mirror of
https://github.com/ceres-solver/ceres-solver.git
synced 2026-08-30 00:50:37 +08:00
Sameer Agarwal
04899645cc
These methods were historically poorly named and every time I read code I get confused whether they are just multiplying or multiplying and adding. Clarifying them also gives us the changce to introduce RightMultiply and LeftMultiply methods in the base class which will simplify a number call sites in a subsequent CL. Fixes https://github.com/ceres-solver/ceres-solver/issues/855 Change-Id: Ice4fb483f1acd02527a6dd753ef0c5a66037f4b0
343 lines
13 KiB
C++
343 lines
13 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/visibility_based_preconditioner.h"
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#include <memory>
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#include "Eigen/Dense"
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#include "ceres/block_random_access_dense_matrix.h"
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#include "ceres/block_random_access_sparse_matrix.h"
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#include "ceres/block_sparse_matrix.h"
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#include "ceres/casts.h"
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#include "ceres/file.h"
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#include "ceres/internal/eigen.h"
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#include "ceres/linear_least_squares_problems.h"
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#include "ceres/schur_eliminator.h"
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#include "ceres/stringprintf.h"
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#include "ceres/test_util.h"
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#include "ceres/types.h"
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#include "glog/logging.h"
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#include "gtest/gtest.h"
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namespace ceres::internal {
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// TODO(sameeragarwal): Re-enable this test once serialization is
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// working again.
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// using testing::AssertionResult;
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// using testing::AssertionSuccess;
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// using testing::AssertionFailure;
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// static const double kTolerance = 1e-12;
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// class VisibilityBasedPreconditionerTest : public ::testing::Test {
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// public:
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// static const int kCameraSize = 9;
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// protected:
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// void SetUp() {
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// string input_file = TestFileAbsolutePath("problem-6-1384-000.lsqp");
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// std::unique_ptr<LinearLeastSquaresProblem> problem =
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// CreateLinearLeastSquaresProblemFromFile(input_file));
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// A_.reset(down_cast<BlockSparseMatrix*>(problem->A.release()));
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// b_.reset(problem->b.release());
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// D_.reset(problem->D.release());
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// const CompressedRowBlockStructure* bs =
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// CHECK_NOTNULL(A_->block_structure());
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// const int num_col_blocks = bs->cols.size();
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// num_cols_ = A_->num_cols();
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// num_rows_ = A_->num_rows();
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// num_eliminate_blocks_ = problem->num_eliminate_blocks;
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// num_camera_blocks_ = num_col_blocks - num_eliminate_blocks_;
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// options_.elimination_groups.push_back(num_eliminate_blocks_);
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// options_.elimination_groups.push_back(
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// A_->block_structure()->cols.size() - num_eliminate_blocks_);
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// vector<int> blocks(num_col_blocks - num_eliminate_blocks_, 0);
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// for (int i = num_eliminate_blocks_; i < num_col_blocks; ++i) {
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// blocks[i - num_eliminate_blocks_] = bs->cols[i].size;
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// }
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// // The input matrix is a real jacobian and fairly poorly
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// // conditioned. Setting D to a large constant makes the normal
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// // equations better conditioned and makes the tests below better
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// // conditioned.
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// VectorRef(D_.get(), num_cols_).setConstant(10.0);
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// schur_complement_ =
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// std::make_unique<BlockRandomAccessDenseMatrix>(blocks);
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// Vector rhs(schur_complement_->num_rows());
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// std::unique_ptr<SchurEliminatorBase> eliminator;
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// LinearSolver::Options eliminator_options;
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// eliminator_options.elimination_groups = options_.elimination_groups;
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// eliminator_options.num_threads = options_.num_threads;
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// eliminator = SchurEliminatorBase::Create(eliminator_options);
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// eliminator->Init(num_eliminate_blocks_, bs);
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// eliminator->Eliminate(A_.get(), b_.get(), D_.get(),
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// schur_complement_.get(), rhs.data());
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// }
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// AssertionResult IsSparsityStructureValid() {
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// preconditioner_->InitStorage(*A_->block_structure());
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// const std::unordered_set<pair<int, int>, pair_hash>& cluster_pairs =
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// get_cluster_pairs(); const vector<int>& cluster_membership =
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// get_cluster_membership();
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// for (int i = 0; i < num_camera_blocks_; ++i) {
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// for (int j = i; j < num_camera_blocks_; ++j) {
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// if (cluster_pairs.count(make_pair(cluster_membership[i],
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// cluster_membership[j]))) {
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// if (!IsBlockPairInPreconditioner(i, j)) {
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// return AssertionFailure()
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// << "block pair (" << i << "," << j << "missing";
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// }
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// } else {
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// if (IsBlockPairInPreconditioner(i, j)) {
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// return AssertionFailure()
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// << "block pair (" << i << "," << j << "should not be present";
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// }
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// }
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// }
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// }
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// return AssertionSuccess();
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// }
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// AssertionResult PreconditionerValuesMatch() {
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// preconditioner_->Update(*A_, D_.get());
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// const std::unordered_set<pair<int, int>, pair_hash>& cluster_pairs =
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// get_cluster_pairs(); const BlockRandomAccessSparseMatrix* m = get_m();
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// Matrix preconditioner_matrix;
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// m->matrix()->ToDenseMatrix(&preconditioner_matrix);
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// ConstMatrixRef full_schur_complement(schur_complement_->values(),
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// m->num_rows(),
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// m->num_rows());
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// const int num_clusters = get_num_clusters();
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// const int kDiagonalBlockSize =
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// kCameraSize * num_camera_blocks_ / num_clusters;
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// for (int i = 0; i < num_clusters; ++i) {
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// for (int j = i; j < num_clusters; ++j) {
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// double diff = 0.0;
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// if (cluster_pairs.count(make_pair(i, j))) {
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// diff =
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// (preconditioner_matrix.block(kDiagonalBlockSize * i,
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// kDiagonalBlockSize * j,
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// kDiagonalBlockSize,
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// kDiagonalBlockSize) -
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// full_schur_complement.block(kDiagonalBlockSize * i,
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// kDiagonalBlockSize * j,
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// kDiagonalBlockSize,
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// kDiagonalBlockSize)).norm();
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// } else {
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// diff = preconditioner_matrix.block(kDiagonalBlockSize * i,
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// kDiagonalBlockSize * j,
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// kDiagonalBlockSize,
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// kDiagonalBlockSize).norm();
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// }
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// if (diff > kTolerance) {
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// return AssertionFailure()
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// << "Preconditioner block " << i << " " << j << " differs "
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// << "from expected value by " << diff;
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// }
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// }
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// }
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// return AssertionSuccess();
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// }
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// // Accessors
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// int get_num_blocks() { return preconditioner_->num_blocks_; }
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// int get_num_clusters() { return preconditioner_->num_clusters_; }
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// int* get_mutable_num_clusters() { return &preconditioner_->num_clusters_; }
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// const vector<int>& get_block_size() {
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// return preconditioner_->block_size_; }
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// vector<int>* get_mutable_block_size() {
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// return &preconditioner_->block_size_; }
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// const vector<int>& get_cluster_membership() {
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// return preconditioner_->cluster_membership_;
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// }
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// vector<int>* get_mutable_cluster_membership() {
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// return &preconditioner_->cluster_membership_;
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// }
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// const set<pair<int, int>>& get_block_pairs() {
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// return preconditioner_->block_pairs_;
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// }
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// set<pair<int, int>>* get_mutable_block_pairs() {
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// return &preconditioner_->block_pairs_;
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// }
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// const std::unordered_set<pair<int, int>, pair_hash>& get_cluster_pairs() {
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// return preconditioner_->cluster_pairs_;
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// }
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// std::unordered_set<pair<int, int>, pair_hash>* get_mutable_cluster_pairs()
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// {
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// return &preconditioner_->cluster_pairs_;
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// }
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// bool IsBlockPairInPreconditioner(const int block1, const int block2) {
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// return preconditioner_->IsBlockPairInPreconditioner(block1, block2);
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// }
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// bool IsBlockPairOffDiagonal(const int block1, const int block2) {
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// return preconditioner_->IsBlockPairOffDiagonal(block1, block2);
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// }
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// const BlockRandomAccessSparseMatrix* get_m() {
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// return preconditioner_->m_.get();
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// }
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// int num_rows_;
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// int num_cols_;
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// int num_eliminate_blocks_;
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// int num_camera_blocks_;
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// std::unique_ptr<BlockSparseMatrix> A_;
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// std::unique_ptr<double[]> b_;
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// std::unique_ptr<double[]> D_;
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// Preconditioner::Options options_;
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// std::unique_ptr<VisibilityBasedPreconditioner> preconditioner_;
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// std::unique_ptr<BlockRandomAccessDenseMatrix> schur_complement_;
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// };
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// TEST_F(VisibilityBasedPreconditionerTest, OneClusterClusterJacobi) {
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// options_.type = CLUSTER_JACOBI;
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// preconditioner_ =
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// std::make_unique<VisibilityBasedPreconditioner>(
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// *A_->block_structure(), options_);
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// // Override the clustering to be a single clustering containing all
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// // the cameras.
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// vector<int>& cluster_membership = *get_mutable_cluster_membership();
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// for (int i = 0; i < num_camera_blocks_; ++i) {
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// cluster_membership[i] = 0;
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// }
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// *get_mutable_num_clusters() = 1;
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// std::unordered_set<pair<int, int>, pair_hash>& cluster_pairs =
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// *get_mutable_cluster_pairs(); cluster_pairs.clear();
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// cluster_pairs.insert(make_pair(0, 0));
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// EXPECT_TRUE(IsSparsityStructureValid());
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// EXPECT_TRUE(PreconditionerValuesMatch());
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// // Multiplication by the inverse of the preconditioner.
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// const int num_rows = schur_complement_->num_rows();
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// ConstMatrixRef full_schur_complement(schur_complement_->values(),
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// num_rows,
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// num_rows);
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// Vector x(num_rows);
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// Vector y(num_rows);
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// Vector z(num_rows);
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// for (int i = 0; i < num_rows; ++i) {
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// x.setZero();
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// y.setZero();
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// z.setZero();
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// x[i] = 1.0;
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// preconditioner_->RightMultiplyAndAccumulate(x.data(), y.data());
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// z = full_schur_complement
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// .selfadjointView<Eigen::Upper>()
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// .llt().solve(x);
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// double max_relative_difference =
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// ((y - z).array() / z.array()).matrix().lpNorm<Eigen::Infinity>();
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// EXPECT_NEAR(max_relative_difference, 0.0, kTolerance);
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// }
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// }
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// TEST_F(VisibilityBasedPreconditionerTest, ClusterJacobi) {
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// options_.type = CLUSTER_JACOBI;
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// preconditioner_ =
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// std::make_unique<VisibilityBasedPreconditioner>(*A_->block_structure(),
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// options_);
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// // Override the clustering to be equal number of cameras.
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// vector<int>& cluster_membership = *get_mutable_cluster_membership();
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// cluster_membership.resize(num_camera_blocks_);
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// static const int kNumClusters = 3;
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// for (int i = 0; i < num_camera_blocks_; ++i) {
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// cluster_membership[i] = (i * kNumClusters) / num_camera_blocks_;
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// }
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// *get_mutable_num_clusters() = kNumClusters;
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// std::unordered_set<pair<int, int>, pair_hash>& cluster_pairs =
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// *get_mutable_cluster_pairs(); cluster_pairs.clear(); for (int i = 0; i <
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// kNumClusters; ++i) {
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// cluster_pairs.insert(make_pair(i, i));
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// }
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// EXPECT_TRUE(IsSparsityStructureValid());
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// EXPECT_TRUE(PreconditionerValuesMatch());
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// }
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// TEST_F(VisibilityBasedPreconditionerTest, ClusterTridiagonal) {
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// options_.type = CLUSTER_TRIDIAGONAL;
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// preconditioner_ =
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// std::make_unique<VisibilityBasedPreconditioner>(*A_->block_structure(),
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// options_);
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// static const int kNumClusters = 3;
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// // Override the clustering to be 3 clusters.
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// vector<int>& cluster_membership = *get_mutable_cluster_membership();
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// cluster_membership.resize(num_camera_blocks_);
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// for (int i = 0; i < num_camera_blocks_; ++i) {
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// cluster_membership[i] = (i * kNumClusters) / num_camera_blocks_;
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// }
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// *get_mutable_num_clusters() = kNumClusters;
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// // Spanning forest has structure 0-1 2
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// std::unordered_set<pair<int, int>, pair_hash>& cluster_pairs =
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// *get_mutable_cluster_pairs(); cluster_pairs.clear(); for (int i = 0; i <
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// kNumClusters; ++i) {
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// cluster_pairs.insert(make_pair(i, i));
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// }
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// cluster_pairs.insert(make_pair(0, 1));
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// EXPECT_TRUE(IsSparsityStructureValid());
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// EXPECT_TRUE(PreconditionerValuesMatch());
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// }
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} // namespace ceres::internal
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