internal/ceres/compressed_col_sparse_matrix_utils_test.cc - ceres-solver - Git at Google

 // 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 <algorithm>
 #include <numeric>
 #include "ceres/compressed_col_sparse_matrix_utils.h"
 #include "ceres/internal/port.h"
 #include "ceres/triplet_sparse_matrix.h"
 #include "glog/logging.h"
 #include "gtest/gtest.h"
 #include "Eigen/SparseCore"

 namespace ceres {
 namespace internal {

 using std::vector;

 TEST(_, BlockPermutationToScalarPermutation) {
   vector<int> blocks;
   //  Block structure
   //  0  --1-  ---2---  ---3---  4
   // [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
   blocks.push_back(1);
   blocks.push_back(2);
   blocks.push_back(3);
   blocks.push_back(3);
   blocks.push_back(1);

   // Block ordering
   // [1, 0, 2, 4, 5]
   vector<int> block_ordering;
   block_ordering.push_back(1);
   block_ordering.push_back(0);
   block_ordering.push_back(2);
   block_ordering.push_back(4);
   block_ordering.push_back(3);

   // Expected ordering
   // [1, 2, 0, 3, 4, 5, 9, 6, 7, 8]
   vector<int> expected_scalar_ordering;
   expected_scalar_ordering.push_back(1);
   expected_scalar_ordering.push_back(2);
   expected_scalar_ordering.push_back(0);
   expected_scalar_ordering.push_back(3);
   expected_scalar_ordering.push_back(4);
   expected_scalar_ordering.push_back(5);
   expected_scalar_ordering.push_back(9);
   expected_scalar_ordering.push_back(6);
   expected_scalar_ordering.push_back(7);
   expected_scalar_ordering.push_back(8);

   vector<int> scalar_ordering;
   BlockOrderingToScalarOrdering(blocks,
                                 block_ordering,
                                 &scalar_ordering);
   EXPECT_EQ(scalar_ordering.size(), expected_scalar_ordering.size());
   for (int i = 0; i < expected_scalar_ordering.size(); ++i) {
     EXPECT_EQ(scalar_ordering[i], expected_scalar_ordering[i]);
   }
 }

 static void FillBlock(const vector<int>& row_blocks,
                       const vector<int>& col_blocks,
                       const int row_block_id,
                       const int col_block_id,
                       vector<Eigen::Triplet<double>>* triplets) {
   const int row_offset = std::accumulate(&row_blocks[0], &row_blocks[row_block_id], 0);
   const int col_offset = std::accumulate(&col_blocks[0], &col_blocks[col_block_id], 0);
   for (int r = 0; r < row_blocks[row_block_id]; ++r) {
     for (int c = 0; c < col_blocks[col_block_id]; ++c) {
       triplets->push_back(Eigen::Triplet<double>(row_offset + r, col_offset + c, 1.0));
     }
   }
 }

 TEST(_, ScalarMatrixToBlockMatrix) {
   // Block sparsity.
   //
   //     [1 2 3 2]
   // [1]  x   x
   // [2]    x   x
   // [2]  x x
   // num_nonzeros = 1 + 3 + 4 + 4 + 1 + 2 = 15


   vector<int> col_blocks;
   col_blocks.push_back(1);
   col_blocks.push_back(2);
   col_blocks.push_back(3);
   col_blocks.push_back(2);

   vector<int> row_blocks;
   row_blocks.push_back(1);
   row_blocks.push_back(2);
   row_blocks.push_back(2);

   const int num_rows = std::accumulate(row_blocks.begin(), row_blocks.end(), 0.0);
   const int num_cols = std::accumulate(col_blocks.begin(), col_blocks.end(), 0.0);

   vector<Eigen::Triplet<double>> triplets;
   FillBlock(row_blocks, col_blocks, 0, 0, &triplets);
   FillBlock(row_blocks, col_blocks, 2, 0, &triplets);
   FillBlock(row_blocks, col_blocks, 1, 1, &triplets);
   FillBlock(row_blocks, col_blocks, 2, 1, &triplets);
   FillBlock(row_blocks, col_blocks, 0, 2, &triplets);
   FillBlock(row_blocks, col_blocks, 1, 3, &triplets);
   Eigen::SparseMatrix<double> sparse_matrix(num_rows, num_cols);
   sparse_matrix.setFromTriplets(triplets.begin(), triplets.end());

   vector<int> expected_compressed_block_rows;
   expected_compressed_block_rows.push_back(0);
   expected_compressed_block_rows.push_back(2);
   expected_compressed_block_rows.push_back(1);
   expected_compressed_block_rows.push_back(2);
   expected_compressed_block_rows.push_back(0);
   expected_compressed_block_rows.push_back(1);

   vector<int> expected_compressed_block_cols;
   expected_compressed_block_cols.push_back(0);
   expected_compressed_block_cols.push_back(2);
   expected_compressed_block_cols.push_back(4);
   expected_compressed_block_cols.push_back(5);
   expected_compressed_block_cols.push_back(6);

   vector<int> compressed_block_rows;
   vector<int> compressed_block_cols;
   CompressedColumnScalarMatrixToBlockMatrix(
       sparse_matrix.innerIndexPtr(),
       sparse_matrix.outerIndexPtr(),
       row_blocks,
       col_blocks,
       &compressed_block_rows,
       &compressed_block_cols);

   EXPECT_EQ(compressed_block_rows, expected_compressed_block_rows);
   EXPECT_EQ(compressed_block_cols, expected_compressed_block_cols);
 }

 class SolveUpperTriangularTest : public ::testing::Test {
  protected:
   void SetUp() {
     cols.resize(5);
     rows.resize(7);
     values.resize(7);

     cols[0] = 0;
     rows[0] = 0;
     values[0] = 0.50754;

     cols[1] = 1;
     rows[1] = 1;
     values[1] = 0.80483;

     cols[2] = 2;
     rows[2] = 1;
     values[2] = 0.14120;
     rows[3] = 2;
     values[3] = 0.3;

     cols[3] = 4;
     rows[4] = 0;
     values[4] = 0.77696;
     rows[5] = 1;
     values[5] = 0.41860;
     rows[6] = 3;
     values[6] = 0.88979;

     cols[4] = 7;
   }

   vector<int> cols;
   vector<int> rows;
   vector<double> values;
 };

 TEST_F(SolveUpperTriangularTest, SolveInPlace) {
   double rhs_and_solution[] = {1.0, 1.0, 2.0, 2.0};
   const double expected[] = { -1.4706, -1.0962, 6.6667, 2.2477};

   SolveUpperTriangularInPlace<int>(cols.size() - 1,
                                    &rows[0],
                                    &cols[0],
                                    &values[0],
                                    rhs_and_solution);

   for (int i = 0; i < 4; ++i) {
     EXPECT_NEAR(rhs_and_solution[i], expected[i], 1e-4) << i;
   }
 }

 TEST_F(SolveUpperTriangularTest, TransposeSolveInPlace) {
   double rhs_and_solution[] = {1.0, 1.0, 2.0, 2.0};
   double expected[] = {1.970288,  1.242498,  6.081864, -0.057255};

   SolveUpperTriangularTransposeInPlace<int>(cols.size() - 1,
                                             &rows[0],
                                             &cols[0],
                                             &values[0],
                                             rhs_and_solution);

   for (int i = 0; i < 4; ++i) {
     EXPECT_NEAR(rhs_and_solution[i], expected[i], 1e-4) << i;
   }
 }

 TEST_F(SolveUpperTriangularTest, RTRSolveWithSparseRHS) {
   double solution[4];
   double expected[] = { 6.8420e+00,   1.0057e+00,  -1.4907e-16,  -1.9335e+00,
                         1.0057e+00,   2.2275e+00,  -1.9493e+00,  -6.5693e-01,
                         -1.4907e-16,  -1.9493e+00,   1.1111e+01,   9.7381e-17,
                         -1.9335e+00,  -6.5693e-01,   9.7381e-17,   1.2631e+00 };

   for (int i = 0; i < 4; ++i) {
     SolveRTRWithSparseRHS<int>(cols.size() - 1,
                                &rows[0],
                                &cols[0],
                                &values[0],
                                i,
                                solution);
     for (int j = 0; j < 4; ++j) {
       EXPECT_NEAR(solution[j], expected[4 * i + j], 1e-3) << i;
     }
   }
 }

 }  // namespace internal
 }  // namespace ceres
	// 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 <algorithm>
	#include <numeric>
	#include "ceres/compressed_col_sparse_matrix_utils.h"
	#include "ceres/internal/port.h"
	#include "ceres/triplet_sparse_matrix.h"
	#include "glog/logging.h"
	#include "gtest/gtest.h"
	#include "Eigen/SparseCore"

	namespace ceres {
	namespace internal {

	using std::vector;

	TEST(_, BlockPermutationToScalarPermutation) {
	vector<int> blocks;
	// Block structure
	// 0 --1- ---2--- ---3--- 4
	// [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
	blocks.push_back(1);
	blocks.push_back(2);
	blocks.push_back(3);
	blocks.push_back(3);
	blocks.push_back(1);

	// Block ordering
	// [1, 0, 2, 4, 5]
	vector<int> block_ordering;
	block_ordering.push_back(1);
	block_ordering.push_back(0);
	block_ordering.push_back(2);
	block_ordering.push_back(4);
	block_ordering.push_back(3);

	// Expected ordering
	// [1, 2, 0, 3, 4, 5, 9, 6, 7, 8]
	vector<int> expected_scalar_ordering;
	expected_scalar_ordering.push_back(1);
	expected_scalar_ordering.push_back(2);
	expected_scalar_ordering.push_back(0);
	expected_scalar_ordering.push_back(3);
	expected_scalar_ordering.push_back(4);
	expected_scalar_ordering.push_back(5);
	expected_scalar_ordering.push_back(9);
	expected_scalar_ordering.push_back(6);
	expected_scalar_ordering.push_back(7);
	expected_scalar_ordering.push_back(8);

	vector<int> scalar_ordering;
	BlockOrderingToScalarOrdering(blocks,
	block_ordering,
	&scalar_ordering);
	EXPECT_EQ(scalar_ordering.size(), expected_scalar_ordering.size());
	for (int i = 0; i < expected_scalar_ordering.size(); ++i) {
	EXPECT_EQ(scalar_ordering[i], expected_scalar_ordering[i]);
	}
	}

	static void FillBlock(const vector<int>& row_blocks,
	const vector<int>& col_blocks,
	const int row_block_id,
	const int col_block_id,
	vector<Eigen::Triplet<double>>* triplets) {
	const int row_offset = std::accumulate(&row_blocks[0], &row_blocks[row_block_id], 0);
	const int col_offset = std::accumulate(&col_blocks[0], &col_blocks[col_block_id], 0);
	for (int r = 0; r < row_blocks[row_block_id]; ++r) {
	for (int c = 0; c < col_blocks[col_block_id]; ++c) {
	triplets->push_back(Eigen::Triplet<double>(row_offset + r, col_offset + c, 1.0));
	}
	}
	}

	TEST(_, ScalarMatrixToBlockMatrix) {
	// Block sparsity.
	//
	// [1 2 3 2]
	// [1] x x
	// [2] x x
	// [2] x x
	// num_nonzeros = 1 + 3 + 4 + 4 + 1 + 2 = 15


	vector<int> col_blocks;
	col_blocks.push_back(1);
	col_blocks.push_back(2);
	col_blocks.push_back(3);
	col_blocks.push_back(2);

	vector<int> row_blocks;
	row_blocks.push_back(1);
	row_blocks.push_back(2);
	row_blocks.push_back(2);

	const int num_rows = std::accumulate(row_blocks.begin(), row_blocks.end(), 0.0);
	const int num_cols = std::accumulate(col_blocks.begin(), col_blocks.end(), 0.0);

	vector<Eigen::Triplet<double>> triplets;
	FillBlock(row_blocks, col_blocks, 0, 0, &triplets);
	FillBlock(row_blocks, col_blocks, 2, 0, &triplets);
	FillBlock(row_blocks, col_blocks, 1, 1, &triplets);
	FillBlock(row_blocks, col_blocks, 2, 1, &triplets);
	FillBlock(row_blocks, col_blocks, 0, 2, &triplets);
	FillBlock(row_blocks, col_blocks, 1, 3, &triplets);
	Eigen::SparseMatrix<double> sparse_matrix(num_rows, num_cols);
	sparse_matrix.setFromTriplets(triplets.begin(), triplets.end());

	vector<int> expected_compressed_block_rows;
	expected_compressed_block_rows.push_back(0);
	expected_compressed_block_rows.push_back(2);
	expected_compressed_block_rows.push_back(1);
	expected_compressed_block_rows.push_back(2);
	expected_compressed_block_rows.push_back(0);
	expected_compressed_block_rows.push_back(1);

	vector<int> expected_compressed_block_cols;
	expected_compressed_block_cols.push_back(0);
	expected_compressed_block_cols.push_back(2);
	expected_compressed_block_cols.push_back(4);
	expected_compressed_block_cols.push_back(5);
	expected_compressed_block_cols.push_back(6);

	vector<int> compressed_block_rows;
	vector<int> compressed_block_cols;
	CompressedColumnScalarMatrixToBlockMatrix(
	sparse_matrix.innerIndexPtr(),
	sparse_matrix.outerIndexPtr(),
	row_blocks,
	col_blocks,
	&compressed_block_rows,
	&compressed_block_cols);

	EXPECT_EQ(compressed_block_rows, expected_compressed_block_rows);
	EXPECT_EQ(compressed_block_cols, expected_compressed_block_cols);
	}

	class SolveUpperTriangularTest : public ::testing::Test {
	protected:
	void SetUp() {
	cols.resize(5);
	rows.resize(7);
	values.resize(7);

	cols[0] = 0;
	rows[0] = 0;
	values[0] = 0.50754;

	cols[1] = 1;
	rows[1] = 1;
	values[1] = 0.80483;

	cols[2] = 2;
	rows[2] = 1;
	values[2] = 0.14120;
	rows[3] = 2;
	values[3] = 0.3;

	cols[3] = 4;
	rows[4] = 0;
	values[4] = 0.77696;
	rows[5] = 1;
	values[5] = 0.41860;
	rows[6] = 3;
	values[6] = 0.88979;

	cols[4] = 7;
	}

	vector<int> cols;
	vector<int> rows;
	vector<double> values;
	};

	TEST_F(SolveUpperTriangularTest, SolveInPlace) {
	double rhs_and_solution[] = {1.0, 1.0, 2.0, 2.0};
	const double expected[] = { -1.4706, -1.0962, 6.6667, 2.2477};

	SolveUpperTriangularInPlace<int>(cols.size() - 1,
	&rows[0],
	&cols[0],
	&values[0],
	rhs_and_solution);

	for (int i = 0; i < 4; ++i) {
	EXPECT_NEAR(rhs_and_solution[i], expected[i], 1e-4) << i;
	}
	}

	TEST_F(SolveUpperTriangularTest, TransposeSolveInPlace) {
	double rhs_and_solution[] = {1.0, 1.0, 2.0, 2.0};
	double expected[] = {1.970288, 1.242498, 6.081864, -0.057255};

	SolveUpperTriangularTransposeInPlace<int>(cols.size() - 1,
	&rows[0],
	&cols[0],
	&values[0],
	rhs_and_solution);

	for (int i = 0; i < 4; ++i) {
	EXPECT_NEAR(rhs_and_solution[i], expected[i], 1e-4) << i;
	}
	}

	TEST_F(SolveUpperTriangularTest, RTRSolveWithSparseRHS) {
	double solution[4];
	double expected[] = { 6.8420e+00, 1.0057e+00, -1.4907e-16, -1.9335e+00,
	1.0057e+00, 2.2275e+00, -1.9493e+00, -6.5693e-01,
	-1.4907e-16, -1.9493e+00, 1.1111e+01, 9.7381e-17,
	-1.9335e+00, -6.5693e-01, 9.7381e-17, 1.2631e+00 };

	for (int i = 0; i < 4; ++i) {
	SolveRTRWithSparseRHS<int>(cols.size() - 1,
	&rows[0],
	&cols[0],
	&values[0],
	i,
	solution);
	for (int j = 0; j < 4; ++j) {
	EXPECT_NEAR(solution[j], expected[4 * i + j], 1e-3) << i;
	}
	}
	}

	} // namespace internal
	} // namespace ceres