mirror of
https://github.com/ceres-solver/ceres-solver.git
synced 2026-08-29 16:40:38 +08:00
Sameer Agarwal
b5b394c738
Add a class that given a block sparse matrix m will compute the product m'*m efficiently. This code is refactoring and cleanup of the code in CompressedRowSparseMatrix devoted to computing the inner product. In that class, the code is mistakenly said to be computing the outer product. It is also devoted to computing the inner product of a CompressedRowSparseMatrix with itself. This code works with BlockSparseMatrix objects instead, which are simpler to deal with as they are better structured to handle block sparse matrices. Change-Id: I920fee1a396bb0fcae9e6f7e46a308c7391d21aa
237 lines
9.9 KiB
C++
237 lines
9.9 KiB
C++
// Ceres Solver - A fast non-linear least squares minimizer
|
|
// Copyright 2017 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
|
|
// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
|
|
// 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
|
|
// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
|
|
// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
|
|
// POSSIBILITY OF SUCH DAMAGE.
|
|
//
|
|
// Author: sameeragarwal@google.com (Sameer Agarwal)
|
|
|
|
#include "ceres/inner_product_computer.h"
|
|
|
|
#include <numeric>
|
|
#include "ceres/block_sparse_matrix.h"
|
|
#include "ceres/internal/eigen.h"
|
|
#include "ceres/internal/scoped_ptr.h"
|
|
#include "ceres/random.h"
|
|
#include "ceres/triplet_sparse_matrix.h"
|
|
#include "glog/logging.h"
|
|
#include "gtest/gtest.h"
|
|
|
|
#include "Eigen/SparseCore"
|
|
|
|
namespace ceres {
|
|
namespace internal {
|
|
|
|
template <enum Eigen::UpLoType T>
|
|
void CompareTriangularPartOfMatrices(const Matrix& expected,
|
|
const Matrix& actual) {
|
|
EXPECT_EQ(actual.rows(), actual.cols());
|
|
EXPECT_EQ(expected.rows(), expected.cols());
|
|
EXPECT_EQ(actual.rows(), expected.rows());
|
|
|
|
const Matrix expected_t = expected.triangularView<T>();
|
|
const Matrix actual_t = actual.triangularView<T>();
|
|
|
|
// TODO(sameeragarwal): Foo
|
|
CHECK_LE((expected_t - actual_t).norm() / actual_t.norm(),
|
|
100 * std::numeric_limits<double>::epsilon())
|
|
<< "expected: \n"
|
|
<< expected_t << "\nactual: \n"
|
|
<< actual_t;
|
|
}
|
|
|
|
#define COMPUTE_AND_COMPARE \
|
|
{ \
|
|
inner_product_computer->Compute(); \
|
|
CompressedRowSparseMatrix* actual_product_crsm = \
|
|
inner_product_computer->mutable_result(); \
|
|
Matrix actual_inner_product = \
|
|
Eigen::MappedSparseMatrix<double, Eigen::ColMajor>( \
|
|
actual_product_crsm->num_rows(), \
|
|
actual_product_crsm->num_rows(), \
|
|
actual_product_crsm->num_nonzeros(), \
|
|
actual_product_crsm->mutable_rows(), \
|
|
actual_product_crsm->mutable_cols(), \
|
|
actual_product_crsm->mutable_values()); \
|
|
(actual_product_crsm->storage_type() == \
|
|
CompressedRowSparseMatrix::LOWER_TRIANGULAR) \
|
|
? CompareTriangularPartOfMatrices<Eigen::Upper>( \
|
|
expected_inner_product, actual_inner_product) \
|
|
: CompareTriangularPartOfMatrices<Eigen::Lower>( \
|
|
expected_inner_product, actual_inner_product); \
|
|
}
|
|
|
|
|
|
TEST(InnerProductComputer, NormalOperation) {
|
|
// "Randomly generated seed."
|
|
SetRandomState(29823);
|
|
const int kMaxNumRowBlocks = 10;
|
|
const int kMaxNumColBlocks = 10;
|
|
const int kNumTrials = 10;
|
|
|
|
// Create a random matrix, compute its outer product using Eigen and
|
|
// ComputeOuterProduct. Convert both matrices to dense matrices and
|
|
// compare their upper triangular parts.
|
|
for (int num_row_blocks = 1; num_row_blocks < kMaxNumRowBlocks;
|
|
++num_row_blocks) {
|
|
for (int num_col_blocks = 1; num_col_blocks < kMaxNumColBlocks;
|
|
++num_col_blocks) {
|
|
for (int trial = 0; trial < kNumTrials; ++trial) {
|
|
BlockSparseMatrix::RandomMatrixOptions options;
|
|
options.num_row_blocks = num_row_blocks;
|
|
options.num_col_blocks = num_col_blocks;
|
|
options.min_row_block_size = 1;
|
|
options.max_row_block_size = 5;
|
|
options.min_col_block_size = 1;
|
|
options.max_col_block_size = 10;
|
|
options.block_density = std::max(0.1, RandDouble());
|
|
|
|
VLOG(2) << "num row blocks: " << options.num_row_blocks;
|
|
VLOG(2) << "num col blocks: " << options.num_col_blocks;
|
|
VLOG(2) << "min row block size: " << options.min_row_block_size;
|
|
VLOG(2) << "max row block size: " << options.max_row_block_size;
|
|
VLOG(2) << "min col block size: " << options.min_col_block_size;
|
|
VLOG(2) << "max col block size: " << options.max_col_block_size;
|
|
VLOG(2) << "block density: " << options.block_density;
|
|
|
|
scoped_ptr<BlockSparseMatrix> random_matrix(
|
|
BlockSparseMatrix::CreateRandomMatrix(options));
|
|
|
|
TripletSparseMatrix tsm(random_matrix->num_rows(),
|
|
random_matrix->num_cols(),
|
|
random_matrix->num_nonzeros());
|
|
random_matrix->ToTripletSparseMatrix(&tsm);
|
|
std::vector<Eigen::Triplet<double> > triplets;
|
|
for (int i = 0; i < tsm.num_nonzeros(); ++i) {
|
|
triplets.push_back(Eigen::Triplet<double>(
|
|
tsm.rows()[i], tsm.cols()[i], tsm.values()[i]));
|
|
}
|
|
Eigen::SparseMatrix<double> eigen_random_matrix(
|
|
random_matrix->num_rows(), random_matrix->num_cols());
|
|
eigen_random_matrix.setFromTriplets(triplets.begin(), triplets.end());
|
|
Matrix expected_inner_product =
|
|
eigen_random_matrix.transpose() * eigen_random_matrix;
|
|
|
|
scoped_ptr<InnerProductComputer> inner_product_computer;
|
|
|
|
inner_product_computer.reset(InnerProductComputer::Create(
|
|
*random_matrix, CompressedRowSparseMatrix::LOWER_TRIANGULAR));
|
|
COMPUTE_AND_COMPARE;
|
|
inner_product_computer.reset(InnerProductComputer::Create(
|
|
*random_matrix, CompressedRowSparseMatrix::UPPER_TRIANGULAR));
|
|
COMPUTE_AND_COMPARE;
|
|
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
|
|
TEST(InnerProductComputer, SubMatrix) {
|
|
// "Randomly generated seed."
|
|
SetRandomState(29823);
|
|
const int kNumRowBlocks = 10;
|
|
const int kNumColBlocks = 20;
|
|
const int kNumTrials = 5;
|
|
|
|
// Create a random matrix, compute its outer product using Eigen and
|
|
// ComputeInnerProductComputer. Convert both matrices to dense matrices and
|
|
// compare their upper triangular parts.
|
|
for (int trial = 0; trial < kNumTrials; ++trial) {
|
|
BlockSparseMatrix::RandomMatrixOptions options;
|
|
options.num_row_blocks = kNumRowBlocks;
|
|
options.num_col_blocks = kNumColBlocks;
|
|
options.min_row_block_size = 1;
|
|
options.max_row_block_size = 5;
|
|
options.min_col_block_size = 1;
|
|
options.max_col_block_size = 10;
|
|
options.block_density = std::max(0.1, RandDouble());
|
|
|
|
VLOG(2) << "num row blocks: " << options.num_row_blocks;
|
|
VLOG(2) << "num col blocks: " << options.num_col_blocks;
|
|
VLOG(2) << "min row block size: " << options.min_row_block_size;
|
|
VLOG(2) << "max row block size: " << options.max_row_block_size;
|
|
VLOG(2) << "min col block size: " << options.min_col_block_size;
|
|
VLOG(2) << "max col block size: " << options.max_col_block_size;
|
|
VLOG(2) << "block density: " << options.block_density;
|
|
|
|
scoped_ptr<BlockSparseMatrix> random_matrix(
|
|
BlockSparseMatrix::CreateRandomMatrix(options));
|
|
|
|
const std::vector<CompressedRow>& row_blocks =
|
|
random_matrix->block_structure()->rows;
|
|
const int num_row_blocks = row_blocks.size();
|
|
|
|
for (int start_row_block = 0; start_row_block < num_row_blocks - 1;
|
|
++start_row_block) {
|
|
for (int end_row_block = start_row_block + 1;
|
|
end_row_block < num_row_blocks;
|
|
++end_row_block) {
|
|
const int start_row = row_blocks[start_row_block].block.position;
|
|
const int end_row = row_blocks[end_row_block].block.position;
|
|
|
|
TripletSparseMatrix tsm(random_matrix->num_rows(),
|
|
random_matrix->num_cols(),
|
|
random_matrix->num_nonzeros());
|
|
random_matrix->ToTripletSparseMatrix(&tsm);
|
|
std::vector<Eigen::Triplet<double> > triplets;
|
|
for (int i = 0; i < tsm.num_nonzeros(); ++i) {
|
|
if (tsm.rows()[i] >= start_row && tsm.rows()[i] < end_row) {
|
|
triplets.push_back(Eigen::Triplet<double>(
|
|
tsm.rows()[i], tsm.cols()[i], tsm.values()[i]));
|
|
}
|
|
}
|
|
|
|
Eigen::SparseMatrix<double> eigen_random_matrix(
|
|
random_matrix->num_rows(), random_matrix->num_cols());
|
|
eigen_random_matrix.setFromTriplets(triplets.begin(), triplets.end());
|
|
|
|
Matrix expected_inner_product =
|
|
eigen_random_matrix.transpose() * eigen_random_matrix;
|
|
|
|
scoped_ptr<InnerProductComputer> inner_product_computer;
|
|
inner_product_computer.reset(InnerProductComputer::Create(
|
|
*random_matrix,
|
|
start_row_block,
|
|
end_row_block,
|
|
CompressedRowSparseMatrix::LOWER_TRIANGULAR));
|
|
COMPUTE_AND_COMPARE;
|
|
inner_product_computer.reset(InnerProductComputer::Create(
|
|
*random_matrix,
|
|
start_row_block,
|
|
end_row_block,
|
|
CompressedRowSparseMatrix::UPPER_TRIANGULAR));
|
|
COMPUTE_AND_COMPARE;
|
|
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
#undef COMPUTE_AND_COMPARE
|
|
|
|
} // namespace internal
|
|
} // namespace ceres
|