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MYNT-EYE-S-SDK/3rdparty/ceres-solver-1.11.0/internal/ceres/compressed_row_sparse_matrix_test.cc
John Zhao 6773d8eb7a feat(3rdparty): add eigen and ceres
2019-01-03 16:25:18 +08:00

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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.
//
// 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/compressed_row_sparse_matrix.h"
#include <numeric>
#include "ceres/casts.h"
#include "ceres/crs_matrix.h"
#include "ceres/cxsparse.h"
#include "ceres/internal/eigen.h"
#include "ceres/internal/scoped_ptr.h"
#include "ceres/linear_least_squares_problems.h"
#include "ceres/random.h"
#include "ceres/triplet_sparse_matrix.h"
#include "glog/logging.h"
#include "gtest/gtest.h"
namespace ceres {
namespace internal {
using std::vector;
void CompareMatrices(const SparseMatrix* a, const SparseMatrix* b) {
EXPECT_EQ(a->num_rows(), b->num_rows());
EXPECT_EQ(a->num_cols(), b->num_cols());
int num_rows = a->num_rows();
int num_cols = a->num_cols();
for (int i = 0; i < num_cols; ++i) {
Vector x = Vector::Zero(num_cols);
x(i) = 1.0;
Vector y_a = Vector::Zero(num_rows);
Vector y_b = Vector::Zero(num_rows);
a->RightMultiply(x.data(), y_a.data());
b->RightMultiply(x.data(), y_b.data());
EXPECT_EQ((y_a - y_b).norm(), 0);
}
}
class CompressedRowSparseMatrixTest : public ::testing::Test {
protected :
virtual void SetUp() {
scoped_ptr<LinearLeastSquaresProblem> problem(
CreateLinearLeastSquaresProblemFromId(1));
CHECK_NOTNULL(problem.get());
tsm.reset(down_cast<TripletSparseMatrix*>(problem->A.release()));
crsm.reset(new CompressedRowSparseMatrix(*tsm));
num_rows = tsm->num_rows();
num_cols = tsm->num_cols();
vector<int>* row_blocks = crsm->mutable_row_blocks();
row_blocks->resize(num_rows);
std::fill(row_blocks->begin(), row_blocks->end(), 1);
vector<int>* col_blocks = crsm->mutable_col_blocks();
col_blocks->resize(num_cols);
std::fill(col_blocks->begin(), col_blocks->end(), 1);
}
int num_rows;
int num_cols;
scoped_ptr<TripletSparseMatrix> tsm;
scoped_ptr<CompressedRowSparseMatrix> crsm;
};
TEST_F(CompressedRowSparseMatrixTest, RightMultiply) {
CompareMatrices(tsm.get(), crsm.get());
}
TEST_F(CompressedRowSparseMatrixTest, LeftMultiply) {
for (int i = 0; i < num_rows; ++i) {
Vector a = Vector::Zero(num_rows);
a(i) = 1.0;
Vector b1 = Vector::Zero(num_cols);
Vector b2 = Vector::Zero(num_cols);
tsm->LeftMultiply(a.data(), b1.data());
crsm->LeftMultiply(a.data(), b2.data());
EXPECT_EQ((b1 - b2).norm(), 0);
}
}
TEST_F(CompressedRowSparseMatrixTest, ColumnNorm) {
Vector b1 = Vector::Zero(num_cols);
Vector b2 = Vector::Zero(num_cols);
tsm->SquaredColumnNorm(b1.data());
crsm->SquaredColumnNorm(b2.data());
EXPECT_EQ((b1 - b2).norm(), 0);
}
TEST_F(CompressedRowSparseMatrixTest, Scale) {
Vector scale(num_cols);
for (int i = 0; i < num_cols; ++i) {
scale(i) = i + 1;
}
tsm->ScaleColumns(scale.data());
crsm->ScaleColumns(scale.data());
CompareMatrices(tsm.get(), crsm.get());
}
TEST_F(CompressedRowSparseMatrixTest, DeleteRows) {
// Clear the row and column blocks as these are purely scalar tests.
crsm->mutable_row_blocks()->clear();
crsm->mutable_col_blocks()->clear();
for (int i = 0; i < num_rows; ++i) {
tsm->Resize(num_rows - i, num_cols);
crsm->DeleteRows(crsm->num_rows() - tsm->num_rows());
CompareMatrices(tsm.get(), crsm.get());
}
}
TEST_F(CompressedRowSparseMatrixTest, AppendRows) {
// Clear the row and column blocks as these are purely scalar tests.
crsm->mutable_row_blocks()->clear();
crsm->mutable_col_blocks()->clear();
for (int i = 0; i < num_rows; ++i) {
TripletSparseMatrix tsm_appendage(*tsm);
tsm_appendage.Resize(i, num_cols);
tsm->AppendRows(tsm_appendage);
CompressedRowSparseMatrix crsm_appendage(tsm_appendage);
crsm->AppendRows(crsm_appendage);
CompareMatrices(tsm.get(), crsm.get());
}
}
TEST_F(CompressedRowSparseMatrixTest, AppendAndDeleteBlockDiagonalMatrix) {
int num_diagonal_rows = crsm->num_cols();
scoped_array<double> diagonal(new double[num_diagonal_rows]);
for (int i = 0; i < num_diagonal_rows; ++i) {
diagonal[i] = i;
}
vector<int> row_and_column_blocks;
row_and_column_blocks.push_back(1);
row_and_column_blocks.push_back(2);
row_and_column_blocks.push_back(2);
const vector<int> pre_row_blocks = crsm->row_blocks();
const vector<int> pre_col_blocks = crsm->col_blocks();
scoped_ptr<CompressedRowSparseMatrix> appendage(
CompressedRowSparseMatrix::CreateBlockDiagonalMatrix(
diagonal.get(), row_and_column_blocks));
LOG(INFO) << appendage->row_blocks().size();
crsm->AppendRows(*appendage);
const vector<int> post_row_blocks = crsm->row_blocks();
const vector<int> post_col_blocks = crsm->col_blocks();
vector<int> expected_row_blocks = pre_row_blocks;
expected_row_blocks.insert(expected_row_blocks.end(),
row_and_column_blocks.begin(),
row_and_column_blocks.end());
vector<int> expected_col_blocks = pre_col_blocks;
EXPECT_EQ(expected_row_blocks, crsm->row_blocks());
EXPECT_EQ(expected_col_blocks, crsm->col_blocks());
crsm->DeleteRows(num_diagonal_rows);
EXPECT_EQ(crsm->row_blocks(), pre_row_blocks);
EXPECT_EQ(crsm->col_blocks(), pre_col_blocks);
}
TEST_F(CompressedRowSparseMatrixTest, ToDenseMatrix) {
Matrix tsm_dense;
Matrix crsm_dense;
tsm->ToDenseMatrix(&tsm_dense);
crsm->ToDenseMatrix(&crsm_dense);
EXPECT_EQ((tsm_dense - crsm_dense).norm(), 0.0);
}
TEST_F(CompressedRowSparseMatrixTest, ToCRSMatrix) {
CRSMatrix crs_matrix;
crsm->ToCRSMatrix(&crs_matrix);
EXPECT_EQ(crsm->num_rows(), crs_matrix.num_rows);
EXPECT_EQ(crsm->num_cols(), crs_matrix.num_cols);
EXPECT_EQ(crsm->num_rows() + 1, crs_matrix.rows.size());
EXPECT_EQ(crsm->num_nonzeros(), crs_matrix.cols.size());
EXPECT_EQ(crsm->num_nonzeros(), crs_matrix.values.size());
for (int i = 0; i < crsm->num_rows() + 1; ++i) {
EXPECT_EQ(crsm->rows()[i], crs_matrix.rows[i]);
}
for (int i = 0; i < crsm->num_nonzeros(); ++i) {
EXPECT_EQ(crsm->cols()[i], crs_matrix.cols[i]);
EXPECT_EQ(crsm->values()[i], crs_matrix.values[i]);
}
}
TEST(CompressedRowSparseMatrix, CreateBlockDiagonalMatrix) {
vector<int> blocks;
blocks.push_back(1);
blocks.push_back(2);
blocks.push_back(2);
Vector diagonal(5);
for (int i = 0; i < 5; ++i) {
diagonal(i) = i + 1;
}
scoped_ptr<CompressedRowSparseMatrix> matrix(
CompressedRowSparseMatrix::CreateBlockDiagonalMatrix(
diagonal.data(), blocks));
EXPECT_EQ(matrix->num_rows(), 5);
EXPECT_EQ(matrix->num_cols(), 5);
EXPECT_EQ(matrix->num_nonzeros(), 9);
EXPECT_EQ(blocks, matrix->row_blocks());
EXPECT_EQ(blocks, matrix->col_blocks());
Vector x(5);
Vector y(5);
x.setOnes();
y.setZero();
matrix->RightMultiply(x.data(), y.data());
for (int i = 0; i < diagonal.size(); ++i) {
EXPECT_EQ(y[i], diagonal[i]);
}
y.setZero();
matrix->LeftMultiply(x.data(), y.data());
for (int i = 0; i < diagonal.size(); ++i) {
EXPECT_EQ(y[i], diagonal[i]);
}
Matrix dense;
matrix->ToDenseMatrix(&dense);
EXPECT_EQ((dense.diagonal() - diagonal).norm(), 0.0);
}
class SolveLowerTriangularTest : public ::testing::Test {
protected:
void SetUp() {
matrix_.reset(new CompressedRowSparseMatrix(4, 4, 7));
int* rows = matrix_->mutable_rows();
int* cols = matrix_->mutable_cols();
double* values = matrix_->mutable_values();
rows[0] = 0;
cols[0] = 0;
values[0] = 0.50754;
rows[1] = 1;
cols[1] = 1;
values[1] = 0.80483;
rows[2] = 2;
cols[2] = 1;
values[2] = 0.14120;
cols[3] = 2;
values[3] = 0.3;
rows[3] = 4;
cols[4] = 0;
values[4] = 0.77696;
cols[5] = 1;
values[5] = 0.41860;
cols[6] = 3;
values[6] = 0.88979;
rows[4] = 7;
}
scoped_ptr<CompressedRowSparseMatrix> matrix_;
};
TEST_F(SolveLowerTriangularTest, SolveInPlace) {
double rhs_and_solution[] = {1.0, 1.0, 2.0, 2.0};
double expected[] = {1.970288, 1.242498, 6.081864, -0.057255};
matrix_->SolveLowerTriangularInPlace(rhs_and_solution);
for (int i = 0; i < 4; ++i) {
EXPECT_NEAR(rhs_and_solution[i], expected[i], 1e-4) << i;
}
}
TEST_F(SolveLowerTriangularTest, TransposeSolveInPlace) {
double rhs_and_solution[] = {1.0, 1.0, 2.0, 2.0};
const double expected[] = { -1.4706, -1.0962, 6.6667, 2.2477};
matrix_->SolveLowerTriangularTransposeInPlace(rhs_and_solution);
for (int i = 0; i < 4; ++i) {
EXPECT_NEAR(rhs_and_solution[i], expected[i], 1e-4) << i;
}
}
TEST(CompressedRowSparseMatrix, Transpose) {
// 0 1 0 2 3 0
// 4 6 7 0 0 8
// 9 10 0 11 12 0
// 13 0 14 15 9 0
// 0 16 17 0 0 0
// Block structure:
// A A A A B B
// A A A A B B
// A A A A B B
// C C C C D D
// C C C C D D
// C C C C D D
CompressedRowSparseMatrix matrix(5, 6, 30);
int* rows = matrix.mutable_rows();
int* cols = matrix.mutable_cols();
double* values = matrix.mutable_values();
matrix.mutable_row_blocks()->push_back(3);
matrix.mutable_row_blocks()->push_back(3);
matrix.mutable_col_blocks()->push_back(4);
matrix.mutable_col_blocks()->push_back(2);
rows[0] = 0;
cols[0] = 1;
cols[1] = 3;
cols[2] = 4;
rows[1] = 3;
cols[3] = 0;
cols[4] = 1;
cols[5] = 2;
cols[6] = 5;
rows[2] = 7;
cols[7] = 0;
cols[8] = 1;
cols[9] = 3;
cols[10] = 4;
rows[3] = 11;
cols[11] = 0;
cols[12] = 2;
cols[13] = 3;
cols[14] = 4;
rows[4] = 15;
cols[15] = 1;
cols[16] = 2;
rows[5] = 17;
std::copy(values, values + 17, cols);
scoped_ptr<CompressedRowSparseMatrix> transpose(matrix.Transpose());
ASSERT_EQ(transpose->row_blocks().size(), matrix.col_blocks().size());
for (int i = 0; i < transpose->row_blocks().size(); ++i) {
EXPECT_EQ(transpose->row_blocks()[i], matrix.col_blocks()[i]);
}
ASSERT_EQ(transpose->col_blocks().size(), matrix.row_blocks().size());
for (int i = 0; i < transpose->col_blocks().size(); ++i) {
EXPECT_EQ(transpose->col_blocks()[i], matrix.row_blocks()[i]);
}
Matrix dense_matrix;
matrix.ToDenseMatrix(&dense_matrix);
Matrix dense_transpose;
transpose->ToDenseMatrix(&dense_transpose);
EXPECT_NEAR((dense_matrix - dense_transpose.transpose()).norm(), 0.0, 1e-14);
}
#ifndef CERES_NO_CXSPARSE
struct RandomMatrixOptions {
int num_row_blocks;
int min_row_block_size;
int max_row_block_size;
int num_col_blocks;
int min_col_block_size;
int max_col_block_size;
double block_density;
};
CompressedRowSparseMatrix* CreateRandomCompressedRowSparseMatrix(
const RandomMatrixOptions& options) {
vector<int> row_blocks;
for (int i = 0; i < options.num_row_blocks; ++i) {
const int delta_block_size =
Uniform(options.max_row_block_size - options.min_row_block_size);
row_blocks.push_back(options.min_row_block_size + delta_block_size);
}
vector<int> col_blocks;
for (int i = 0; i < options.num_col_blocks; ++i) {
const int delta_block_size =
Uniform(options.max_col_block_size - options.min_col_block_size);
col_blocks.push_back(options.min_col_block_size + delta_block_size);
}
vector<int> rows;
vector<int> cols;
vector<double> values;
while (values.size() == 0) {
int row_block_begin = 0;
for (int r = 0; r < options.num_row_blocks; ++r) {
int col_block_begin = 0;
for (int c = 0; c < options.num_col_blocks; ++c) {
if (RandDouble() <= options.block_density) {
for (int i = 0; i < row_blocks[r]; ++i) {
for (int j = 0; j < col_blocks[c]; ++j) {
rows.push_back(row_block_begin + i);
cols.push_back(col_block_begin + j);
values.push_back(RandNormal());
}
}
}
col_block_begin += col_blocks[c];
}
row_block_begin += row_blocks[r];
}
}
const int num_rows = std::accumulate(row_blocks.begin(), row_blocks.end(), 0);
const int num_cols = std::accumulate(col_blocks.begin(), col_blocks.end(), 0);
const int num_nonzeros = values.size();
TripletSparseMatrix tsm(num_rows, num_cols, num_nonzeros);
std::copy(rows.begin(), rows.end(), tsm.mutable_rows());
std::copy(cols.begin(), cols.end(), tsm.mutable_cols());
std::copy(values.begin(), values.end(), tsm.mutable_values());
tsm.set_num_nonzeros(num_nonzeros);
CompressedRowSparseMatrix* matrix = new CompressedRowSparseMatrix(tsm);
(*matrix->mutable_row_blocks()) = row_blocks;
(*matrix->mutable_col_blocks()) = col_blocks;
return matrix;
}
void ToDenseMatrix(const cs_di* matrix, Matrix* dense_matrix) {
dense_matrix->resize(matrix->m, matrix->n);
dense_matrix->setZero();
for (int c = 0; c < matrix->n; ++c) {
for (int idx = matrix->p[c]; idx < matrix->p[c + 1]; ++idx) {
const int r = matrix->i[idx];
(*dense_matrix)(r, c) = matrix->x[idx];
}
}
}
TEST(CompressedRowSparseMatrix, ComputeOuterProduct) {
// "Randomly generated seed."
SetRandomState(29823);
int kMaxNumRowBlocks = 10;
int kMaxNumColBlocks = 10;
int kNumTrials = 10;
CXSparse cxsparse;
const double kTolerance = 1e-18;
// Create a random matrix, compute its outer product using CXSParse
// and ComputeOuterProduct. Convert both matrices to dense matrices
// and compare their upper triangular parts. They should be within
// kTolerance of each other.
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) {
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<CompressedRowSparseMatrix> matrix(
CreateRandomCompressedRowSparseMatrix(options));
cs_di cs_matrix_transpose =
cxsparse.CreateSparseMatrixTransposeView(matrix.get());
cs_di* cs_matrix = cxsparse.TransposeMatrix(&cs_matrix_transpose);
cs_di* expected_outer_product =
cxsparse.MatrixMatrixMultiply(&cs_matrix_transpose, cs_matrix);
vector<int> program;
scoped_ptr<CompressedRowSparseMatrix> outer_product(
CompressedRowSparseMatrix::CreateOuterProductMatrixAndProgram(
*matrix, &program));
CompressedRowSparseMatrix::ComputeOuterProduct(*matrix,
program,
outer_product.get());
cs_di actual_outer_product =
cxsparse.CreateSparseMatrixTransposeView(outer_product.get());
ASSERT_EQ(actual_outer_product.m, actual_outer_product.n);
ASSERT_EQ(expected_outer_product->m, expected_outer_product->n);
ASSERT_EQ(actual_outer_product.m, expected_outer_product->m);
Matrix actual_matrix;
Matrix expected_matrix;
ToDenseMatrix(expected_outer_product, &expected_matrix);
expected_matrix.triangularView<Eigen::StrictlyLower>().setZero();
ToDenseMatrix(&actual_outer_product, &actual_matrix);
const double diff_norm =
(actual_matrix - expected_matrix).norm() / expected_matrix.norm();
ASSERT_NEAR(diff_norm, 0.0, kTolerance)
<< "expected: \n"
<< expected_matrix
<< "\nactual: \n"
<< actual_matrix;
cxsparse.Free(cs_matrix);
cxsparse.Free(expected_outer_product);
}
}
}
}
#endif // CERES_NO_CXSPARSE
} // namespace internal
} // namespace ceres
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