mirror of
https://github.com/ceres-solver/ceres-solver.git
synced 2026-08-29 08:34:37 +08:00
Sameer Agarwal
06bfe6ffac
Since c++11, we can depend on C++ threads always being available. With the recent work on the performance of CXX threading, the additional complexity of maintaining multiple backends for some minor performance delta is not worth it https://github.com/ceres-solver/ceres-solver/issues/886 Change-Id: Idee480b22a498daec9c4366da8589aa58eaf36a1
1364 lines
43 KiB
C++
1364 lines
43 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/covariance.h"
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#include <algorithm>
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#include <cmath>
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#include <cstdint>
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#include <map>
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#include <memory>
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#include <utility>
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#include "ceres/autodiff_cost_function.h"
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#include "ceres/compressed_row_sparse_matrix.h"
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#include "ceres/cost_function.h"
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#include "ceres/covariance_impl.h"
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#include "ceres/internal/config.h"
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#include "ceres/manifold.h"
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#include "ceres/map_util.h"
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#include "ceres/problem_impl.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::make_pair;
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using std::map;
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using std::pair;
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using std::vector;
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class UnaryCostFunction : public CostFunction {
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public:
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UnaryCostFunction(const int num_residuals,
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const int32_t parameter_block_size,
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const double* jacobian)
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: jacobian_(jacobian, jacobian + num_residuals * parameter_block_size) {
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set_num_residuals(num_residuals);
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mutable_parameter_block_sizes()->push_back(parameter_block_size);
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}
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bool Evaluate(double const* const* parameters,
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double* residuals,
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double** jacobians) const final {
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for (int i = 0; i < num_residuals(); ++i) {
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residuals[i] = 1;
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}
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if (jacobians == nullptr) {
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return true;
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}
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if (jacobians[0] != nullptr) {
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std::copy(jacobian_.begin(), jacobian_.end(), jacobians[0]);
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}
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return true;
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}
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private:
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vector<double> jacobian_;
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};
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class BinaryCostFunction : public CostFunction {
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public:
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BinaryCostFunction(const int num_residuals,
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const int32_t parameter_block1_size,
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const int32_t parameter_block2_size,
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const double* jacobian1,
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const double* jacobian2)
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: jacobian1_(jacobian1,
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jacobian1 + num_residuals * parameter_block1_size),
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jacobian2_(jacobian2,
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jacobian2 + num_residuals * parameter_block2_size) {
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set_num_residuals(num_residuals);
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mutable_parameter_block_sizes()->push_back(parameter_block1_size);
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mutable_parameter_block_sizes()->push_back(parameter_block2_size);
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}
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bool Evaluate(double const* const* parameters,
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double* residuals,
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double** jacobians) const final {
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for (int i = 0; i < num_residuals(); ++i) {
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residuals[i] = 2;
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}
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if (jacobians == nullptr) {
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return true;
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}
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if (jacobians[0] != nullptr) {
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std::copy(jacobian1_.begin(), jacobian1_.end(), jacobians[0]);
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}
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if (jacobians[1] != nullptr) {
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std::copy(jacobian2_.begin(), jacobian2_.end(), jacobians[1]);
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}
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return true;
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}
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private:
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vector<double> jacobian1_;
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vector<double> jacobian2_;
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};
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TEST(CovarianceImpl, ComputeCovarianceSparsity) {
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double parameters[10];
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double* block1 = parameters;
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double* block2 = block1 + 1;
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double* block3 = block2 + 2;
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double* block4 = block3 + 3;
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ProblemImpl problem;
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// Add in random order
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Vector junk_jacobian = Vector::Zero(10);
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problem.AddResidualBlock(
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new UnaryCostFunction(1, 1, junk_jacobian.data()), nullptr, block1);
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problem.AddResidualBlock(
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new UnaryCostFunction(1, 4, junk_jacobian.data()), nullptr, block4);
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problem.AddResidualBlock(
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new UnaryCostFunction(1, 3, junk_jacobian.data()), nullptr, block3);
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problem.AddResidualBlock(
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new UnaryCostFunction(1, 2, junk_jacobian.data()), nullptr, block2);
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// Sparsity pattern
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//
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// Note that the problem structure does not imply this sparsity
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// pattern since all the residual blocks are unary. But the
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// ComputeCovarianceSparsity function in its current incarnation
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// does not pay attention to this fact and only looks at the
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// parameter block pairs that the user provides.
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//
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// X . . . . . X X X X
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// . X X X X X . . . .
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// . X X X X X . . . .
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// . . . X X X . . . .
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// . . . X X X . . . .
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// . . . X X X . . . .
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// . . . . . . X X X X
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// . . . . . . X X X X
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// . . . . . . X X X X
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// . . . . . . X X X X
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// clang-format off
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int expected_rows[] = {0, 5, 10, 15, 18, 21, 24, 28, 32, 36, 40};
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int expected_cols[] = {0, 6, 7, 8, 9,
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1, 2, 3, 4, 5,
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1, 2, 3, 4, 5,
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3, 4, 5,
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3, 4, 5,
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3, 4, 5,
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6, 7, 8, 9,
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6, 7, 8, 9,
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6, 7, 8, 9,
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6, 7, 8, 9};
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// clang-format on
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vector<pair<const double*, const double*>> covariance_blocks;
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covariance_blocks.emplace_back(block1, block1);
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covariance_blocks.emplace_back(block4, block4);
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covariance_blocks.emplace_back(block2, block2);
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covariance_blocks.emplace_back(block3, block3);
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covariance_blocks.emplace_back(block2, block3);
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covariance_blocks.emplace_back(block4, block1); // reversed
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Covariance::Options options;
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CovarianceImpl covariance_impl(options);
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EXPECT_TRUE(
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covariance_impl.ComputeCovarianceSparsity(covariance_blocks, &problem));
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const CompressedRowSparseMatrix* crsm = covariance_impl.covariance_matrix();
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EXPECT_EQ(crsm->num_rows(), 10);
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EXPECT_EQ(crsm->num_cols(), 10);
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EXPECT_EQ(crsm->num_nonzeros(), 40);
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const int* rows = crsm->rows();
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for (int r = 0; r < crsm->num_rows() + 1; ++r) {
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EXPECT_EQ(rows[r], expected_rows[r])
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<< r << " " << rows[r] << " " << expected_rows[r];
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}
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const int* cols = crsm->cols();
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for (int c = 0; c < crsm->num_nonzeros(); ++c) {
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EXPECT_EQ(cols[c], expected_cols[c])
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<< c << " " << cols[c] << " " << expected_cols[c];
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}
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}
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TEST(CovarianceImpl, ComputeCovarianceSparsityWithConstantParameterBlock) {
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double parameters[10];
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double* block1 = parameters;
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double* block2 = block1 + 1;
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double* block3 = block2 + 2;
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double* block4 = block3 + 3;
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ProblemImpl problem;
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// Add in random order
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Vector junk_jacobian = Vector::Zero(10);
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problem.AddResidualBlock(
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new UnaryCostFunction(1, 1, junk_jacobian.data()), nullptr, block1);
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problem.AddResidualBlock(
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new UnaryCostFunction(1, 4, junk_jacobian.data()), nullptr, block4);
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problem.AddResidualBlock(
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new UnaryCostFunction(1, 3, junk_jacobian.data()), nullptr, block3);
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problem.AddResidualBlock(
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new UnaryCostFunction(1, 2, junk_jacobian.data()), nullptr, block2);
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problem.SetParameterBlockConstant(block3);
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// Sparsity pattern
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//
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// Note that the problem structure does not imply this sparsity
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// pattern since all the residual blocks are unary. But the
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// ComputeCovarianceSparsity function in its current incarnation
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// does not pay attention to this fact and only looks at the
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// parameter block pairs that the user provides.
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//
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// X . . X X X X
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// . X X . . . .
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// . X X . . . .
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// . . . X X X X
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// . . . X X X X
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// . . . X X X X
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// . . . X X X X
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// clang-format off
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int expected_rows[] = {0, 5, 7, 9, 13, 17, 21, 25};
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int expected_cols[] = {0, 3, 4, 5, 6,
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1, 2,
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1, 2,
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3, 4, 5, 6,
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3, 4, 5, 6,
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3, 4, 5, 6,
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3, 4, 5, 6};
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// clang-format on
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vector<pair<const double*, const double*>> covariance_blocks;
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covariance_blocks.emplace_back(block1, block1);
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covariance_blocks.emplace_back(block4, block4);
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covariance_blocks.emplace_back(block2, block2);
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covariance_blocks.emplace_back(block3, block3);
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covariance_blocks.emplace_back(block2, block3);
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covariance_blocks.emplace_back(block4, block1); // reversed
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Covariance::Options options;
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CovarianceImpl covariance_impl(options);
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EXPECT_TRUE(
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covariance_impl.ComputeCovarianceSparsity(covariance_blocks, &problem));
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const CompressedRowSparseMatrix* crsm = covariance_impl.covariance_matrix();
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EXPECT_EQ(crsm->num_rows(), 7);
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EXPECT_EQ(crsm->num_cols(), 7);
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EXPECT_EQ(crsm->num_nonzeros(), 25);
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const int* rows = crsm->rows();
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for (int r = 0; r < crsm->num_rows() + 1; ++r) {
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EXPECT_EQ(rows[r], expected_rows[r])
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<< r << " " << rows[r] << " " << expected_rows[r];
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}
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const int* cols = crsm->cols();
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for (int c = 0; c < crsm->num_nonzeros(); ++c) {
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EXPECT_EQ(cols[c], expected_cols[c])
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<< c << " " << cols[c] << " " << expected_cols[c];
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}
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}
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TEST(CovarianceImpl, ComputeCovarianceSparsityWithFreeParameterBlock) {
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double parameters[10];
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double* block1 = parameters;
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double* block2 = block1 + 1;
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double* block3 = block2 + 2;
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double* block4 = block3 + 3;
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ProblemImpl problem;
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// Add in random order
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Vector junk_jacobian = Vector::Zero(10);
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problem.AddResidualBlock(
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new UnaryCostFunction(1, 1, junk_jacobian.data()), nullptr, block1);
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problem.AddResidualBlock(
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new UnaryCostFunction(1, 4, junk_jacobian.data()), nullptr, block4);
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problem.AddParameterBlock(block3, 3);
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problem.AddResidualBlock(
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new UnaryCostFunction(1, 2, junk_jacobian.data()), nullptr, block2);
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// Sparsity pattern
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//
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// Note that the problem structure does not imply this sparsity
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// pattern since all the residual blocks are unary. But the
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// ComputeCovarianceSparsity function in its current incarnation
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// does not pay attention to this fact and only looks at the
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// parameter block pairs that the user provides.
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//
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// X . . X X X X
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// . X X . . . .
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// . X X . . . .
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// . . . X X X X
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// . . . X X X X
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// . . . X X X X
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// . . . X X X X
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// clang-format off
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int expected_rows[] = {0, 5, 7, 9, 13, 17, 21, 25};
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int expected_cols[] = {0, 3, 4, 5, 6,
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1, 2,
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1, 2,
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3, 4, 5, 6,
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3, 4, 5, 6,
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3, 4, 5, 6,
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3, 4, 5, 6};
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// clang-format on
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vector<pair<const double*, const double*>> covariance_blocks;
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covariance_blocks.emplace_back(block1, block1);
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covariance_blocks.emplace_back(block4, block4);
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covariance_blocks.emplace_back(block2, block2);
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covariance_blocks.emplace_back(block3, block3);
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covariance_blocks.emplace_back(block2, block3);
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covariance_blocks.emplace_back(block4, block1); // reversed
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Covariance::Options options;
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CovarianceImpl covariance_impl(options);
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EXPECT_TRUE(
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covariance_impl.ComputeCovarianceSparsity(covariance_blocks, &problem));
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const CompressedRowSparseMatrix* crsm = covariance_impl.covariance_matrix();
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EXPECT_EQ(crsm->num_rows(), 7);
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EXPECT_EQ(crsm->num_cols(), 7);
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EXPECT_EQ(crsm->num_nonzeros(), 25);
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const int* rows = crsm->rows();
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for (int r = 0; r < crsm->num_rows() + 1; ++r) {
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EXPECT_EQ(rows[r], expected_rows[r])
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<< r << " " << rows[r] << " " << expected_rows[r];
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}
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const int* cols = crsm->cols();
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for (int c = 0; c < crsm->num_nonzeros(); ++c) {
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EXPECT_EQ(cols[c], expected_cols[c])
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<< c << " " << cols[c] << " " << expected_cols[c];
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}
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}
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// x_plus_delta = delta * x;
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class PolynomialManifold : public Manifold {
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public:
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bool Plus(const double* x,
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const double* delta,
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double* x_plus_delta) const final {
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x_plus_delta[0] = delta[0] * x[0];
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x_plus_delta[1] = delta[0] * x[1];
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return true;
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}
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bool Minus(const double* y, const double* x, double* y_minus_x) const final {
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LOG(FATAL) << "Should not be called";
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return true;
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}
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bool PlusJacobian(const double* x, double* jacobian) const final {
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jacobian[0] = x[0];
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jacobian[1] = x[1];
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return true;
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}
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bool MinusJacobian(const double* x, double* jacobian) const final {
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LOG(FATAL) << "Should not be called";
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return true;
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}
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int AmbientSize() const final { return 2; }
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int TangentSize() const final { return 1; }
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};
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class CovarianceTest : public ::testing::Test {
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protected:
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using BoundsMap = map<const double*, pair<int, int>>;
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void SetUp() override {
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double* x = parameters_;
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double* y = x + 2;
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double* z = y + 3;
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x[0] = 1;
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x[1] = 1;
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y[0] = 2;
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y[1] = 2;
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y[2] = 2;
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z[0] = 3;
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{
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double jacobian[] = {1.0, 0.0, 0.0, 1.0};
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problem_.AddResidualBlock(
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new UnaryCostFunction(2, 2, jacobian), nullptr, x);
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}
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{
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double jacobian[] = {2.0, 0.0, 0.0, 0.0, 2.0, 0.0, 0.0, 0.0, 2.0};
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problem_.AddResidualBlock(
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new UnaryCostFunction(3, 3, jacobian), nullptr, y);
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}
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{
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double jacobian = 5.0;
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problem_.AddResidualBlock(
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new UnaryCostFunction(1, 1, &jacobian), nullptr, z);
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}
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{
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double jacobian1[] = {1.0, 2.0, 3.0};
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double jacobian2[] = {-5.0, -6.0};
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problem_.AddResidualBlock(
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new BinaryCostFunction(1, 3, 2, jacobian1, jacobian2), nullptr, y, x);
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}
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{
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double jacobian1[] = {2.0};
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double jacobian2[] = {3.0, -2.0};
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problem_.AddResidualBlock(
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new BinaryCostFunction(1, 1, 2, jacobian1, jacobian2), nullptr, z, x);
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}
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all_covariance_blocks_.emplace_back(x, x);
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all_covariance_blocks_.emplace_back(y, y);
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all_covariance_blocks_.emplace_back(z, z);
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all_covariance_blocks_.emplace_back(x, y);
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all_covariance_blocks_.emplace_back(x, z);
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all_covariance_blocks_.emplace_back(y, z);
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column_bounds_[x] = make_pair(0, 2);
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column_bounds_[y] = make_pair(2, 5);
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column_bounds_[z] = make_pair(5, 6);
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}
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// Computes covariance in ambient space.
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void ComputeAndCompareCovarianceBlocks(const Covariance::Options& options,
|
|
const double* expected_covariance) {
|
|
ComputeAndCompareCovarianceBlocksInTangentOrAmbientSpace(
|
|
options,
|
|
true, // ambient
|
|
expected_covariance);
|
|
}
|
|
|
|
// Computes covariance in tangent space.
|
|
void ComputeAndCompareCovarianceBlocksInTangentSpace(
|
|
const Covariance::Options& options, const double* expected_covariance) {
|
|
ComputeAndCompareCovarianceBlocksInTangentOrAmbientSpace(
|
|
options,
|
|
false, // tangent
|
|
expected_covariance);
|
|
}
|
|
|
|
void ComputeAndCompareCovarianceBlocksInTangentOrAmbientSpace(
|
|
const Covariance::Options& options,
|
|
bool lift_covariance_to_ambient_space,
|
|
const double* expected_covariance) {
|
|
// Generate all possible combination of block pairs and check if the
|
|
// covariance computation is correct.
|
|
for (int i = 0; i <= 64; ++i) {
|
|
vector<pair<const double*, const double*>> covariance_blocks;
|
|
if (i & 1) {
|
|
covariance_blocks.push_back(all_covariance_blocks_[0]);
|
|
}
|
|
|
|
if (i & 2) {
|
|
covariance_blocks.push_back(all_covariance_blocks_[1]);
|
|
}
|
|
|
|
if (i & 4) {
|
|
covariance_blocks.push_back(all_covariance_blocks_[2]);
|
|
}
|
|
|
|
if (i & 8) {
|
|
covariance_blocks.push_back(all_covariance_blocks_[3]);
|
|
}
|
|
|
|
if (i & 16) {
|
|
covariance_blocks.push_back(all_covariance_blocks_[4]);
|
|
}
|
|
|
|
if (i & 32) {
|
|
covariance_blocks.push_back(all_covariance_blocks_[5]);
|
|
}
|
|
|
|
Covariance covariance(options);
|
|
EXPECT_TRUE(covariance.Compute(covariance_blocks, &problem_));
|
|
|
|
for (auto& covariance_block : covariance_blocks) {
|
|
const double* block1 = covariance_block.first;
|
|
const double* block2 = covariance_block.second;
|
|
// block1, block2
|
|
GetCovarianceBlockAndCompare(block1,
|
|
block2,
|
|
lift_covariance_to_ambient_space,
|
|
covariance,
|
|
expected_covariance);
|
|
// block2, block1
|
|
GetCovarianceBlockAndCompare(block2,
|
|
block1,
|
|
lift_covariance_to_ambient_space,
|
|
covariance,
|
|
expected_covariance);
|
|
}
|
|
}
|
|
}
|
|
|
|
void GetCovarianceBlockAndCompare(const double* block1,
|
|
const double* block2,
|
|
bool lift_covariance_to_ambient_space,
|
|
const Covariance& covariance,
|
|
const double* expected_covariance) {
|
|
const BoundsMap& column_bounds = lift_covariance_to_ambient_space
|
|
? column_bounds_
|
|
: local_column_bounds_;
|
|
const int row_begin = FindOrDie(column_bounds, block1).first;
|
|
const int row_end = FindOrDie(column_bounds, block1).second;
|
|
const int col_begin = FindOrDie(column_bounds, block2).first;
|
|
const int col_end = FindOrDie(column_bounds, block2).second;
|
|
|
|
Matrix actual(row_end - row_begin, col_end - col_begin);
|
|
if (lift_covariance_to_ambient_space) {
|
|
EXPECT_TRUE(covariance.GetCovarianceBlock(block1, block2, actual.data()));
|
|
} else {
|
|
EXPECT_TRUE(covariance.GetCovarianceBlockInTangentSpace(
|
|
block1, block2, actual.data()));
|
|
}
|
|
|
|
int dof = 0; // degrees of freedom = sum of LocalSize()s
|
|
for (const auto& bound : column_bounds) {
|
|
dof = std::max(dof, bound.second.second);
|
|
}
|
|
ConstMatrixRef expected(expected_covariance, dof, dof);
|
|
double diff_norm =
|
|
(expected.block(
|
|
row_begin, col_begin, row_end - row_begin, col_end - col_begin) -
|
|
actual)
|
|
.norm();
|
|
diff_norm /= (row_end - row_begin) * (col_end - col_begin);
|
|
|
|
const double kTolerance = 1e-5;
|
|
EXPECT_NEAR(diff_norm, 0.0, kTolerance)
|
|
<< "rows: " << row_begin << " " << row_end << " "
|
|
<< "cols: " << col_begin << " " << col_end << " "
|
|
<< "\n\n expected: \n "
|
|
<< expected.block(
|
|
row_begin, col_begin, row_end - row_begin, col_end - col_begin)
|
|
<< "\n\n actual: \n " << actual << "\n\n full expected: \n"
|
|
<< expected;
|
|
}
|
|
|
|
double parameters_[6];
|
|
Problem problem_;
|
|
vector<pair<const double*, const double*>> all_covariance_blocks_;
|
|
BoundsMap column_bounds_;
|
|
BoundsMap local_column_bounds_;
|
|
};
|
|
|
|
TEST_F(CovarianceTest, NormalBehavior) {
|
|
// J
|
|
//
|
|
// 1 0 0 0 0 0
|
|
// 0 1 0 0 0 0
|
|
// 0 0 2 0 0 0
|
|
// 0 0 0 2 0 0
|
|
// 0 0 0 0 2 0
|
|
// 0 0 0 0 0 5
|
|
// -5 -6 1 2 3 0
|
|
// 3 -2 0 0 0 2
|
|
|
|
// J'J
|
|
//
|
|
// 35 24 -5 -10 -15 6
|
|
// 24 41 -6 -12 -18 -4
|
|
// -5 -6 5 2 3 0
|
|
// -10 -12 2 8 6 0
|
|
// -15 -18 3 6 13 0
|
|
// 6 -4 0 0 0 29
|
|
|
|
// inv(J'J) computed using octave.
|
|
// clang-format off
|
|
double expected_covariance[] = {
|
|
7.0747e-02, -8.4923e-03, 1.6821e-02, 3.3643e-02, 5.0464e-02, -1.5809e-02, // NOLINT
|
|
-8.4923e-03, 8.1352e-02, 2.4758e-02, 4.9517e-02, 7.4275e-02, 1.2978e-02, // NOLINT
|
|
1.6821e-02, 2.4758e-02, 2.4904e-01, -1.9271e-03, -2.8906e-03, -6.5325e-05, // NOLINT
|
|
3.3643e-02, 4.9517e-02, -1.9271e-03, 2.4615e-01, -5.7813e-03, -1.3065e-04, // NOLINT
|
|
5.0464e-02, 7.4275e-02, -2.8906e-03, -5.7813e-03, 2.4133e-01, -1.9598e-04, // NOLINT
|
|
-1.5809e-02, 1.2978e-02, -6.5325e-05, -1.3065e-04, -1.9598e-04, 3.9544e-02, // NOLINT
|
|
};
|
|
// clang-format on
|
|
|
|
Covariance::Options options;
|
|
|
|
#ifndef CERES_NO_SUITESPARSE
|
|
options.algorithm_type = SPARSE_QR;
|
|
options.sparse_linear_algebra_library_type = SUITE_SPARSE;
|
|
ComputeAndCompareCovarianceBlocks(options, expected_covariance);
|
|
#endif
|
|
|
|
options.algorithm_type = DENSE_SVD;
|
|
ComputeAndCompareCovarianceBlocks(options, expected_covariance);
|
|
|
|
options.algorithm_type = SPARSE_QR;
|
|
options.sparse_linear_algebra_library_type = EIGEN_SPARSE;
|
|
ComputeAndCompareCovarianceBlocks(options, expected_covariance);
|
|
}
|
|
|
|
TEST_F(CovarianceTest, ThreadedNormalBehavior) {
|
|
// J
|
|
//
|
|
// 1 0 0 0 0 0
|
|
// 0 1 0 0 0 0
|
|
// 0 0 2 0 0 0
|
|
// 0 0 0 2 0 0
|
|
// 0 0 0 0 2 0
|
|
// 0 0 0 0 0 5
|
|
// -5 -6 1 2 3 0
|
|
// 3 -2 0 0 0 2
|
|
|
|
// J'J
|
|
//
|
|
// 35 24 -5 -10 -15 6
|
|
// 24 41 -6 -12 -18 -4
|
|
// -5 -6 5 2 3 0
|
|
// -10 -12 2 8 6 0
|
|
// -15 -18 3 6 13 0
|
|
// 6 -4 0 0 0 29
|
|
|
|
// inv(J'J) computed using octave.
|
|
// clang-format off
|
|
double expected_covariance[] = {
|
|
7.0747e-02, -8.4923e-03, 1.6821e-02, 3.3643e-02, 5.0464e-02, -1.5809e-02, // NOLINT
|
|
-8.4923e-03, 8.1352e-02, 2.4758e-02, 4.9517e-02, 7.4275e-02, 1.2978e-02, // NOLINT
|
|
1.6821e-02, 2.4758e-02, 2.4904e-01, -1.9271e-03, -2.8906e-03, -6.5325e-05, // NOLINT
|
|
3.3643e-02, 4.9517e-02, -1.9271e-03, 2.4615e-01, -5.7813e-03, -1.3065e-04, // NOLINT
|
|
5.0464e-02, 7.4275e-02, -2.8906e-03, -5.7813e-03, 2.4133e-01, -1.9598e-04, // NOLINT
|
|
-1.5809e-02, 1.2978e-02, -6.5325e-05, -1.3065e-04, -1.9598e-04, 3.9544e-02, // NOLINT
|
|
};
|
|
// clang-format on
|
|
|
|
Covariance::Options options;
|
|
options.num_threads = 4;
|
|
|
|
#ifndef CERES_NO_SUITESPARSE
|
|
options.algorithm_type = SPARSE_QR;
|
|
options.sparse_linear_algebra_library_type = SUITE_SPARSE;
|
|
ComputeAndCompareCovarianceBlocks(options, expected_covariance);
|
|
#endif
|
|
|
|
options.algorithm_type = DENSE_SVD;
|
|
ComputeAndCompareCovarianceBlocks(options, expected_covariance);
|
|
|
|
options.algorithm_type = SPARSE_QR;
|
|
options.sparse_linear_algebra_library_type = EIGEN_SPARSE;
|
|
ComputeAndCompareCovarianceBlocks(options, expected_covariance);
|
|
}
|
|
|
|
TEST_F(CovarianceTest, ConstantParameterBlock) {
|
|
problem_.SetParameterBlockConstant(parameters_);
|
|
|
|
// J
|
|
//
|
|
// 0 0 0 0 0 0
|
|
// 0 0 0 0 0 0
|
|
// 0 0 2 0 0 0
|
|
// 0 0 0 2 0 0
|
|
// 0 0 0 0 2 0
|
|
// 0 0 0 0 0 5
|
|
// 0 0 1 2 3 0
|
|
// 0 0 0 0 0 2
|
|
|
|
// J'J
|
|
//
|
|
// 0 0 0 0 0 0
|
|
// 0 0 0 0 0 0
|
|
// 0 0 5 2 3 0
|
|
// 0 0 2 8 6 0
|
|
// 0 0 3 6 13 0
|
|
// 0 0 0 0 0 29
|
|
|
|
// pinv(J'J) computed using octave.
|
|
// clang-format off
|
|
double expected_covariance[] = {
|
|
0, 0, 0, 0, 0, 0, // NOLINT
|
|
0, 0, 0, 0, 0, 0, // NOLINT
|
|
0, 0, 0.23611, -0.02778, -0.04167, -0.00000, // NOLINT
|
|
0, 0, -0.02778, 0.19444, -0.08333, -0.00000, // NOLINT
|
|
0, 0, -0.04167, -0.08333, 0.12500, -0.00000, // NOLINT
|
|
0, 0, -0.00000, -0.00000, -0.00000, 0.03448 // NOLINT
|
|
// clang-format on
|
|
};
|
|
|
|
Covariance::Options options;
|
|
|
|
#ifndef CERES_NO_SUITESPARSE
|
|
options.algorithm_type = SPARSE_QR;
|
|
options.sparse_linear_algebra_library_type = SUITE_SPARSE;
|
|
ComputeAndCompareCovarianceBlocks(options, expected_covariance);
|
|
#endif
|
|
|
|
options.algorithm_type = DENSE_SVD;
|
|
ComputeAndCompareCovarianceBlocks(options, expected_covariance);
|
|
|
|
options.algorithm_type = SPARSE_QR;
|
|
options.sparse_linear_algebra_library_type = EIGEN_SPARSE;
|
|
ComputeAndCompareCovarianceBlocks(options, expected_covariance);
|
|
}
|
|
|
|
TEST_F(CovarianceTest, Manifold) {
|
|
double* x = parameters_;
|
|
double* y = x + 2;
|
|
|
|
problem_.SetManifold(x, new PolynomialManifold);
|
|
|
|
vector<int> subset;
|
|
subset.push_back(2);
|
|
problem_.SetManifold(y, new SubsetManifold(3, subset));
|
|
|
|
// Raw Jacobian: J
|
|
//
|
|
// 1 0 0 0 0 0
|
|
// 0 1 0 0 0 0
|
|
// 0 0 2 0 0 0
|
|
// 0 0 0 2 0 0
|
|
// 0 0 0 0 2 0
|
|
// 0 0 0 0 0 5
|
|
// -5 -6 1 2 3 0
|
|
// 3 -2 0 0 0 2
|
|
|
|
// Local to global jacobian: A
|
|
//
|
|
// 1 0 0 0
|
|
// 1 0 0 0
|
|
// 0 1 0 0
|
|
// 0 0 1 0
|
|
// 0 0 0 0
|
|
// 0 0 0 1
|
|
|
|
// A * inv((J*A)'*(J*A)) * A'
|
|
// Computed using octave.
|
|
// clang-format off
|
|
double expected_covariance[] = {
|
|
0.01766, 0.01766, 0.02158, 0.04316, 0.00000, -0.00122,
|
|
0.01766, 0.01766, 0.02158, 0.04316, 0.00000, -0.00122,
|
|
0.02158, 0.02158, 0.24860, -0.00281, 0.00000, -0.00149,
|
|
0.04316, 0.04316, -0.00281, 0.24439, 0.00000, -0.00298,
|
|
0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000,
|
|
-0.00122, -0.00122, -0.00149, -0.00298, 0.00000, 0.03457
|
|
};
|
|
// clang-format on
|
|
|
|
Covariance::Options options;
|
|
|
|
#ifndef CERES_NO_SUITESPARSE
|
|
options.algorithm_type = SPARSE_QR;
|
|
options.sparse_linear_algebra_library_type = SUITE_SPARSE;
|
|
ComputeAndCompareCovarianceBlocks(options, expected_covariance);
|
|
#endif
|
|
|
|
options.algorithm_type = DENSE_SVD;
|
|
ComputeAndCompareCovarianceBlocks(options, expected_covariance);
|
|
|
|
options.algorithm_type = SPARSE_QR;
|
|
options.sparse_linear_algebra_library_type = EIGEN_SPARSE;
|
|
ComputeAndCompareCovarianceBlocks(options, expected_covariance);
|
|
}
|
|
|
|
TEST_F(CovarianceTest, ManifoldInTangentSpace) {
|
|
double* x = parameters_;
|
|
double* y = x + 2;
|
|
double* z = y + 3;
|
|
|
|
problem_.SetManifold(x, new PolynomialManifold);
|
|
|
|
vector<int> subset;
|
|
subset.push_back(2);
|
|
problem_.SetManifold(y, new SubsetManifold(3, subset));
|
|
|
|
local_column_bounds_[x] = make_pair(0, 1);
|
|
local_column_bounds_[y] = make_pair(1, 3);
|
|
local_column_bounds_[z] = make_pair(3, 4);
|
|
|
|
// Raw Jacobian: J
|
|
//
|
|
// 1 0 0 0 0 0
|
|
// 0 1 0 0 0 0
|
|
// 0 0 2 0 0 0
|
|
// 0 0 0 2 0 0
|
|
// 0 0 0 0 2 0
|
|
// 0 0 0 0 0 5
|
|
// -5 -6 1 2 3 0
|
|
// 3 -2 0 0 0 2
|
|
|
|
// Local to global jacobian: A
|
|
//
|
|
// 1 0 0 0
|
|
// 1 0 0 0
|
|
// 0 1 0 0
|
|
// 0 0 1 0
|
|
// 0 0 0 0
|
|
// 0 0 0 1
|
|
|
|
// inv((J*A)'*(J*A))
|
|
// Computed using octave.
|
|
// clang-format off
|
|
double expected_covariance[] = {
|
|
0.01766, 0.02158, 0.04316, -0.00122,
|
|
0.02158, 0.24860, -0.00281, -0.00149,
|
|
0.04316, -0.00281, 0.24439, -0.00298,
|
|
-0.00122, -0.00149, -0.00298, 0.03457 // NOLINT
|
|
};
|
|
// clang-format on
|
|
|
|
Covariance::Options options;
|
|
|
|
#ifndef CERES_NO_SUITESPARSE
|
|
options.algorithm_type = SPARSE_QR;
|
|
options.sparse_linear_algebra_library_type = SUITE_SPARSE;
|
|
|
|
ComputeAndCompareCovarianceBlocksInTangentSpace(options, expected_covariance);
|
|
#endif
|
|
|
|
options.algorithm_type = DENSE_SVD;
|
|
ComputeAndCompareCovarianceBlocksInTangentSpace(options, expected_covariance);
|
|
|
|
options.algorithm_type = SPARSE_QR;
|
|
options.sparse_linear_algebra_library_type = EIGEN_SPARSE;
|
|
ComputeAndCompareCovarianceBlocksInTangentSpace(options, expected_covariance);
|
|
}
|
|
|
|
TEST_F(CovarianceTest, ManifoldInTangentSpaceWithConstantBlocks) {
|
|
double* x = parameters_;
|
|
double* y = x + 2;
|
|
double* z = y + 3;
|
|
|
|
problem_.SetManifold(x, new PolynomialManifold);
|
|
problem_.SetParameterBlockConstant(x);
|
|
|
|
vector<int> subset;
|
|
subset.push_back(2);
|
|
problem_.SetManifold(y, new SubsetManifold(3, subset));
|
|
problem_.SetParameterBlockConstant(y);
|
|
|
|
local_column_bounds_[x] = make_pair(0, 1);
|
|
local_column_bounds_[y] = make_pair(1, 3);
|
|
local_column_bounds_[z] = make_pair(3, 4);
|
|
|
|
// Raw Jacobian: J
|
|
//
|
|
// 1 0 0 0 0 0
|
|
// 0 1 0 0 0 0
|
|
// 0 0 2 0 0 0
|
|
// 0 0 0 2 0 0
|
|
// 0 0 0 0 2 0
|
|
// 0 0 0 0 0 5
|
|
// -5 -6 1 2 3 0
|
|
// 3 -2 0 0 0 2
|
|
|
|
// Local to global jacobian: A
|
|
//
|
|
// 0 0 0 0
|
|
// 0 0 0 0
|
|
// 0 0 0 0
|
|
// 0 0 0 0
|
|
// 0 0 0 0
|
|
// 0 0 0 1
|
|
|
|
// pinv((J*A)'*(J*A))
|
|
// Computed using octave.
|
|
// clang-format off
|
|
double expected_covariance[] = {
|
|
0.0, 0.0, 0.0, 0.0,
|
|
0.0, 0.0, 0.0, 0.0,
|
|
0.0, 0.0, 0.0, 0.0,
|
|
0.0, 0.0, 0.0, 0.034482 // NOLINT
|
|
};
|
|
// clang-format on
|
|
|
|
Covariance::Options options;
|
|
|
|
#ifndef CERES_NO_SUITESPARSE
|
|
options.algorithm_type = SPARSE_QR;
|
|
options.sparse_linear_algebra_library_type = SUITE_SPARSE;
|
|
|
|
ComputeAndCompareCovarianceBlocksInTangentSpace(options, expected_covariance);
|
|
#endif
|
|
|
|
options.algorithm_type = DENSE_SVD;
|
|
ComputeAndCompareCovarianceBlocksInTangentSpace(options, expected_covariance);
|
|
|
|
options.algorithm_type = SPARSE_QR;
|
|
options.sparse_linear_algebra_library_type = EIGEN_SPARSE;
|
|
ComputeAndCompareCovarianceBlocksInTangentSpace(options, expected_covariance);
|
|
}
|
|
|
|
TEST_F(CovarianceTest, TruncatedRank) {
|
|
// J
|
|
//
|
|
// 1 0 0 0 0 0
|
|
// 0 1 0 0 0 0
|
|
// 0 0 2 0 0 0
|
|
// 0 0 0 2 0 0
|
|
// 0 0 0 0 2 0
|
|
// 0 0 0 0 0 5
|
|
// -5 -6 1 2 3 0
|
|
// 3 -2 0 0 0 2
|
|
|
|
// J'J
|
|
//
|
|
// 35 24 -5 -10 -15 6
|
|
// 24 41 -6 -12 -18 -4
|
|
// -5 -6 5 2 3 0
|
|
// -10 -12 2 8 6 0
|
|
// -15 -18 3 6 13 0
|
|
// 6 -4 0 0 0 29
|
|
|
|
// 3.4142 is the smallest eigenvalue of J'J. The following matrix
|
|
// was obtained by dropping the eigenvector corresponding to this
|
|
// eigenvalue.
|
|
// clang-format off
|
|
double expected_covariance[] = {
|
|
5.4135e-02, -3.5121e-02, 1.7257e-04, 3.4514e-04, 5.1771e-04, -1.6076e-02, // NOLINT
|
|
-3.5121e-02, 3.8667e-02, -1.9288e-03, -3.8576e-03, -5.7864e-03, 1.2549e-02, // NOLINT
|
|
1.7257e-04, -1.9288e-03, 2.3235e-01, -3.5297e-02, -5.2946e-02, -3.3329e-04, // NOLINT
|
|
3.4514e-04, -3.8576e-03, -3.5297e-02, 1.7941e-01, -1.0589e-01, -6.6659e-04, // NOLINT
|
|
5.1771e-04, -5.7864e-03, -5.2946e-02, -1.0589e-01, 9.1162e-02, -9.9988e-04, // NOLINT
|
|
-1.6076e-02, 1.2549e-02, -3.3329e-04, -6.6659e-04, -9.9988e-04, 3.9539e-02 // NOLINT
|
|
};
|
|
// clang-format on
|
|
|
|
{
|
|
Covariance::Options options;
|
|
options.algorithm_type = DENSE_SVD;
|
|
// Force dropping of the smallest eigenvector.
|
|
options.null_space_rank = 1;
|
|
ComputeAndCompareCovarianceBlocks(options, expected_covariance);
|
|
}
|
|
|
|
{
|
|
Covariance::Options options;
|
|
options.algorithm_type = DENSE_SVD;
|
|
// Force dropping of the smallest eigenvector via the ratio but
|
|
// automatic truncation.
|
|
options.min_reciprocal_condition_number = 0.044494;
|
|
options.null_space_rank = -1;
|
|
ComputeAndCompareCovarianceBlocks(options, expected_covariance);
|
|
}
|
|
}
|
|
|
|
TEST_F(CovarianceTest, DenseCovarianceMatrixFromSetOfParameters) {
|
|
Covariance::Options options;
|
|
Covariance covariance(options);
|
|
double* x = parameters_;
|
|
double* y = x + 2;
|
|
double* z = y + 3;
|
|
vector<const double*> parameter_blocks;
|
|
parameter_blocks.push_back(x);
|
|
parameter_blocks.push_back(y);
|
|
parameter_blocks.push_back(z);
|
|
covariance.Compute(parameter_blocks, &problem_);
|
|
double expected_covariance[36];
|
|
covariance.GetCovarianceMatrix(parameter_blocks, expected_covariance);
|
|
|
|
#ifndef CERES_NO_SUITESPARSE
|
|
options.algorithm_type = SPARSE_QR;
|
|
options.sparse_linear_algebra_library_type = SUITE_SPARSE;
|
|
ComputeAndCompareCovarianceBlocks(options, expected_covariance);
|
|
#endif
|
|
|
|
options.algorithm_type = DENSE_SVD;
|
|
ComputeAndCompareCovarianceBlocks(options, expected_covariance);
|
|
|
|
options.algorithm_type = SPARSE_QR;
|
|
options.sparse_linear_algebra_library_type = EIGEN_SPARSE;
|
|
ComputeAndCompareCovarianceBlocks(options, expected_covariance);
|
|
}
|
|
|
|
TEST_F(CovarianceTest, DenseCovarianceMatrixFromSetOfParametersThreaded) {
|
|
Covariance::Options options;
|
|
options.num_threads = 4;
|
|
Covariance covariance(options);
|
|
double* x = parameters_;
|
|
double* y = x + 2;
|
|
double* z = y + 3;
|
|
vector<const double*> parameter_blocks;
|
|
parameter_blocks.push_back(x);
|
|
parameter_blocks.push_back(y);
|
|
parameter_blocks.push_back(z);
|
|
covariance.Compute(parameter_blocks, &problem_);
|
|
double expected_covariance[36];
|
|
covariance.GetCovarianceMatrix(parameter_blocks, expected_covariance);
|
|
|
|
#ifndef CERES_NO_SUITESPARSE
|
|
options.algorithm_type = SPARSE_QR;
|
|
options.sparse_linear_algebra_library_type = SUITE_SPARSE;
|
|
ComputeAndCompareCovarianceBlocks(options, expected_covariance);
|
|
#endif
|
|
|
|
options.algorithm_type = DENSE_SVD;
|
|
ComputeAndCompareCovarianceBlocks(options, expected_covariance);
|
|
|
|
options.algorithm_type = SPARSE_QR;
|
|
options.sparse_linear_algebra_library_type = EIGEN_SPARSE;
|
|
ComputeAndCompareCovarianceBlocks(options, expected_covariance);
|
|
}
|
|
|
|
TEST_F(CovarianceTest, DenseCovarianceMatrixFromSetOfParametersInTangentSpace) {
|
|
Covariance::Options options;
|
|
Covariance covariance(options);
|
|
double* x = parameters_;
|
|
double* y = x + 2;
|
|
double* z = y + 3;
|
|
|
|
problem_.SetManifold(x, new PolynomialManifold);
|
|
|
|
vector<int> subset;
|
|
subset.push_back(2);
|
|
problem_.SetManifold(y, new SubsetManifold(3, subset));
|
|
|
|
local_column_bounds_[x] = make_pair(0, 1);
|
|
local_column_bounds_[y] = make_pair(1, 3);
|
|
local_column_bounds_[z] = make_pair(3, 4);
|
|
|
|
vector<const double*> parameter_blocks;
|
|
parameter_blocks.push_back(x);
|
|
parameter_blocks.push_back(y);
|
|
parameter_blocks.push_back(z);
|
|
covariance.Compute(parameter_blocks, &problem_);
|
|
double expected_covariance[16];
|
|
covariance.GetCovarianceMatrixInTangentSpace(parameter_blocks,
|
|
expected_covariance);
|
|
|
|
#ifndef CERES_NO_SUITESPARSE
|
|
options.algorithm_type = SPARSE_QR;
|
|
options.sparse_linear_algebra_library_type = SUITE_SPARSE;
|
|
|
|
ComputeAndCompareCovarianceBlocksInTangentSpace(options, expected_covariance);
|
|
#endif
|
|
|
|
options.algorithm_type = DENSE_SVD;
|
|
ComputeAndCompareCovarianceBlocksInTangentSpace(options, expected_covariance);
|
|
|
|
options.algorithm_type = SPARSE_QR;
|
|
options.sparse_linear_algebra_library_type = EIGEN_SPARSE;
|
|
ComputeAndCompareCovarianceBlocksInTangentSpace(options, expected_covariance);
|
|
}
|
|
|
|
TEST_F(CovarianceTest, ComputeCovarianceFailure) {
|
|
Covariance::Options options;
|
|
Covariance covariance(options);
|
|
double* x = parameters_;
|
|
double* y = x + 2;
|
|
vector<const double*> parameter_blocks;
|
|
parameter_blocks.push_back(x);
|
|
parameter_blocks.push_back(x);
|
|
parameter_blocks.push_back(y);
|
|
parameter_blocks.push_back(y);
|
|
EXPECT_DEATH_IF_SUPPORTED(covariance.Compute(parameter_blocks, &problem_),
|
|
"Covariance::Compute called with duplicate blocks "
|
|
"at indices \\(0, 1\\) and \\(2, 3\\)");
|
|
vector<pair<const double*, const double*>> covariance_blocks;
|
|
covariance_blocks.emplace_back(x, x);
|
|
covariance_blocks.emplace_back(x, x);
|
|
covariance_blocks.emplace_back(y, y);
|
|
covariance_blocks.emplace_back(y, y);
|
|
EXPECT_DEATH_IF_SUPPORTED(covariance.Compute(covariance_blocks, &problem_),
|
|
"Covariance::Compute called with duplicate blocks "
|
|
"at indices \\(0, 1\\) and \\(2, 3\\)");
|
|
}
|
|
|
|
class RankDeficientCovarianceTest : public CovarianceTest {
|
|
protected:
|
|
void SetUp() final {
|
|
double* x = parameters_;
|
|
double* y = x + 2;
|
|
double* z = y + 3;
|
|
|
|
{
|
|
double jacobian[] = {1.0, 0.0, 0.0, 1.0};
|
|
problem_.AddResidualBlock(
|
|
new UnaryCostFunction(2, 2, jacobian), nullptr, x);
|
|
}
|
|
|
|
{
|
|
double jacobian[] = {0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0};
|
|
problem_.AddResidualBlock(
|
|
new UnaryCostFunction(3, 3, jacobian), nullptr, y);
|
|
}
|
|
|
|
{
|
|
double jacobian = 5.0;
|
|
problem_.AddResidualBlock(
|
|
new UnaryCostFunction(1, 1, &jacobian), nullptr, z);
|
|
}
|
|
|
|
{
|
|
double jacobian1[] = {0.0, 0.0, 0.0};
|
|
double jacobian2[] = {-5.0, -6.0};
|
|
problem_.AddResidualBlock(
|
|
new BinaryCostFunction(1, 3, 2, jacobian1, jacobian2), nullptr, y, x);
|
|
}
|
|
|
|
{
|
|
double jacobian1[] = {2.0};
|
|
double jacobian2[] = {3.0, -2.0};
|
|
problem_.AddResidualBlock(
|
|
new BinaryCostFunction(1, 1, 2, jacobian1, jacobian2), nullptr, z, x);
|
|
}
|
|
|
|
all_covariance_blocks_.emplace_back(x, x);
|
|
all_covariance_blocks_.emplace_back(y, y);
|
|
all_covariance_blocks_.emplace_back(z, z);
|
|
all_covariance_blocks_.emplace_back(x, y);
|
|
all_covariance_blocks_.emplace_back(x, z);
|
|
all_covariance_blocks_.emplace_back(y, z);
|
|
|
|
column_bounds_[x] = make_pair(0, 2);
|
|
column_bounds_[y] = make_pair(2, 5);
|
|
column_bounds_[z] = make_pair(5, 6);
|
|
}
|
|
};
|
|
|
|
TEST_F(RankDeficientCovarianceTest, AutomaticTruncation) {
|
|
// J
|
|
//
|
|
// 1 0 0 0 0 0
|
|
// 0 1 0 0 0 0
|
|
// 0 0 0 0 0 0
|
|
// 0 0 0 0 0 0
|
|
// 0 0 0 0 0 0
|
|
// 0 0 0 0 0 5
|
|
// -5 -6 0 0 0 0
|
|
// 3 -2 0 0 0 2
|
|
|
|
// J'J
|
|
//
|
|
// 35 24 0 0 0 6
|
|
// 24 41 0 0 0 -4
|
|
// 0 0 0 0 0 0
|
|
// 0 0 0 0 0 0
|
|
// 0 0 0 0 0 0
|
|
// 6 -4 0 0 0 29
|
|
|
|
// pinv(J'J) computed using octave.
|
|
// clang-format off
|
|
double expected_covariance[] = {
|
|
0.053998, -0.033145, 0.000000, 0.000000, 0.000000, -0.015744,
|
|
-0.033145, 0.045067, 0.000000, 0.000000, 0.000000, 0.013074,
|
|
0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
|
|
0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
|
|
0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
|
|
-0.015744, 0.013074, 0.000000, 0.000000, 0.000000, 0.039543
|
|
};
|
|
// clang-format on
|
|
|
|
Covariance::Options options;
|
|
options.algorithm_type = DENSE_SVD;
|
|
options.null_space_rank = -1;
|
|
ComputeAndCompareCovarianceBlocks(options, expected_covariance);
|
|
}
|
|
|
|
struct LinearCostFunction {
|
|
template <typename T>
|
|
bool operator()(const T* x, const T* y, T* residual) const {
|
|
residual[0] = T(10.0) - *x;
|
|
residual[1] = T(5.0) - *y;
|
|
return true;
|
|
}
|
|
static CostFunction* Create() {
|
|
return new AutoDiffCostFunction<LinearCostFunction, 2, 1, 1>(
|
|
new LinearCostFunction);
|
|
}
|
|
};
|
|
|
|
TEST(Covariance, ZeroSizedManifoldGetCovariance) {
|
|
double x = 0.0;
|
|
double y = 1.0;
|
|
Problem problem;
|
|
problem.AddResidualBlock(LinearCostFunction::Create(), nullptr, &x, &y);
|
|
problem.SetManifold(&y, new SubsetManifold(1, {0}));
|
|
// J = [-1 0]
|
|
// [ 0 0]
|
|
Covariance::Options options;
|
|
options.algorithm_type = DENSE_SVD;
|
|
Covariance covariance(options);
|
|
vector<pair<const double*, const double*>> covariance_blocks;
|
|
covariance_blocks.emplace_back(&x, &x);
|
|
covariance_blocks.emplace_back(&x, &y);
|
|
covariance_blocks.emplace_back(&y, &x);
|
|
covariance_blocks.emplace_back(&y, &y);
|
|
EXPECT_TRUE(covariance.Compute(covariance_blocks, &problem));
|
|
|
|
double value = -1;
|
|
covariance.GetCovarianceBlock(&x, &x, &value);
|
|
EXPECT_NEAR(value, 1.0, std::numeric_limits<double>::epsilon());
|
|
|
|
value = -1;
|
|
covariance.GetCovarianceBlock(&x, &y, &value);
|
|
EXPECT_NEAR(value, 0.0, std::numeric_limits<double>::epsilon());
|
|
|
|
value = -1;
|
|
covariance.GetCovarianceBlock(&y, &x, &value);
|
|
EXPECT_NEAR(value, 0.0, std::numeric_limits<double>::epsilon());
|
|
|
|
value = -1;
|
|
covariance.GetCovarianceBlock(&y, &y, &value);
|
|
EXPECT_NEAR(value, 0.0, std::numeric_limits<double>::epsilon());
|
|
}
|
|
|
|
TEST(Covariance, ZeroSizedManifoldGetCovarianceInTangentSpace) {
|
|
double x = 0.0;
|
|
double y = 1.0;
|
|
Problem problem;
|
|
problem.AddResidualBlock(LinearCostFunction::Create(), nullptr, &x, &y);
|
|
problem.SetManifold(&y, new SubsetManifold(1, {0}));
|
|
// J = [-1 0]
|
|
// [ 0 0]
|
|
Covariance::Options options;
|
|
options.algorithm_type = DENSE_SVD;
|
|
Covariance covariance(options);
|
|
vector<pair<const double*, const double*>> covariance_blocks;
|
|
covariance_blocks.emplace_back(&x, &x);
|
|
covariance_blocks.emplace_back(&x, &y);
|
|
covariance_blocks.emplace_back(&y, &x);
|
|
covariance_blocks.emplace_back(&y, &y);
|
|
EXPECT_TRUE(covariance.Compute(covariance_blocks, &problem));
|
|
|
|
double value = -1;
|
|
covariance.GetCovarianceBlockInTangentSpace(&x, &x, &value);
|
|
EXPECT_NEAR(value, 1.0, std::numeric_limits<double>::epsilon());
|
|
|
|
value = -1;
|
|
// The following three calls, should not touch this value, since the
|
|
// tangent space is of size zero
|
|
covariance.GetCovarianceBlockInTangentSpace(&x, &y, &value);
|
|
EXPECT_EQ(value, -1);
|
|
covariance.GetCovarianceBlockInTangentSpace(&y, &x, &value);
|
|
EXPECT_EQ(value, -1);
|
|
covariance.GetCovarianceBlockInTangentSpace(&y, &y, &value);
|
|
EXPECT_EQ(value, -1);
|
|
}
|
|
|
|
class LargeScaleCovarianceTest : public ::testing::Test {
|
|
protected:
|
|
void SetUp() final {
|
|
num_parameter_blocks_ = 2000;
|
|
parameter_block_size_ = 5;
|
|
parameters_ = std::make_unique<double[]>(parameter_block_size_ *
|
|
num_parameter_blocks_);
|
|
|
|
Matrix jacobian(parameter_block_size_, parameter_block_size_);
|
|
for (int i = 0; i < num_parameter_blocks_; ++i) {
|
|
jacobian.setIdentity();
|
|
jacobian *= (i + 1);
|
|
|
|
double* block_i = parameters_.get() + i * parameter_block_size_;
|
|
problem_.AddResidualBlock(
|
|
new UnaryCostFunction(
|
|
parameter_block_size_, parameter_block_size_, jacobian.data()),
|
|
nullptr,
|
|
block_i);
|
|
for (int j = i; j < num_parameter_blocks_; ++j) {
|
|
double* block_j = parameters_.get() + j * parameter_block_size_;
|
|
all_covariance_blocks_.emplace_back(block_i, block_j);
|
|
}
|
|
}
|
|
}
|
|
|
|
void ComputeAndCompare(
|
|
CovarianceAlgorithmType algorithm_type,
|
|
SparseLinearAlgebraLibraryType sparse_linear_algebra_library_type,
|
|
int num_threads) {
|
|
Covariance::Options options;
|
|
options.algorithm_type = algorithm_type;
|
|
options.sparse_linear_algebra_library_type =
|
|
sparse_linear_algebra_library_type;
|
|
options.num_threads = num_threads;
|
|
Covariance covariance(options);
|
|
EXPECT_TRUE(covariance.Compute(all_covariance_blocks_, &problem_));
|
|
|
|
Matrix expected(parameter_block_size_, parameter_block_size_);
|
|
Matrix actual(parameter_block_size_, parameter_block_size_);
|
|
const double kTolerance = 1e-16;
|
|
|
|
for (int i = 0; i < num_parameter_blocks_; ++i) {
|
|
expected.setIdentity();
|
|
expected /= (i + 1.0) * (i + 1.0);
|
|
|
|
double* block_i = parameters_.get() + i * parameter_block_size_;
|
|
covariance.GetCovarianceBlock(block_i, block_i, actual.data());
|
|
EXPECT_NEAR((expected - actual).norm(), 0.0, kTolerance)
|
|
<< "block: " << i << ", " << i << "\n"
|
|
<< "expected: \n"
|
|
<< expected << "\n"
|
|
<< "actual: \n"
|
|
<< actual;
|
|
|
|
expected.setZero();
|
|
for (int j = i + 1; j < num_parameter_blocks_; ++j) {
|
|
double* block_j = parameters_.get() + j * parameter_block_size_;
|
|
covariance.GetCovarianceBlock(block_i, block_j, actual.data());
|
|
EXPECT_NEAR((expected - actual).norm(), 0.0, kTolerance)
|
|
<< "block: " << i << ", " << j << "\n"
|
|
<< "expected: \n"
|
|
<< expected << "\n"
|
|
<< "actual: \n"
|
|
<< actual;
|
|
}
|
|
}
|
|
}
|
|
|
|
std::unique_ptr<double[]> parameters_;
|
|
int parameter_block_size_;
|
|
int num_parameter_blocks_;
|
|
|
|
Problem problem_;
|
|
vector<pair<const double*, const double*>> all_covariance_blocks_;
|
|
};
|
|
|
|
#if !defined(CERES_NO_SUITESPARSE)
|
|
|
|
TEST_F(LargeScaleCovarianceTest, Parallel) {
|
|
ComputeAndCompare(SPARSE_QR, SUITE_SPARSE, 4);
|
|
}
|
|
|
|
#endif // !defined(CERES_NO_SUITESPARSE)
|
|
|
|
} // namespace internal
|
|
} // namespace ceres
|