MYNT-EYE-S-SDK/3rdparty/ceres-solver-1.11.0/internal/ceres/block_jacobi_preconditioner_test.cc

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// 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:
//
// * 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
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// POSSIBILITY OF SUCH DAMAGE.
//
// Author: sameeragarwal@google.com (Sameer Agarwal)
#include "ceres/block_jacobi_preconditioner.h"
#include <vector>
#include "ceres/block_random_access_diagonal_matrix.h"
#include "ceres/linear_least_squares_problems.h"
#include "ceres/block_sparse_matrix.h"
#include "ceres/internal/scoped_ptr.h"
#include "gtest/gtest.h"
#include "Eigen/Dense"
namespace ceres {
namespace internal {
class BlockJacobiPreconditionerTest : public ::testing::Test {
protected:
void SetUpFromProblemId(int problem_id) {
scoped_ptr<LinearLeastSquaresProblem> problem(
CreateLinearLeastSquaresProblemFromId(problem_id));
CHECK_NOTNULL(problem.get());
A.reset(down_cast<BlockSparseMatrix*>(problem->A.release()));
D.reset(problem->D.release());
Matrix dense_a;
A->ToDenseMatrix(&dense_a);
dense_ata = dense_a.transpose() * dense_a;
dense_ata += VectorRef(D.get(), A->num_cols())
.array().square().matrix().asDiagonal();
}
void VerifyDiagonalBlocks(const int problem_id) {
SetUpFromProblemId(problem_id);
BlockJacobiPreconditioner pre(*A);
pre.Update(*A, D.get());
BlockRandomAccessDiagonalMatrix* m =
const_cast<BlockRandomAccessDiagonalMatrix*>(&pre.matrix());
EXPECT_EQ(m->num_rows(), A->num_cols());
EXPECT_EQ(m->num_cols(), A->num_cols());
const CompressedRowBlockStructure* bs = A->block_structure();
for (int i = 0; i < bs->cols.size(); ++i) {
const int block_size = bs->cols[i].size;
int r, c, row_stride, col_stride;
CellInfo* cell_info = m->GetCell(i, i,
&r, &c,
&row_stride, &col_stride);
MatrixRef m(cell_info->values, row_stride, col_stride);
Matrix actual_block_inverse = m.block(r, c, block_size, block_size);
Matrix expected_block = dense_ata.block(bs->cols[i].position,
bs->cols[i].position,
block_size,
block_size);
const double residual = (actual_block_inverse * expected_block -
Matrix::Identity(block_size, block_size)).norm();
EXPECT_NEAR(residual, 0.0, 1e-12) << "Block: " << i;
}
}
scoped_ptr<BlockSparseMatrix> A;
scoped_array<double> D;
Matrix dense_ata;
};
TEST_F(BlockJacobiPreconditionerTest, SmallProblem) {
VerifyDiagonalBlocks(2);
}
TEST_F(BlockJacobiPreconditionerTest, LargeProblem) {
VerifyDiagonalBlocks(3);
}
} // namespace internal
} // namespace ceres