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// 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
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// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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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