| // 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 | |
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| // specific prior written permission. | |
| // | |
| // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" | |
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| // POSSIBILITY OF SUCH DAMAGE. | |
| // | |
| // Authors: dmitriy.korchemkin@gmail.com (Dmitriy Korchemkin) | |
| #include "ceres/internal/config.h" | |
| #ifndef CERES_NO_CUDA | |
| #include <numeric> | |
| #include "ceres/block_sparse_matrix.h" | |
| #include "ceres/cuda_block_structure.h" | |
| #include "gtest/gtest.h" | |
| namespace ceres::internal { | |
| class CudaBlockStructureTest : public ::testing::Test { | |
| protected: | |
| void SetUp() final { | |
| std::string message; | |
| ASSERT_TRUE(context_.InitCuda(&message)) | |
| << "InitCuda() failed because: " << message; | |
| BlockSparseMatrix::RandomMatrixOptions options; | |
| options.num_row_blocks = 1234; | |
| options.min_row_block_size = 1; | |
| options.max_row_block_size = 10; | |
| options.num_col_blocks = 567; | |
| options.min_col_block_size = 1; | |
| options.max_col_block_size = 10; | |
| options.block_density = 0.2; | |
| std::mt19937 rng; | |
| A_ = BlockSparseMatrix::CreateRandomMatrix(options, rng); | |
| std::iota( | |
| A_->mutable_values(), A_->mutable_values() + A_->num_nonzeros(), 1); | |
| } | |
| std::vector<Cell> GetCells(const CudaBlockSparseStructure& structure) { | |
| const auto& cuda_buffer = structure.cells_; | |
| std::vector<Cell> cells(cuda_buffer.size()); | |
| cuda_buffer.CopyToCpu(cells.data(), cells.size()); | |
| return cells; | |
| } | |
| std::vector<Block> GetRowBlocks(const CudaBlockSparseStructure& structure) { | |
| const auto& cuda_buffer = structure.row_blocks_; | |
| std::vector<Block> blocks(cuda_buffer.size()); | |
| cuda_buffer.CopyToCpu(blocks.data(), blocks.size()); | |
| return blocks; | |
| } | |
| std::vector<Block> GetColBlocks(const CudaBlockSparseStructure& structure) { | |
| const auto& cuda_buffer = structure.col_blocks_; | |
| std::vector<Block> blocks(cuda_buffer.size()); | |
| cuda_buffer.CopyToCpu(blocks.data(), blocks.size()); | |
| return blocks; | |
| } | |
| std::vector<int> GetRowBlockOffsets( | |
| const CudaBlockSparseStructure& structure) { | |
| const auto& cuda_buffer = structure.first_cell_in_row_block_; | |
| std::vector<int> first_cell_in_row_block(cuda_buffer.size()); | |
| cuda_buffer.CopyToCpu(first_cell_in_row_block.data(), | |
| first_cell_in_row_block.size()); | |
| return first_cell_in_row_block; | |
| } | |
| std::unique_ptr<BlockSparseMatrix> A_; | |
| ContextImpl context_; | |
| }; | |
| TEST_F(CudaBlockStructureTest, StructureIdentity) { | |
| auto block_structure = A_->block_structure(); | |
| const int num_row_blocks = block_structure->rows.size(); | |
| const int num_col_blocks = block_structure->cols.size(); | |
| CudaBlockSparseStructure cuda_block_structure(*block_structure, &context_); | |
| ASSERT_EQ(cuda_block_structure.num_rows(), A_->num_rows()); | |
| ASSERT_EQ(cuda_block_structure.num_cols(), A_->num_cols()); | |
| ASSERT_EQ(cuda_block_structure.num_nonzeros(), A_->num_nonzeros()); | |
| ASSERT_EQ(cuda_block_structure.num_row_blocks(), num_row_blocks); | |
| ASSERT_EQ(cuda_block_structure.num_col_blocks(), num_col_blocks); | |
| std::vector<Block> blocks = GetColBlocks(cuda_block_structure); | |
| ASSERT_EQ(blocks.size(), num_col_blocks); | |
| for (int i = 0; i < num_col_blocks; ++i) { | |
| EXPECT_EQ(block_structure->cols[i].position, blocks[i].position); | |
| EXPECT_EQ(block_structure->cols[i].size, blocks[i].size); | |
| } | |
| std::vector<Cell> cells = GetCells(cuda_block_structure); | |
| std::vector<int> first_cell_in_row_block = | |
| GetRowBlockOffsets(cuda_block_structure); | |
| blocks = GetRowBlocks(cuda_block_structure); | |
| ASSERT_EQ(blocks.size(), num_row_blocks); | |
| ASSERT_EQ(first_cell_in_row_block.size(), num_row_blocks + 1); | |
| ASSERT_EQ(first_cell_in_row_block.back(), cells.size()); | |
| for (int i = 0; i < num_row_blocks; ++i) { | |
| const int num_cells = block_structure->rows[i].cells.size(); | |
| EXPECT_EQ(blocks[i].position, block_structure->rows[i].block.position); | |
| EXPECT_EQ(blocks[i].size, block_structure->rows[i].block.size); | |
| const int first_cell = first_cell_in_row_block[i]; | |
| const int last_cell = first_cell_in_row_block[i + 1]; | |
| ASSERT_EQ(last_cell - first_cell, num_cells); | |
| for (int j = 0; j < num_cells; ++j) { | |
| EXPECT_EQ(cells[first_cell + j].block_id, | |
| block_structure->rows[i].cells[j].block_id); | |
| EXPECT_EQ(cells[first_cell + j].position, | |
| block_structure->rows[i].cells[j].position); | |
| } | |
| } | |
| } | |
| } // namespace ceres::internal | |
| #endif // CERES_NO_CUDA |