mirror of
https://github.com/ceres-solver/ceres-solver.git
synced 2026-08-30 00:50:37 +08:00
Sergiu Deitsch
f90833f5fa
Currently, the logic for exporting symbols is rather complicated: when tests are enabled internal symbols are exported in addition to the public symbols. Such logic causes several problems. (1) Test binaries link against a Ceres build that is different from the final release since fewer optimizations are applied if more symbols are exported. (2) Also, some toolchains hide symbols by default breaking the existing logic eventually causing linker errors. Since internal symbols are not intended to be used outside of the project, we can compile them into object files and use exactly the same binary code both for the final build and the tests without relying on conditionals. By default, all symbols are now hidden unless annotated as public. Internal symbols are explicitly marked as not being exported in case users chose not to hide symbols by default. Change-Id: I589dd10be2f6f438508783cf99d141af0120057b
250 lines
8.2 KiB
C++
250 lines
8.2 KiB
C++
// Ceres Solver - A fast non-linear least squares minimizer
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// Copyright 2015 Google Inc. All rights reserved.
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// http://ceres-solver.org/
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//
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// Redistribution and use in source and binary forms, with or without
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// modification, are permitted provided that the following conditions are met:
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//
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// * Redistributions of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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// * Redistributions in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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// * Neither the name of Google Inc. nor the names of its contributors may be
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// used to endorse or promote products derived from this software without
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// specific prior written permission.
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//
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// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
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// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
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// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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// POSSIBILITY OF SUCH DAMAGE.
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//
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// Author: sameeragarwal@google.com (Sameer Agarwal)
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#include "ceres/compressed_col_sparse_matrix_utils.h"
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#include <algorithm>
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#include <numeric>
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#include "Eigen/SparseCore"
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#include "ceres/internal/export.h"
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#include "ceres/triplet_sparse_matrix.h"
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#include "glog/logging.h"
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#include "gtest/gtest.h"
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namespace ceres {
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namespace internal {
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using std::vector;
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TEST(_, BlockPermutationToScalarPermutation) {
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vector<int> blocks;
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// Block structure
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// 0 --1- ---2--- ---3--- 4
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// [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
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blocks.push_back(1);
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blocks.push_back(2);
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blocks.push_back(3);
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blocks.push_back(3);
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blocks.push_back(1);
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// Block ordering
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// [1, 0, 2, 4, 5]
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vector<int> block_ordering;
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block_ordering.push_back(1);
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block_ordering.push_back(0);
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block_ordering.push_back(2);
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block_ordering.push_back(4);
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block_ordering.push_back(3);
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// Expected ordering
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// [1, 2, 0, 3, 4, 5, 9, 6, 7, 8]
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vector<int> expected_scalar_ordering;
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expected_scalar_ordering.push_back(1);
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expected_scalar_ordering.push_back(2);
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expected_scalar_ordering.push_back(0);
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expected_scalar_ordering.push_back(3);
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expected_scalar_ordering.push_back(4);
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expected_scalar_ordering.push_back(5);
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expected_scalar_ordering.push_back(9);
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expected_scalar_ordering.push_back(6);
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expected_scalar_ordering.push_back(7);
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expected_scalar_ordering.push_back(8);
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vector<int> scalar_ordering;
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BlockOrderingToScalarOrdering(blocks, block_ordering, &scalar_ordering);
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EXPECT_EQ(scalar_ordering.size(), expected_scalar_ordering.size());
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for (int i = 0; i < expected_scalar_ordering.size(); ++i) {
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EXPECT_EQ(scalar_ordering[i], expected_scalar_ordering[i]);
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}
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}
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static void FillBlock(const vector<int>& row_blocks,
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const vector<int>& col_blocks,
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const int row_block_id,
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const int col_block_id,
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vector<Eigen::Triplet<double>>* triplets) {
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const int row_offset =
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std::accumulate(&row_blocks[0], &row_blocks[row_block_id], 0);
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const int col_offset =
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std::accumulate(&col_blocks[0], &col_blocks[col_block_id], 0);
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for (int r = 0; r < row_blocks[row_block_id]; ++r) {
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for (int c = 0; c < col_blocks[col_block_id]; ++c) {
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triplets->push_back(
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Eigen::Triplet<double>(row_offset + r, col_offset + c, 1.0));
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}
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}
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}
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TEST(_, ScalarMatrixToBlockMatrix) {
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// Block sparsity.
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//
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// [1 2 3 2]
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// [1] x x
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// [2] x x
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// [2] x x
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// num_nonzeros = 1 + 3 + 4 + 4 + 1 + 2 = 15
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vector<int> col_blocks;
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col_blocks.push_back(1);
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col_blocks.push_back(2);
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col_blocks.push_back(3);
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col_blocks.push_back(2);
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vector<int> row_blocks;
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row_blocks.push_back(1);
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row_blocks.push_back(2);
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row_blocks.push_back(2);
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const int num_rows =
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std::accumulate(row_blocks.begin(), row_blocks.end(), 0.0);
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const int num_cols =
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std::accumulate(col_blocks.begin(), col_blocks.end(), 0.0);
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vector<Eigen::Triplet<double>> triplets;
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FillBlock(row_blocks, col_blocks, 0, 0, &triplets);
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FillBlock(row_blocks, col_blocks, 2, 0, &triplets);
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FillBlock(row_blocks, col_blocks, 1, 1, &triplets);
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FillBlock(row_blocks, col_blocks, 2, 1, &triplets);
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FillBlock(row_blocks, col_blocks, 0, 2, &triplets);
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FillBlock(row_blocks, col_blocks, 1, 3, &triplets);
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Eigen::SparseMatrix<double> sparse_matrix(num_rows, num_cols);
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sparse_matrix.setFromTriplets(triplets.begin(), triplets.end());
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vector<int> expected_compressed_block_rows;
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expected_compressed_block_rows.push_back(0);
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expected_compressed_block_rows.push_back(2);
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expected_compressed_block_rows.push_back(1);
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expected_compressed_block_rows.push_back(2);
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expected_compressed_block_rows.push_back(0);
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expected_compressed_block_rows.push_back(1);
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vector<int> expected_compressed_block_cols;
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expected_compressed_block_cols.push_back(0);
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expected_compressed_block_cols.push_back(2);
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expected_compressed_block_cols.push_back(4);
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expected_compressed_block_cols.push_back(5);
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expected_compressed_block_cols.push_back(6);
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vector<int> compressed_block_rows;
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vector<int> compressed_block_cols;
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CompressedColumnScalarMatrixToBlockMatrix(sparse_matrix.innerIndexPtr(),
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sparse_matrix.outerIndexPtr(),
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row_blocks,
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col_blocks,
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&compressed_block_rows,
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&compressed_block_cols);
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EXPECT_EQ(compressed_block_rows, expected_compressed_block_rows);
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EXPECT_EQ(compressed_block_cols, expected_compressed_block_cols);
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}
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class SolveUpperTriangularTest : public ::testing::Test {
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protected:
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void SetUp() override {
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cols.resize(5);
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rows.resize(7);
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values.resize(7);
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cols[0] = 0;
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rows[0] = 0;
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values[0] = 0.50754;
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cols[1] = 1;
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rows[1] = 1;
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values[1] = 0.80483;
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cols[2] = 2;
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rows[2] = 1;
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values[2] = 0.14120;
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rows[3] = 2;
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values[3] = 0.3;
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cols[3] = 4;
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rows[4] = 0;
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values[4] = 0.77696;
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rows[5] = 1;
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values[5] = 0.41860;
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rows[6] = 3;
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values[6] = 0.88979;
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cols[4] = 7;
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}
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vector<int> cols;
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vector<int> rows;
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vector<double> values;
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};
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TEST_F(SolveUpperTriangularTest, SolveInPlace) {
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double rhs_and_solution[] = {1.0, 1.0, 2.0, 2.0};
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const double expected[] = {-1.4706, -1.0962, 6.6667, 2.2477};
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SolveUpperTriangularInPlace<int>(
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cols.size() - 1, &rows[0], &cols[0], &values[0], rhs_and_solution);
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for (int i = 0; i < 4; ++i) {
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EXPECT_NEAR(rhs_and_solution[i], expected[i], 1e-4) << i;
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}
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}
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TEST_F(SolveUpperTriangularTest, TransposeSolveInPlace) {
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double rhs_and_solution[] = {1.0, 1.0, 2.0, 2.0};
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double expected[] = {1.970288, 1.242498, 6.081864, -0.057255};
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SolveUpperTriangularTransposeInPlace<int>(
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cols.size() - 1, &rows[0], &cols[0], &values[0], rhs_and_solution);
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for (int i = 0; i < 4; ++i) {
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EXPECT_NEAR(rhs_and_solution[i], expected[i], 1e-4) << i;
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}
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}
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TEST_F(SolveUpperTriangularTest, RTRSolveWithSparseRHS) {
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double solution[4];
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// clang-format off
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double expected[] = { 6.8420e+00, 1.0057e+00, -1.4907e-16, -1.9335e+00,
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1.0057e+00, 2.2275e+00, -1.9493e+00, -6.5693e-01,
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-1.4907e-16, -1.9493e+00, 1.1111e+01, 9.7381e-17,
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-1.9335e+00, -6.5693e-01, 9.7381e-17, 1.2631e+00 };
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// clang-format on
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for (int i = 0; i < 4; ++i) {
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SolveRTRWithSparseRHS<int>(
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cols.size() - 1, &rows[0], &cols[0], &values[0], i, solution);
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for (int j = 0; j < 4; ++j) {
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EXPECT_NEAR(solution[j], expected[4 * i + j], 1e-3) << i;
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}
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}
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}
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} // namespace internal
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} // namespace ceres
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