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https://github.com/ceres-solver/ceres-solver.git
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19ab2c1793
1. Add threading to all three subclasses of BlockRandomAccessMatrix. i.e. BlockRandomAccessDenseMatrix, BlockRandomAccessSparseMatrix and BlockRandomAccessDenseMatrix. For BlockRandomAccessDenseMatrix and BlockRandomAccessSparseMatrix this just means SetZero is parallelized. Which by itself is no big deal, but by doing so, the constructor for all three subclasses become uniform. BlockRandomAccessSparseMatrix::SymmetricRightMultiplyAndAccumulate maybe threaded in the future if needed. BlockRandomAccessDiagonalMatrix is the biggest beneficiary. SetZero Invert and RightMultiplyAndAccumulate are all threaded now. 2. Change the storage in BlockRandomAccessDiagonalMatrix from TripletSparseMatrix to CompressedRowSparseMatrix. This has no performance implications since we do not really use the capabilities of the underlying matrix indexing representation. This is a forward looking change when we decide to transfer this matrix to the GPU, a CompressedRowSparseMatrix will save on a data conversion. 3. Use std::unique_ptr as needed and eliminate the need for custom destructors. 4. Modify CompressedRowSparseMatrix::CreateBlockDiagonalMatrix to take a nullptr as the data vector. Fixes https://github.com/ceres-solver/ceres-solver/issues/936 Fixes https://github.com/ceres-solver/ceres-solver/issues/935 Change-Id: Ia6487f2d924fbe669835bdcc38abf2b451bda4ee
118 lines
4.5 KiB
C++
118 lines
4.5 KiB
C++
// Ceres Solver - A fast non-linear least squares minimizer
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// Copyright 2022 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_diagonal_matrix.h"
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#include <algorithm>
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#include <memory>
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#include <set>
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#include <utility>
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#include <vector>
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#include "Eigen/Dense"
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#include "ceres/compressed_row_sparse_matrix.h"
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#include "ceres/internal/export.h"
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#include "ceres/parallel_for.h"
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#include "ceres/stl_util.h"
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#include "ceres/types.h"
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#include "glog/logging.h"
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namespace ceres::internal {
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BlockRandomAccessDiagonalMatrix::BlockRandomAccessDiagonalMatrix(
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const std::vector<Block>& blocks, ContextImpl* context, int num_threads)
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: context_(context), num_threads_(num_threads) {
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m_ = CompressedRowSparseMatrix::CreateBlockDiagonalMatrix(nullptr, blocks);
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double* values = m_->mutable_values();
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layout_.reserve(blocks.size());
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for (auto& block : blocks) {
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layout_.emplace_back(std::make_unique<CellInfo>(values));
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values += block.size * block.size;
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}
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}
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CellInfo* BlockRandomAccessDiagonalMatrix::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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if (row_block_id != col_block_id) {
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return nullptr;
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}
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auto& blocks = m_->row_blocks();
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const int stride = blocks[row_block_id].size;
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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 = stride;
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*col_stride = stride;
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return layout_[row_block_id].get();
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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 BlockRandomAccessDiagonalMatrix::SetZero() {
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ParallelSetZero(
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context_, num_threads_, m_->mutable_values(), m_->num_nonzeros());
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}
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void BlockRandomAccessDiagonalMatrix::Invert() {
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auto& blocks = m_->row_blocks();
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const int num_blocks = blocks.size();
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ParallelFor(context_, 0, num_blocks, num_threads_, [this, blocks](int i) {
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auto* cell_info = layout_[i].get();
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auto& block = blocks[i];
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MatrixRef b(cell_info->values, block.size, block.size);
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b = b.selfadjointView<Eigen::Upper>().llt().solve(
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Matrix::Identity(block.size, block.size));
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});
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}
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void BlockRandomAccessDiagonalMatrix::RightMultiplyAndAccumulate(
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const double* x, double* y) const {
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CHECK(x != nullptr);
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CHECK(y != nullptr);
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auto& blocks = m_->row_blocks();
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const int num_blocks = blocks.size();
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ParallelFor(
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context_, 0, num_blocks, num_threads_, [this, blocks, x, y](int i) {
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auto* cell_info = layout_[i].get();
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auto& block = blocks[i];
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ConstMatrixRef b(cell_info->values, block.size, block.size);
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VectorRef(y + block.position, block.size).noalias() +=
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b * ConstVectorRef(x + block.position, block.size);
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});
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}
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} // namespace ceres::internal
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