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

 // Ceres Solver - A fast non-linear least squares minimizer
 // Copyright 2022 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 <memory>
 #include <numeric>
 #include <random>

 #include "Eigen/SparseCore"
 #include "ceres/block_sparse_matrix.h"
 #include "ceres/internal/eigen.h"
 #include "ceres/triplet_sparse_matrix.h"
 #include "glog/logging.h"
 #include "gtest/gtest.h"

 namespace ceres {
 namespace internal {

 #define COMPUTE_AND_COMPARE                                                   \
   {                                                                           \
     inner_product_computer->Compute();                                        \
     CompressedRowSparseMatrix* actual_product_crsm =                          \
         inner_product_computer->mutable_result();                             \
     Matrix actual_inner_product =                                             \
         Eigen::Map<Eigen::SparseMatrix<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());                           \
     EXPECT_EQ(actual_inner_product.rows(), actual_inner_product.cols());      \
     EXPECT_EQ(expected_inner_product.rows(), expected_inner_product.cols());  \
     EXPECT_EQ(actual_inner_product.rows(), expected_inner_product.rows());    \
     Matrix expected_t, actual_t;                                              \
     if (actual_product_crsm->storage_type() ==                                \
         CompressedRowSparseMatrix::StorageType::LOWER_TRIANGULAR) {           \
       expected_t = expected_inner_product.triangularView<Eigen::Upper>();     \
       actual_t = actual_inner_product.triangularView<Eigen::Upper>();         \
     } else {                                                                  \
       expected_t = expected_inner_product.triangularView<Eigen::Lower>();     \
       actual_t = actual_inner_product.triangularView<Eigen::Lower>();         \
     }                                                                         \
     EXPECT_LE((expected_t - actual_t).norm(),                                 \
               100 * std::numeric_limits<double>::epsilon() * actual_t.norm()) \
         << "expected: \n"                                                     \
         << expected_t << "\nactual: \n"                                       \
         << actual_t;                                                          \
   }

 TEST(InnerProductComputer, NormalOperation) {
   const int kMaxNumRowBlocks = 10;
   const int kMaxNumColBlocks = 10;
   const int kNumTrials = 10;
   std::mt19937 prng;
   std::uniform_real_distribution<double> distribution(0.01, 1.0);

   // 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 = distribution(prng);

         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;

         std::unique_ptr<BlockSparseMatrix> random_matrix(
             BlockSparseMatrix::CreateRandomMatrix(options, prng));

         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.emplace_back(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;

         std::unique_ptr<InnerProductComputer> inner_product_computer;

         inner_product_computer = InnerProductComputer::Create(
             *random_matrix,
             CompressedRowSparseMatrix::StorageType::LOWER_TRIANGULAR);
         COMPUTE_AND_COMPARE;
         inner_product_computer = InnerProductComputer::Create(
             *random_matrix,
             CompressedRowSparseMatrix::StorageType::UPPER_TRIANGULAR);
         COMPUTE_AND_COMPARE;
       }
     }
   }
 }

 TEST(InnerProductComputer, SubMatrix) {
   const int kNumRowBlocks = 10;
   const int kNumColBlocks = 20;
   const int kNumTrials = 5;
   std::mt19937 prng;
   std::uniform_real_distribution<double> distribution(0.01, 1.0);

   // 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 = distribution(prng);

     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;

     std::unique_ptr<BlockSparseMatrix> random_matrix(
         BlockSparseMatrix::CreateRandomMatrix(options, prng));

     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.emplace_back(
                 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;

         std::unique_ptr<InnerProductComputer> inner_product_computer;
         inner_product_computer = InnerProductComputer::Create(
             *random_matrix,
             start_row_block,
             end_row_block,
             CompressedRowSparseMatrix::StorageType::LOWER_TRIANGULAR);
         COMPUTE_AND_COMPARE;
         inner_product_computer = InnerProductComputer::Create(
             *random_matrix,
             start_row_block,
             end_row_block,
             CompressedRowSparseMatrix::StorageType::UPPER_TRIANGULAR);
         COMPUTE_AND_COMPARE;
       }
     }
   }
 }

 #undef COMPUTE_AND_COMPARE
 }  // namespace internal
 }  // namespace ceres
	// Ceres Solver - A fast non-linear least squares minimizer
	// Copyright 2022 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 <memory>
	#include <numeric>
	#include <random>

	#include "Eigen/SparseCore"
	#include "ceres/block_sparse_matrix.h"
	#include "ceres/internal/eigen.h"
	#include "ceres/triplet_sparse_matrix.h"
	#include "glog/logging.h"
	#include "gtest/gtest.h"

	namespace ceres {
	namespace internal {

	#define COMPUTE_AND_COMPARE \
	{ \
	inner_product_computer->Compute(); \
	CompressedRowSparseMatrix* actual_product_crsm = \
	inner_product_computer->mutable_result(); \
	Matrix actual_inner_product = \
	Eigen::Map<Eigen::SparseMatrix<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()); \
	EXPECT_EQ(actual_inner_product.rows(), actual_inner_product.cols()); \
	EXPECT_EQ(expected_inner_product.rows(), expected_inner_product.cols()); \
	EXPECT_EQ(actual_inner_product.rows(), expected_inner_product.rows()); \
	Matrix expected_t, actual_t; \
	if (actual_product_crsm->storage_type() == \
	CompressedRowSparseMatrix::StorageType::LOWER_TRIANGULAR) { \
	expected_t = expected_inner_product.triangularView<Eigen::Upper>(); \
	actual_t = actual_inner_product.triangularView<Eigen::Upper>(); \
	} else { \
	expected_t = expected_inner_product.triangularView<Eigen::Lower>(); \
	actual_t = actual_inner_product.triangularView<Eigen::Lower>(); \
	} \
	EXPECT_LE((expected_t - actual_t).norm(), \
	100 * std::numeric_limits<double>::epsilon() * actual_t.norm()) \
	<< "expected: \n" \
	<< expected_t << "\nactual: \n" \
	<< actual_t; \
	}

	TEST(InnerProductComputer, NormalOperation) {
	const int kMaxNumRowBlocks = 10;
	const int kMaxNumColBlocks = 10;
	const int kNumTrials = 10;
	std::mt19937 prng;
	std::uniform_real_distribution<double> distribution(0.01, 1.0);

	// 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 = distribution(prng);

	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;

	std::unique_ptr<BlockSparseMatrix> random_matrix(
	BlockSparseMatrix::CreateRandomMatrix(options, prng));

	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.emplace_back(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;

	std::unique_ptr<InnerProductComputer> inner_product_computer;

	inner_product_computer = InnerProductComputer::Create(
	*random_matrix,
	CompressedRowSparseMatrix::StorageType::LOWER_TRIANGULAR);
	COMPUTE_AND_COMPARE;
	inner_product_computer = InnerProductComputer::Create(
	*random_matrix,
	CompressedRowSparseMatrix::StorageType::UPPER_TRIANGULAR);
	COMPUTE_AND_COMPARE;
	}
	}
	}
	}

	TEST(InnerProductComputer, SubMatrix) {
	const int kNumRowBlocks = 10;
	const int kNumColBlocks = 20;
	const int kNumTrials = 5;
	std::mt19937 prng;
	std::uniform_real_distribution<double> distribution(0.01, 1.0);

	// 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 = distribution(prng);

	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;

	std::unique_ptr<BlockSparseMatrix> random_matrix(
	BlockSparseMatrix::CreateRandomMatrix(options, prng));

	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.emplace_back(
	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;

	std::unique_ptr<InnerProductComputer> inner_product_computer;
	inner_product_computer = InnerProductComputer::Create(
	*random_matrix,
	start_row_block,
	end_row_block,
	CompressedRowSparseMatrix::StorageType::LOWER_TRIANGULAR);
	COMPUTE_AND_COMPARE;
	inner_product_computer = InnerProductComputer::Create(
	*random_matrix,
	start_row_block,
	end_row_block,
	CompressedRowSparseMatrix::StorageType::UPPER_TRIANGULAR);
	COMPUTE_AND_COMPARE;
	}
	}
	}
	}

	#undef COMPUTE_AND_COMPARE
	} // namespace internal
	} // namespace ceres