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// 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.
//
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// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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//
// Author: sameeragarwal@google.com (Sameer Agarwal)
#include "ceres/visibility_based_preconditioner.h"
#include <memory>
#include "Eigen/Dense"
#include "ceres/block_random_access_dense_matrix.h"
#include "ceres/block_random_access_sparse_matrix.h"
#include "ceres/block_sparse_matrix.h"
#include "ceres/casts.h"
#include "ceres/file.h"
#include "ceres/internal/eigen.h"
#include "ceres/linear_least_squares_problems.h"
#include "ceres/schur_eliminator.h"
#include "ceres/stringprintf.h"
#include "ceres/test_util.h"
#include "ceres/types.h"
#include "glog/logging.h"
#include "gtest/gtest.h"
namespace ceres::internal {
// TODO(sameeragarwal): Re-enable this test once serialization is
// working again.
// using testing::AssertionResult;
// using testing::AssertionSuccess;
// using testing::AssertionFailure;
// static const double kTolerance = 1e-12;
// class VisibilityBasedPreconditionerTest : public ::testing::Test {
// public:
// static const int kCameraSize = 9;
// protected:
// void SetUp() {
// string input_file = TestFileAbsolutePath("problem-6-1384-000.lsqp");
// std::unique_ptr<LinearLeastSquaresProblem> problem =
// CreateLinearLeastSquaresProblemFromFile(input_file));
// A_.reset(down_cast<BlockSparseMatrix*>(problem->A.release()));
// b_.reset(problem->b.release());
// D_.reset(problem->D.release());
// const CompressedRowBlockStructure* bs =
// CHECK_NOTNULL(A_->block_structure());
// const int num_col_blocks = bs->cols.size();
// num_cols_ = A_->num_cols();
// num_rows_ = A_->num_rows();
// num_eliminate_blocks_ = problem->num_eliminate_blocks;
// num_camera_blocks_ = num_col_blocks - num_eliminate_blocks_;
// options_.elimination_groups.push_back(num_eliminate_blocks_);
// options_.elimination_groups.push_back(
// A_->block_structure()->cols.size() - num_eliminate_blocks_);
// vector<int> blocks(num_col_blocks - num_eliminate_blocks_, 0);
// for (int i = num_eliminate_blocks_; i < num_col_blocks; ++i) {
// blocks[i - num_eliminate_blocks_] = bs->cols[i].size;
// }
// // The input matrix is a real jacobian and fairly poorly
// // conditioned. Setting D to a large constant makes the normal
// // equations better conditioned and makes the tests below better
// // conditioned.
// VectorRef(D_.get(), num_cols_).setConstant(10.0);
// schur_complement_ =
// std::make_unique<BlockRandomAccessDenseMatrix>(blocks);
// Vector rhs(schur_complement_->num_rows());
// std::unique_ptr<SchurEliminatorBase> eliminator;
// LinearSolver::Options eliminator_options;
// eliminator_options.elimination_groups = options_.elimination_groups;
// eliminator_options.num_threads = options_.num_threads;
// eliminator = SchurEliminatorBase::Create(eliminator_options);
// eliminator->Init(num_eliminate_blocks_, bs);
// eliminator->Eliminate(A_.get(), b_.get(), D_.get(),
// schur_complement_.get(), rhs.data());
// }
// AssertionResult IsSparsityStructureValid() {
// preconditioner_->InitStorage(*A_->block_structure());
// const std::unordered_set<pair<int, int>, pair_hash>& cluster_pairs =
// get_cluster_pairs(); const vector<int>& cluster_membership =
// get_cluster_membership();
// for (int i = 0; i < num_camera_blocks_; ++i) {
// for (int j = i; j < num_camera_blocks_; ++j) {
// if (cluster_pairs.count(make_pair(cluster_membership[i],
// cluster_membership[j]))) {
// if (!IsBlockPairInPreconditioner(i, j)) {
// return AssertionFailure()
// << "block pair (" << i << "," << j << "missing";
// }
// } else {
// if (IsBlockPairInPreconditioner(i, j)) {
// return AssertionFailure()
// << "block pair (" << i << "," << j << "should not be present";
// }
// }
// }
// }
// return AssertionSuccess();
// }
// AssertionResult PreconditionerValuesMatch() {
// preconditioner_->Update(*A_, D_.get());
// const std::unordered_set<pair<int, int>, pair_hash>& cluster_pairs =
// get_cluster_pairs(); const BlockRandomAccessSparseMatrix* m = get_m();
// Matrix preconditioner_matrix;
// m->matrix()->ToDenseMatrix(&preconditioner_matrix);
// ConstMatrixRef full_schur_complement(schur_complement_->values(),
// m->num_rows(),
// m->num_rows());
// const int num_clusters = get_num_clusters();
// const int kDiagonalBlockSize =
// kCameraSize * num_camera_blocks_ / num_clusters;
// for (int i = 0; i < num_clusters; ++i) {
// for (int j = i; j < num_clusters; ++j) {
// double diff = 0.0;
// if (cluster_pairs.count(make_pair(i, j))) {
// diff =
// (preconditioner_matrix.block(kDiagonalBlockSize * i,
// kDiagonalBlockSize * j,
// kDiagonalBlockSize,
// kDiagonalBlockSize) -
// full_schur_complement.block(kDiagonalBlockSize * i,
// kDiagonalBlockSize * j,
// kDiagonalBlockSize,
// kDiagonalBlockSize)).norm();
// } else {
// diff = preconditioner_matrix.block(kDiagonalBlockSize * i,
// kDiagonalBlockSize * j,
// kDiagonalBlockSize,
// kDiagonalBlockSize).norm();
// }
// if (diff > kTolerance) {
// return AssertionFailure()
// << "Preconditioner block " << i << " " << j << " differs "
// << "from expected value by " << diff;
// }
// }
// }
// return AssertionSuccess();
// }
// // Accessors
// int get_num_blocks() { return preconditioner_->num_blocks_; }
// int get_num_clusters() { return preconditioner_->num_clusters_; }
// int* get_mutable_num_clusters() { return &preconditioner_->num_clusters_; }
// const vector<int>& get_block_size() {
// return preconditioner_->block_size_; }
// vector<int>* get_mutable_block_size() {
// return &preconditioner_->block_size_; }
// const vector<int>& get_cluster_membership() {
// return preconditioner_->cluster_membership_;
// }
// vector<int>* get_mutable_cluster_membership() {
// return &preconditioner_->cluster_membership_;
// }
// const set<pair<int, int>>& get_block_pairs() {
// return preconditioner_->block_pairs_;
// }
// set<pair<int, int>>* get_mutable_block_pairs() {
// return &preconditioner_->block_pairs_;
// }
// const std::unordered_set<pair<int, int>, pair_hash>& get_cluster_pairs() {
// return preconditioner_->cluster_pairs_;
// }
// std::unordered_set<pair<int, int>, pair_hash>* get_mutable_cluster_pairs()
// {
// return &preconditioner_->cluster_pairs_;
// }
// bool IsBlockPairInPreconditioner(const int block1, const int block2) {
// return preconditioner_->IsBlockPairInPreconditioner(block1, block2);
// }
// bool IsBlockPairOffDiagonal(const int block1, const int block2) {
// return preconditioner_->IsBlockPairOffDiagonal(block1, block2);
// }
// const BlockRandomAccessSparseMatrix* get_m() {
// return preconditioner_->m_.get();
// }
// int num_rows_;
// int num_cols_;
// int num_eliminate_blocks_;
// int num_camera_blocks_;
// std::unique_ptr<BlockSparseMatrix> A_;
// std::unique_ptr<double[]> b_;
// std::unique_ptr<double[]> D_;
// Preconditioner::Options options_;
// std::unique_ptr<VisibilityBasedPreconditioner> preconditioner_;
// std::unique_ptr<BlockRandomAccessDenseMatrix> schur_complement_;
// };
// TEST_F(VisibilityBasedPreconditionerTest, OneClusterClusterJacobi) {
// options_.type = CLUSTER_JACOBI;
// preconditioner_ =
// std::make_unique<VisibilityBasedPreconditioner>(
// *A_->block_structure(), options_);
// // Override the clustering to be a single clustering containing all
// // the cameras.
// vector<int>& cluster_membership = *get_mutable_cluster_membership();
// for (int i = 0; i < num_camera_blocks_; ++i) {
// cluster_membership[i] = 0;
// }
// *get_mutable_num_clusters() = 1;
// std::unordered_set<pair<int, int>, pair_hash>& cluster_pairs =
// *get_mutable_cluster_pairs(); cluster_pairs.clear();
// cluster_pairs.insert(make_pair(0, 0));
// EXPECT_TRUE(IsSparsityStructureValid());
// EXPECT_TRUE(PreconditionerValuesMatch());
// // Multiplication by the inverse of the preconditioner.
// const int num_rows = schur_complement_->num_rows();
// ConstMatrixRef full_schur_complement(schur_complement_->values(),
// num_rows,
// num_rows);
// Vector x(num_rows);
// Vector y(num_rows);
// Vector z(num_rows);
// for (int i = 0; i < num_rows; ++i) {
// x.setZero();
// y.setZero();
// z.setZero();
// x[i] = 1.0;
// preconditioner_->RightMultiplyAndAccumulate(x.data(), y.data());
// z = full_schur_complement
// .selfadjointView<Eigen::Upper>()
// .llt().solve(x);
// double max_relative_difference =
// ((y - z).array() / z.array()).matrix().lpNorm<Eigen::Infinity>();
// EXPECT_NEAR(max_relative_difference, 0.0, kTolerance);
// }
// }
// TEST_F(VisibilityBasedPreconditionerTest, ClusterJacobi) {
// options_.type = CLUSTER_JACOBI;
// preconditioner_ =
// std::make_unique<VisibilityBasedPreconditioner>(*A_->block_structure(),
// options_);
// // Override the clustering to be equal number of cameras.
// vector<int>& cluster_membership = *get_mutable_cluster_membership();
// cluster_membership.resize(num_camera_blocks_);
// static const int kNumClusters = 3;
// for (int i = 0; i < num_camera_blocks_; ++i) {
// cluster_membership[i] = (i * kNumClusters) / num_camera_blocks_;
// }
// *get_mutable_num_clusters() = kNumClusters;
// std::unordered_set<pair<int, int>, pair_hash>& cluster_pairs =
// *get_mutable_cluster_pairs(); cluster_pairs.clear(); for (int i = 0; i <
// kNumClusters; ++i) {
// cluster_pairs.insert(make_pair(i, i));
// }
// EXPECT_TRUE(IsSparsityStructureValid());
// EXPECT_TRUE(PreconditionerValuesMatch());
// }
// TEST_F(VisibilityBasedPreconditionerTest, ClusterTridiagonal) {
// options_.type = CLUSTER_TRIDIAGONAL;
// preconditioner_ =
// std::make_unique<VisibilityBasedPreconditioner>(*A_->block_structure(),
// options_);
// static const int kNumClusters = 3;
// // Override the clustering to be 3 clusters.
// vector<int>& cluster_membership = *get_mutable_cluster_membership();
// cluster_membership.resize(num_camera_blocks_);
// for (int i = 0; i < num_camera_blocks_; ++i) {
// cluster_membership[i] = (i * kNumClusters) / num_camera_blocks_;
// }
// *get_mutable_num_clusters() = kNumClusters;
// // Spanning forest has structure 0-1 2
// std::unordered_set<pair<int, int>, pair_hash>& cluster_pairs =
// *get_mutable_cluster_pairs(); cluster_pairs.clear(); for (int i = 0; i <
// kNumClusters; ++i) {
// cluster_pairs.insert(make_pair(i, i));
// }
// cluster_pairs.insert(make_pair(0, 1));
// EXPECT_TRUE(IsSparsityStructureValid());
// EXPECT_TRUE(PreconditionerValuesMatch());
// }
} // namespace ceres::internal