| // Ceres Solver - A fast non-linear least squares minimizer |
| // Copyright 2023 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 <algorithm> |
| #include <memory> |
| #include <numeric> |
| #include <random> |
| #include <string> |
| #include <vector> |
| |
| #include "Eigen/SparseCore" |
| #include "ceres/casts.h" |
| #include "ceres/context_impl.h" |
| #include "ceres/crs_matrix.h" |
| #include "ceres/internal/eigen.h" |
| #include "ceres/linear_least_squares_problems.h" |
| #include "ceres/triplet_sparse_matrix.h" |
| #include "gtest/gtest.h" |
| |
| namespace ceres::internal { |
| |
| static 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->RightMultiplyAndAccumulate(x.data(), y_a.data()); |
| b->RightMultiplyAndAccumulate(x.data(), y_b.data()); |
| EXPECT_EQ((y_a - y_b).norm(), 0); |
| } |
| } |
| |
| class CompressedRowSparseMatrixTest : public ::testing::Test { |
| protected: |
| void SetUp() final { |
| auto problem = CreateLinearLeastSquaresProblemFromId(1); |
| |
| ASSERT_TRUE(problem != nullptr); |
| |
| tsm.reset(down_cast<TripletSparseMatrix*>(problem->A.release())); |
| crsm = CompressedRowSparseMatrix::FromTripletSparseMatrix(*tsm); |
| |
| num_rows = tsm->num_rows(); |
| num_cols = tsm->num_cols(); |
| |
| std::vector<Block>* row_blocks = crsm->mutable_row_blocks(); |
| row_blocks->resize(num_rows); |
| for (int i = 0; i < row_blocks->size(); ++i) { |
| (*row_blocks)[i] = Block(1, i); |
| } |
| std::vector<Block>* col_blocks = crsm->mutable_col_blocks(); |
| col_blocks->resize(num_cols); |
| for (int i = 0; i < col_blocks->size(); ++i) { |
| (*col_blocks)[i] = Block(1, i); |
| } |
| } |
| |
| int num_rows; |
| int num_cols; |
| |
| std::unique_ptr<TripletSparseMatrix> tsm; |
| std::unique_ptr<CompressedRowSparseMatrix> crsm; |
| }; |
| |
| 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); |
| auto crsm_appendage = |
| CompressedRowSparseMatrix::FromTripletSparseMatrix(tsm_appendage); |
| |
| crsm->AppendRows(*crsm_appendage); |
| CompareMatrices(tsm.get(), crsm.get()); |
| } |
| } |
| |
| TEST_F(CompressedRowSparseMatrixTest, AppendAndDeleteBlockDiagonalMatrix) { |
| int num_diagonal_rows = crsm->num_cols(); |
| |
| auto diagonal = std::make_unique<double[]>(num_diagonal_rows); |
| for (int i = 0; i < num_diagonal_rows; ++i) { |
| diagonal[i] = i; |
| } |
| |
| std::vector<Block> row_and_column_blocks; |
| row_and_column_blocks.emplace_back(1, 0); |
| row_and_column_blocks.emplace_back(2, 1); |
| row_and_column_blocks.emplace_back(2, 3); |
| |
| const std::vector<Block> pre_row_blocks = crsm->row_blocks(); |
| const std::vector<Block> pre_col_blocks = crsm->col_blocks(); |
| |
| auto appendage = CompressedRowSparseMatrix::CreateBlockDiagonalMatrix( |
| diagonal.get(), row_and_column_blocks); |
| |
| crsm->AppendRows(*appendage); |
| |
| const std::vector<Block> post_row_blocks = crsm->row_blocks(); |
| const std::vector<Block> post_col_blocks = crsm->col_blocks(); |
| |
| std::vector<Block> expected_row_blocks = pre_row_blocks; |
| expected_row_blocks.insert(expected_row_blocks.end(), |
| row_and_column_blocks.begin(), |
| row_and_column_blocks.end()); |
| |
| std::vector<Block> 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) { |
| std::vector<Block> blocks; |
| blocks.emplace_back(1, 0); |
| blocks.emplace_back(2, 1); |
| blocks.emplace_back(2, 3); |
| |
| Vector diagonal(5); |
| for (int i = 0; i < 5; ++i) { |
| diagonal(i) = i + 1; |
| } |
| |
| auto 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->RightMultiplyAndAccumulate(x.data(), y.data()); |
| for (int i = 0; i < diagonal.size(); ++i) { |
| EXPECT_EQ(y[i], diagonal[i]); |
| } |
| |
| y.setZero(); |
| matrix->LeftMultiplyAndAccumulate(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); |
| } |
| |
| TEST(CompressedRowSparseMatrix, Transpose) { |
| // 0 1 0 2 3 0 |
| // 4 5 6 0 0 7 |
| // 8 9 0 10 11 0 |
| // 12 0 13 14 15 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()->emplace_back(3, 0); |
| matrix.mutable_row_blocks()->emplace_back(3, 3); |
| matrix.mutable_col_blocks()->emplace_back(4, 0); |
| matrix.mutable_col_blocks()->emplace_back(2, 4); |
| |
| 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::iota(values, values + 17, 1); |
| |
| auto 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); |
| } |
| |
| TEST(CompressedRowSparseMatrix, FromTripletSparseMatrix) { |
| std::mt19937 prng; |
| TripletSparseMatrix::RandomMatrixOptions options; |
| options.num_rows = 5; |
| options.num_cols = 7; |
| options.density = 0.5; |
| |
| const int kNumTrials = 10; |
| for (int i = 0; i < kNumTrials; ++i) { |
| auto tsm = TripletSparseMatrix::CreateRandomMatrix(options, prng); |
| auto crsm = CompressedRowSparseMatrix::FromTripletSparseMatrix(*tsm); |
| |
| Matrix expected; |
| tsm->ToDenseMatrix(&expected); |
| Matrix actual; |
| crsm->ToDenseMatrix(&actual); |
| EXPECT_NEAR((expected - actual).norm() / actual.norm(), |
| 0.0, |
| std::numeric_limits<double>::epsilon()) |
| << "\nexpected: \n" |
| << expected << "\nactual: \n" |
| << actual; |
| } |
| } |
| |
| TEST(CompressedRowSparseMatrix, FromTripletSparseMatrixTransposed) { |
| std::mt19937 prng; |
| TripletSparseMatrix::RandomMatrixOptions options; |
| options.num_rows = 5; |
| options.num_cols = 7; |
| options.density = 0.5; |
| |
| const int kNumTrials = 10; |
| for (int i = 0; i < kNumTrials; ++i) { |
| auto tsm = TripletSparseMatrix::CreateRandomMatrix(options, prng); |
| auto crsm = |
| CompressedRowSparseMatrix::FromTripletSparseMatrixTransposed(*tsm); |
| |
| Matrix tmp; |
| tsm->ToDenseMatrix(&tmp); |
| Matrix expected = tmp.transpose(); |
| Matrix actual; |
| crsm->ToDenseMatrix(&actual); |
| EXPECT_NEAR((expected - actual).norm() / actual.norm(), |
| 0.0, |
| std::numeric_limits<double>::epsilon()) |
| << "\nexpected: \n" |
| << expected << "\nactual: \n" |
| << actual; |
| } |
| } |
| |
| using Param = ::testing::tuple<CompressedRowSparseMatrix::StorageType>; |
| |
| static std::string ParamInfoToString(testing::TestParamInfo<Param> info) { |
| if (::testing::get<0>(info.param) == |
| CompressedRowSparseMatrix::StorageType::UPPER_TRIANGULAR) { |
| return "UPPER"; |
| } |
| |
| if (::testing::get<0>(info.param) == |
| CompressedRowSparseMatrix::StorageType::LOWER_TRIANGULAR) { |
| return "LOWER"; |
| } |
| |
| return "UNSYMMETRIC"; |
| } |
| |
| class RightMultiplyAndAccumulateTest : public ::testing::TestWithParam<Param> { |
| }; |
| |
| TEST_P(RightMultiplyAndAccumulateTest, _) { |
| const int kMinNumBlocks = 1; |
| const int kMaxNumBlocks = 10; |
| const int kMinBlockSize = 1; |
| const int kMaxBlockSize = 5; |
| const int kNumTrials = 10; |
| std::mt19937 prng; |
| std::uniform_real_distribution<double> uniform(0.5, 1.0); |
| for (int num_blocks = kMinNumBlocks; num_blocks < kMaxNumBlocks; |
| ++num_blocks) { |
| for (int trial = 0; trial < kNumTrials; ++trial) { |
| Param param = GetParam(); |
| CompressedRowSparseMatrix::RandomMatrixOptions options; |
| options.num_col_blocks = num_blocks; |
| options.min_col_block_size = kMinBlockSize; |
| options.max_col_block_size = kMaxBlockSize; |
| options.num_row_blocks = 2 * num_blocks; |
| options.min_row_block_size = kMinBlockSize; |
| options.max_row_block_size = kMaxBlockSize; |
| options.block_density = uniform(prng); |
| options.storage_type = ::testing::get<0>(param); |
| auto matrix = |
| CompressedRowSparseMatrix::CreateRandomMatrix(options, prng); |
| const int num_rows = matrix->num_rows(); |
| const int num_cols = matrix->num_cols(); |
| |
| Vector x(num_cols); |
| x.setRandom(); |
| |
| Vector actual_y(num_rows); |
| actual_y.setZero(); |
| matrix->RightMultiplyAndAccumulate(x.data(), actual_y.data()); |
| |
| Matrix dense; |
| matrix->ToDenseMatrix(&dense); |
| Vector expected_y; |
| if (::testing::get<0>(param) == |
| CompressedRowSparseMatrix::StorageType::UPPER_TRIANGULAR) { |
| expected_y = dense.selfadjointView<Eigen::Upper>() * x; |
| } else if (::testing::get<0>(param) == |
| CompressedRowSparseMatrix::StorageType::LOWER_TRIANGULAR) { |
| expected_y = dense.selfadjointView<Eigen::Lower>() * x; |
| } else { |
| expected_y = dense * x; |
| } |
| |
| ASSERT_NEAR((expected_y - actual_y).norm() / actual_y.norm(), |
| 0.0, |
| std::numeric_limits<double>::epsilon() * 10) |
| << "\n" |
| << dense << "x:\n" |
| << x.transpose() << "\n" |
| << "expected: \n" |
| << expected_y.transpose() << "\n" |
| << "actual: \n" |
| << actual_y.transpose(); |
| } |
| } |
| } |
| |
| INSTANTIATE_TEST_SUITE_P( |
| CompressedRowSparseMatrix, |
| RightMultiplyAndAccumulateTest, |
| ::testing::Values(CompressedRowSparseMatrix::StorageType::LOWER_TRIANGULAR, |
| CompressedRowSparseMatrix::StorageType::UPPER_TRIANGULAR, |
| CompressedRowSparseMatrix::StorageType::UNSYMMETRIC), |
| ParamInfoToString); |
| |
| class LeftMultiplyAndAccumulateTest : public ::testing::TestWithParam<Param> {}; |
| |
| TEST_P(LeftMultiplyAndAccumulateTest, _) { |
| const int kMinNumBlocks = 1; |
| const int kMaxNumBlocks = 10; |
| const int kMinBlockSize = 1; |
| const int kMaxBlockSize = 5; |
| const int kNumTrials = 10; |
| std::mt19937 prng; |
| std::uniform_real_distribution<double> uniform(0.5, 1.0); |
| for (int num_blocks = kMinNumBlocks; num_blocks < kMaxNumBlocks; |
| ++num_blocks) { |
| for (int trial = 0; trial < kNumTrials; ++trial) { |
| Param param = GetParam(); |
| CompressedRowSparseMatrix::RandomMatrixOptions options; |
| options.num_col_blocks = num_blocks; |
| options.min_col_block_size = kMinBlockSize; |
| options.max_col_block_size = kMaxBlockSize; |
| options.num_row_blocks = 2 * num_blocks; |
| options.min_row_block_size = kMinBlockSize; |
| options.max_row_block_size = kMaxBlockSize; |
| options.block_density = uniform(prng); |
| options.storage_type = ::testing::get<0>(param); |
| auto matrix = |
| CompressedRowSparseMatrix::CreateRandomMatrix(options, prng); |
| const int num_rows = matrix->num_rows(); |
| const int num_cols = matrix->num_cols(); |
| |
| Vector x(num_rows); |
| x.setRandom(); |
| |
| Vector actual_y(num_cols); |
| actual_y.setZero(); |
| matrix->LeftMultiplyAndAccumulate(x.data(), actual_y.data()); |
| |
| Matrix dense; |
| matrix->ToDenseMatrix(&dense); |
| Vector expected_y; |
| if (::testing::get<0>(param) == |
| CompressedRowSparseMatrix::StorageType::UPPER_TRIANGULAR) { |
| expected_y = dense.selfadjointView<Eigen::Upper>() * x; |
| } else if (::testing::get<0>(param) == |
| CompressedRowSparseMatrix::StorageType::LOWER_TRIANGULAR) { |
| expected_y = dense.selfadjointView<Eigen::Lower>() * x; |
| } else { |
| expected_y = dense.transpose() * x; |
| } |
| |
| ASSERT_NEAR((expected_y - actual_y).norm() / actual_y.norm(), |
| 0.0, |
| std::numeric_limits<double>::epsilon() * 10) |
| << "\n" |
| << dense << "x\n" |
| << x.transpose() << "\n" |
| << "expected: \n" |
| << expected_y.transpose() << "\n" |
| << "actual: \n" |
| << actual_y.transpose(); |
| } |
| } |
| } |
| |
| INSTANTIATE_TEST_SUITE_P( |
| CompressedRowSparseMatrix, |
| LeftMultiplyAndAccumulateTest, |
| ::testing::Values(CompressedRowSparseMatrix::StorageType::LOWER_TRIANGULAR, |
| CompressedRowSparseMatrix::StorageType::UPPER_TRIANGULAR, |
| CompressedRowSparseMatrix::StorageType::UNSYMMETRIC), |
| ParamInfoToString); |
| |
| class SquaredColumnNormTest : public ::testing::TestWithParam<Param> {}; |
| |
| TEST_P(SquaredColumnNormTest, _) { |
| const int kMinNumBlocks = 1; |
| const int kMaxNumBlocks = 10; |
| const int kMinBlockSize = 1; |
| const int kMaxBlockSize = 5; |
| const int kNumTrials = 10; |
| std::mt19937 prng; |
| std::uniform_real_distribution<double> uniform(0.5, 1.0); |
| for (int num_blocks = kMinNumBlocks; num_blocks < kMaxNumBlocks; |
| ++num_blocks) { |
| for (int trial = 0; trial < kNumTrials; ++trial) { |
| Param param = GetParam(); |
| CompressedRowSparseMatrix::RandomMatrixOptions options; |
| options.num_col_blocks = num_blocks; |
| options.min_col_block_size = kMinBlockSize; |
| options.max_col_block_size = kMaxBlockSize; |
| options.num_row_blocks = 2 * num_blocks; |
| options.min_row_block_size = kMinBlockSize; |
| options.max_row_block_size = kMaxBlockSize; |
| options.block_density = uniform(prng); |
| options.storage_type = ::testing::get<0>(param); |
| auto matrix = |
| CompressedRowSparseMatrix::CreateRandomMatrix(options, prng); |
| const int num_cols = matrix->num_cols(); |
| |
| Vector actual(num_cols); |
| actual.setZero(); |
| matrix->SquaredColumnNorm(actual.data()); |
| |
| Matrix dense; |
| matrix->ToDenseMatrix(&dense); |
| Vector expected; |
| if (::testing::get<0>(param) == |
| CompressedRowSparseMatrix::StorageType::UPPER_TRIANGULAR) { |
| const Matrix full = dense.selfadjointView<Eigen::Upper>(); |
| expected = full.colwise().squaredNorm(); |
| } else if (::testing::get<0>(param) == |
| CompressedRowSparseMatrix::StorageType::LOWER_TRIANGULAR) { |
| const Matrix full = dense.selfadjointView<Eigen::Lower>(); |
| expected = full.colwise().squaredNorm(); |
| } else { |
| expected = dense.colwise().squaredNorm(); |
| } |
| |
| ASSERT_NEAR((expected - actual).norm() / actual.norm(), |
| 0.0, |
| std::numeric_limits<double>::epsilon() * 10) |
| << "\n" |
| << dense << "expected: \n" |
| << expected.transpose() << "\n" |
| << "actual: \n" |
| << actual.transpose(); |
| } |
| } |
| } |
| |
| INSTANTIATE_TEST_SUITE_P( |
| CompressedRowSparseMatrix, |
| SquaredColumnNormTest, |
| ::testing::Values(CompressedRowSparseMatrix::StorageType::LOWER_TRIANGULAR, |
| CompressedRowSparseMatrix::StorageType::UPPER_TRIANGULAR, |
| CompressedRowSparseMatrix::StorageType::UNSYMMETRIC), |
| ParamInfoToString); |
| |
| const int kMaxNumThreads = 8; |
| class CompressedRowSparseMatrixParallelTest |
| : public ::testing::TestWithParam<int> { |
| void SetUp() final { context_.EnsureMinimumThreads(kMaxNumThreads); } |
| |
| protected: |
| ContextImpl context_; |
| }; |
| |
| TEST_P(CompressedRowSparseMatrixParallelTest, |
| RightMultiplyAndAccumulateUnsymmetric) { |
| const int kMinNumBlocks = 1; |
| const int kMaxNumBlocks = 10; |
| const int kMinBlockSize = 1; |
| const int kMaxBlockSize = 5; |
| const int kNumTrials = 10; |
| const int kNumThreads = GetParam(); |
| std::mt19937 prng; |
| std::uniform_real_distribution<double> uniform(0.5, 1.0); |
| for (int num_blocks = kMinNumBlocks; num_blocks < kMaxNumBlocks; |
| ++num_blocks) { |
| for (int trial = 0; trial < kNumTrials; ++trial) { |
| CompressedRowSparseMatrix::RandomMatrixOptions options; |
| options.num_col_blocks = num_blocks; |
| options.min_col_block_size = kMinBlockSize; |
| options.max_col_block_size = kMaxBlockSize; |
| options.num_row_blocks = 2 * num_blocks; |
| options.min_row_block_size = kMinBlockSize; |
| options.max_row_block_size = kMaxBlockSize; |
| options.block_density = uniform(prng); |
| options.storage_type = |
| CompressedRowSparseMatrix::StorageType::UNSYMMETRIC; |
| auto matrix = |
| CompressedRowSparseMatrix::CreateRandomMatrix(options, prng); |
| const int num_rows = matrix->num_rows(); |
| const int num_cols = matrix->num_cols(); |
| |
| Vector x(num_cols); |
| x.setRandom(); |
| |
| Vector actual_y(num_rows); |
| actual_y.setZero(); |
| matrix->RightMultiplyAndAccumulate( |
| x.data(), actual_y.data(), &context_, kNumThreads); |
| |
| Matrix dense; |
| matrix->ToDenseMatrix(&dense); |
| Vector expected_y = dense * x; |
| |
| ASSERT_NEAR((expected_y - actual_y).norm() / actual_y.norm(), |
| 0.0, |
| std::numeric_limits<double>::epsilon() * 10) |
| << "\n" |
| << dense << "x:\n" |
| << x.transpose() << "\n" |
| << "expected: \n" |
| << expected_y.transpose() << "\n" |
| << "actual: \n" |
| << actual_y.transpose(); |
| } |
| } |
| } |
| INSTANTIATE_TEST_SUITE_P(ParallelProducts, |
| CompressedRowSparseMatrixParallelTest, |
| ::testing::Values(1, 2, 4, 8), |
| ::testing::PrintToStringParamName()); |
| |
| // TODO(sameeragarwal) Add tests for the random matrix creation methods. |
| |
| } // namespace ceres::internal |
