include <set>
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#include <utility>
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#include <vector>
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#include "ceres/internal/port.h"
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#include "ceres/internal/scoped_ptr.h"
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#include "ceres/mutex.h"
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#include "ceres/triplet_sparse_matrix.h"
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#include "ceres/types.h"
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#include "glog/logging.h"
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namespace ceres {
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namespace internal {
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using std::make_pair;
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using std::pair;
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using std::set;
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using std::vector;
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BlockRandomAccessSparseMatrix::BlockRandomAccessSparseMatrix(
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const vector<int>& blocks,
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const set<pair<int, int> >& block_pairs)
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: kMaxRowBlocks(10 * 1000 * 1000),
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blocks_(blocks) {
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CHECK_LT(blocks.size(), kMaxRowBlocks);
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// Build the row/column layout vector and count the number of scalar
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// rows/columns.
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int num_cols = 0;
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block_positions_.reserve(blocks_.size());
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for (int i = 0; i < blocks_.size(); ++i) {
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block_positions_.push_back(num_cols);
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num_cols += blocks_[i];
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}
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// Count the number of scalar non-zero entries and build the layout
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// object for looking into the values array of the
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// TripletSparseMatrix.
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int num_nonzeros = 0;
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for (set<pair<int, int> >::const_iterator it = block_pairs.begin();
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it != block_pairs.end();
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++it) {
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const int row_block_size = blocks_[it->first];
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const int col_block_size = blocks_[it->second];
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num_nonzeros += row_block_size * col_block_size;
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}
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VLOG(1) << "Matrix Size [" << num_cols
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<< "," << num_cols
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<< "] " << num_nonzeros;
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tsm_.reset(new TripletSparseMatrix(num_cols, num_cols, num_nonzeros));
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tsm_->set_num_nonzeros(num_nonzeros);
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int* rows = tsm_->mutable_rows();
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int* cols = tsm_->mutable_cols();
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double* values = tsm_->mutable_values();
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int pos = 0;
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for (set<pair<int, int> >::const_iterator it = block_pairs.begin();
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it != block_pairs.end();
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++it) {
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const int row_block_size = blocks_[it->first];
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const int col_block_size = blocks_[it->second];
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cell_values_.push_back(make_pair(make_pair(it->first, it->second),
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values + pos));
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layout_[IntPairToLong(it->first, it->second)] =
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new CellInfo(values + pos);
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pos += row_block_size * col_block_size;
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}
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// Fill the sparsity pattern of the underlying matrix.
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for (set<pair<int, int> >::const_iterator it = block_pairs.begin();
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it != block_pairs.end();
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++it) {
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const int row_block_id = it->first;
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const int col_block_id = it->second;
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const int row_block_size = blocks_[row_block_id];
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const int col_block_size = blocks_[col_block_id];
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int pos =
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layout_[IntPairToLong(row_block_id, col_block_id)]->values - values;
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for (int r = 0; r < row_block_size; ++r) {
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for (int c = 0; c < col_block_size; ++c, ++pos) {
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rows[pos] = block_positions_[row_block_id] + r;
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cols[pos] = block_positions_[col_block_id] + c;
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values[pos] = 1.0;
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DCHECK_LT(rows[pos], tsm_->num_rows());
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DCHECK_LT(cols[pos], tsm_->num_rows());
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}
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}
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}
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}
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// Assume that the user does not hold any locks on any cell blocks
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// when they are calling SetZero.
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BlockRandomAccessSparseMatrix::~BlockRandomAccessSparseMatrix() {
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for (LayoutType::iterator it = layout_.begin();
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it != layout_.end();
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++it) {
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delete it->second;
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}
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}
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CellInfo* BlockRandomAccessSparseMatrix::GetCell(int row_block_id,
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int col_block_id,
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int* row,
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int* col,
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int* row_stride,
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int* col_stride) {
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const LayoutType::iterator it =
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layout_.find(IntPairToLong(row_block_id, col_block_id));
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if (it == layout_.end()) {
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return NULL;
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}
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// Each cell is stored contiguously as its own little dense matrix.
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*row = 0;
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*col = 0;
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*row_stride = blocks_[row_block_id];
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*col_stride = blocks_[col_block_id];
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return it->second;
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}
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// Assume that the user does not hold any locks on any cell blocks
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// when they are calling SetZero.
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void BlockRandomAccessSparseMatrix::SetZero() {
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if (tsm_->num_nonzeros()) {
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VectorRef(tsm_->mutable_values(),
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tsm_->num_nonzeros()).setZero();
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}
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}
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void BlockRandomAccessSparseMatrix::SymmetricRightMultiply(const double* x,
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double* y) const {
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vector< pair<pair<int, int>, double*> >::const_iterator it =
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cell_values_.begin();
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for (; it != cell_values_.end(); ++it) {
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const int row = it->first.first;
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const int row_block_size = blocks_[row];
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const int row_block_pos = block_positions_[row];
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const int col = it->first.second;
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const int col_block_size = blocks_[col];
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const int col_block_pos = block_positions_[col];
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MatrixVectorMultiply<Eigen::Dynamic, Eigen::Dynamic, 1>(
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it->second, row_block_size, col_block_size,
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x + col_block_pos,
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y + row_block_pos);
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// Since the matrix is symmetric, but only the upper triangular
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// part is stored, if the block being accessed is not a diagonal
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// block, then use the same block to do the corresponding lower
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// triangular multiply also.
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if (row != col) {
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MatrixTransposeVectorMultiply<Eigen::Dynamic, Eigen::Dynamic, 1>(
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it->second, row_block_size, col_block_size,
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x + row_block_pos,
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y + col_block_pos);
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}
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}
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}
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} // namespace internal
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} // namespace ceres
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