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ceres-solver/internal/ceres/block_random_access_diagonal_matrix_test.cc
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Sameer Agarwal 19ab2c1793 BlockRandomAccessMatrix Refactor
1. Add threading to all three subclasses of BlockRandomAccessMatrix.
   i.e. BlockRandomAccessDenseMatrix, BlockRandomAccessSparseMatrix
   and BlockRandomAccessDenseMatrix.

   For BlockRandomAccessDenseMatrix and BlockRandomAccessSparseMatrix
   this just means SetZero is parallelized. Which by itself is no
   big deal, but by doing so, the constructor for all three subclasses
   become uniform.

   BlockRandomAccessSparseMatrix::SymmetricRightMultiplyAndAccumulate
   maybe threaded in the future if needed.

   BlockRandomAccessDiagonalMatrix is the biggest beneficiary. SetZero
   Invert and RightMultiplyAndAccumulate are all threaded now.

2. Change the storage in BlockRandomAccessDiagonalMatrix from
   TripletSparseMatrix to CompressedRowSparseMatrix. This has no
   performance implications since we do not really use the capabilities
   of the underlying matrix indexing representation. This is a forward
   looking change when we decide to transfer this matrix to the GPU,
   a CompressedRowSparseMatrix will save on a data conversion.

3. Use std::unique_ptr as needed and eliminate the need for custom
   destructors.

4. Modify CompressedRowSparseMatrix::CreateBlockDiagonalMatrix to
   take a nullptr as the data vector.

Fixes https://github.com/ceres-solver/ceres-solver/issues/936
Fixes https://github.com/ceres-solver/ceres-solver/issues/935

Change-Id: Ia6487f2d924fbe669835bdcc38abf2b451bda4ee
2022-12-23 06:45:28 -08:00

170 lines
5.5 KiB
C++

// 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:
//
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// Author: sameeragarwal@google.com (Sameer Agarwal)
#include "ceres/block_random_access_diagonal_matrix.h"
#include <limits>
#include <memory>
#include <vector>
#include "Eigen/Cholesky"
#include "ceres/internal/eigen.h"
#include "glog/logging.h"
#include "gtest/gtest.h"
namespace ceres::internal {
class BlockRandomAccessDiagonalMatrixTest : public ::testing::Test {
public:
void SetUp() override {
std::vector<Block> blocks;
blocks.emplace_back(3, 0);
blocks.emplace_back(4, 3);
blocks.emplace_back(5, 7);
const int num_rows = 3 + 4 + 5;
num_nonzeros_ = 3 * 3 + 4 * 4 + 5 * 5;
m_ =
std::make_unique<BlockRandomAccessDiagonalMatrix>(blocks, &context_, 1);
EXPECT_EQ(m_->num_rows(), num_rows);
EXPECT_EQ(m_->num_cols(), num_rows);
for (int i = 0; i < blocks.size(); ++i) {
const int row_block_id = i;
int col_block_id;
int row;
int col;
int row_stride;
int col_stride;
for (int j = 0; j < blocks.size(); ++j) {
col_block_id = j;
CellInfo* cell = m_->GetCell(
row_block_id, col_block_id, &row, &col, &row_stride, &col_stride);
// Off diagonal entries are not present.
if (i != j) {
EXPECT_TRUE(cell == nullptr);
continue;
}
EXPECT_TRUE(cell != nullptr);
EXPECT_EQ(row, 0);
EXPECT_EQ(col, 0);
EXPECT_EQ(row_stride, blocks[row_block_id].size);
EXPECT_EQ(col_stride, blocks[col_block_id].size);
// Write into the block
MatrixRef(cell->values, row_stride, col_stride)
.block(row,
col,
blocks[row_block_id].size,
blocks[col_block_id].size) =
(row_block_id + 1) * (col_block_id + 1) *
Matrix::Ones(blocks[row_block_id].size,
blocks[col_block_id].size) +
Matrix::Identity(blocks[row_block_id].size,
blocks[row_block_id].size);
}
}
}
protected:
ContextImpl context_;
int num_nonzeros_;
std::unique_ptr<BlockRandomAccessDiagonalMatrix> m_;
};
TEST_F(BlockRandomAccessDiagonalMatrixTest, MatrixContents) {
auto* crsm = m_->matrix();
EXPECT_EQ(crsm->num_nonzeros(), num_nonzeros_);
Matrix dense;
crsm->ToDenseMatrix(&dense);
double kTolerance = 1e-14;
// (0,0)
EXPECT_NEAR(
(dense.block(0, 0, 3, 3) - (Matrix::Ones(3, 3) + Matrix::Identity(3, 3)))
.norm(),
0.0,
kTolerance);
// (1,1)
EXPECT_NEAR((dense.block(3, 3, 4, 4) -
(2 * 2 * Matrix::Ones(4, 4) + Matrix::Identity(4, 4)))
.norm(),
0.0,
kTolerance);
// (1,1)
EXPECT_NEAR((dense.block(7, 7, 5, 5) -
(3 * 3 * Matrix::Ones(5, 5) + Matrix::Identity(5, 5)))
.norm(),
0.0,
kTolerance);
// There is nothing else in the matrix besides these four blocks.
EXPECT_NEAR(
dense.norm(),
sqrt(6 * 1.0 + 3 * 4.0 + 12 * 16.0 + 4 * 25.0 + 20 * 81.0 + 5 * 100.0),
kTolerance);
}
TEST_F(BlockRandomAccessDiagonalMatrixTest, RightMultiplyAndAccumulate) {
double kTolerance = 1e-14;
auto* crsm = m_->matrix();
Matrix dense;
crsm->ToDenseMatrix(&dense);
Vector x = Vector::Random(dense.rows());
Vector expected_y = dense * x;
Vector actual_y = Vector::Zero(dense.rows());
m_->RightMultiplyAndAccumulate(x.data(), actual_y.data());
EXPECT_NEAR((expected_y - actual_y).norm(), 0, kTolerance);
}
TEST_F(BlockRandomAccessDiagonalMatrixTest, Invert) {
double kTolerance = 1e-14;
auto* crsm = m_->matrix();
Matrix dense;
crsm->ToDenseMatrix(&dense);
Matrix expected_inverse =
dense.llt().solve(Matrix::Identity(dense.rows(), dense.rows()));
m_->Invert();
crsm->ToDenseMatrix(&dense);
EXPECT_NEAR((expected_inverse - dense).norm(), 0.0, kTolerance);
}
} // namespace ceres::internal