mirror of
https://github.com/ceres-solver/ceres-solver.git
synced 2026-08-29 16:40:38 +08:00
Sergiu Deitsch
91773746be
If arguments are passed to a cost function that can be used to construct the functor, the latter will be instantiated by the cost function using std::make_unique to ensure exception safety. This not only avoids static analysis warnings caused by calling new but also spelling the cost functor type name multiple times. Also expand deduction guides for instantiating Dynamic(Auto|Numeric)DiffCostFunction from std::unique_ptr enabled constructor overloads. Finally, make CostFunction default move constructible and assignable but only through derived classes. This in turn allows derived classes to be movable without relying on custom implementations of corresponding operators. Change-Id: Idee8b9871d862bc9f9f8b5a8d0bedc52863e93c0
532 lines
17 KiB
C++
532 lines
17 KiB
C++
// Ceres Solver - A fast non-linear least squares minimizer
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// Copyright 2024 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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// mierle@gmail.com (Keir Mierle)
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#include "ceres/dynamic_numeric_diff_cost_function.h"
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#include <cstddef>
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#include <memory>
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#include <vector>
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#include "gtest/gtest.h"
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namespace ceres::internal {
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const double kTolerance = 1e-6;
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// Takes 2 parameter blocks:
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// parameters[0] is size 10.
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// parameters[1] is size 5.
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// Emits 21 residuals:
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// A: i - parameters[0][i], for i in [0,10) -- this is 10 residuals
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// B: parameters[0][i] - i, for i in [0,10) -- this is another 10.
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// C: sum(parameters[0][i]^2 - 8*parameters[0][i]) + sum(parameters[1][i])
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class MyCostFunctor {
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public:
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bool operator()(double const* const* parameters, double* residuals) const {
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const double* params0 = parameters[0];
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int r = 0;
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for (int i = 0; i < 10; ++i) {
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residuals[r++] = i - params0[i];
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residuals[r++] = params0[i] - i;
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}
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double c_residual = 0.0;
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for (int i = 0; i < 10; ++i) {
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c_residual += pow(params0[i], 2) - 8.0 * params0[i];
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}
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const double* params1 = parameters[1];
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for (int i = 0; i < 5; ++i) {
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c_residual += params1[i];
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}
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residuals[r++] = c_residual;
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return true;
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}
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};
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TEST(DynamicNumericdiffCostFunctionTest, TestResiduals) {
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std::vector<double> param_block_0(10, 0.0);
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std::vector<double> param_block_1(5, 0.0);
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DynamicNumericDiffCostFunction<MyCostFunctor> cost_function(
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new MyCostFunctor());
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cost_function.AddParameterBlock(param_block_0.size());
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cost_function.AddParameterBlock(param_block_1.size());
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cost_function.SetNumResiduals(21);
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// Test residual computation.
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std::vector<double> residuals(21, -100000);
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std::vector<double*> parameter_blocks(2);
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parameter_blocks[0] = ¶m_block_0[0];
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parameter_blocks[1] = ¶m_block_1[0];
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EXPECT_TRUE(
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cost_function.Evaluate(¶meter_blocks[0], residuals.data(), nullptr));
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for (int r = 0; r < 10; ++r) {
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EXPECT_EQ(1.0 * r, residuals.at(r * 2));
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EXPECT_EQ(-1.0 * r, residuals.at(r * 2 + 1));
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}
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EXPECT_EQ(0, residuals.at(20));
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}
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TEST(DynamicNumericdiffCostFunctionTest, TestJacobian) {
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// Test the residual counting.
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std::vector<double> param_block_0(10, 0.0);
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for (int i = 0; i < 10; ++i) {
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param_block_0[i] = 2 * i;
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}
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std::vector<double> param_block_1(5, 0.0);
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DynamicNumericDiffCostFunction<MyCostFunctor> cost_function(
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new MyCostFunctor());
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cost_function.AddParameterBlock(param_block_0.size());
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cost_function.AddParameterBlock(param_block_1.size());
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cost_function.SetNumResiduals(21);
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// Prepare the residuals.
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std::vector<double> residuals(21, -100000);
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// Prepare the parameters.
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std::vector<double*> parameter_blocks(2);
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parameter_blocks[0] = ¶m_block_0[0];
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parameter_blocks[1] = ¶m_block_1[0];
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// Prepare the jacobian.
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std::vector<std::vector<double>> jacobian_vect(2);
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jacobian_vect[0].resize(21 * 10, -100000);
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jacobian_vect[1].resize(21 * 5, -100000);
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std::vector<double*> jacobian;
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jacobian.push_back(jacobian_vect[0].data());
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jacobian.push_back(jacobian_vect[1].data());
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// Test jacobian computation.
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EXPECT_TRUE(cost_function.Evaluate(
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parameter_blocks.data(), residuals.data(), jacobian.data()));
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for (int r = 0; r < 10; ++r) {
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EXPECT_EQ(-1.0 * r, residuals.at(r * 2));
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EXPECT_EQ(+1.0 * r, residuals.at(r * 2 + 1));
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}
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EXPECT_EQ(420, residuals.at(20));
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for (int p = 0; p < 10; ++p) {
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// Check "A" Jacobian.
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EXPECT_NEAR(-1.0, jacobian_vect[0][2 * p * 10 + p], kTolerance);
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// Check "B" Jacobian.
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EXPECT_NEAR(+1.0, jacobian_vect[0][(2 * p + 1) * 10 + p], kTolerance);
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jacobian_vect[0][2 * p * 10 + p] = 0.0;
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jacobian_vect[0][(2 * p + 1) * 10 + p] = 0.0;
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}
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// Check "C" Jacobian for first parameter block.
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for (int p = 0; p < 10; ++p) {
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EXPECT_NEAR(4 * p - 8, jacobian_vect[0][20 * 10 + p], kTolerance);
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jacobian_vect[0][20 * 10 + p] = 0.0;
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}
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for (double entry : jacobian_vect[0]) {
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EXPECT_NEAR(0.0, entry, kTolerance);
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}
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// Check "C" Jacobian for second parameter block.
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for (int p = 0; p < 5; ++p) {
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EXPECT_NEAR(1.0, jacobian_vect[1][20 * 5 + p], kTolerance);
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jacobian_vect[1][20 * 5 + p] = 0.0;
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}
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for (double entry : jacobian_vect[1]) {
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EXPECT_NEAR(0.0, entry, kTolerance);
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}
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}
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TEST(DynamicNumericdiffCostFunctionTest,
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JacobianWithFirstParameterBlockConstant) { // NOLINT
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// Test the residual counting.
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std::vector<double> param_block_0(10, 0.0);
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for (int i = 0; i < 10; ++i) {
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param_block_0[i] = 2 * i;
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}
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std::vector<double> param_block_1(5, 0.0);
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DynamicNumericDiffCostFunction<MyCostFunctor> cost_function(
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new MyCostFunctor());
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cost_function.AddParameterBlock(param_block_0.size());
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cost_function.AddParameterBlock(param_block_1.size());
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cost_function.SetNumResiduals(21);
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// Prepare the residuals.
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std::vector<double> residuals(21, -100000);
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// Prepare the parameters.
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std::vector<double*> parameter_blocks(2);
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parameter_blocks[0] = ¶m_block_0[0];
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parameter_blocks[1] = ¶m_block_1[0];
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// Prepare the jacobian.
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std::vector<std::vector<double>> jacobian_vect(2);
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jacobian_vect[0].resize(21 * 10, -100000);
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jacobian_vect[1].resize(21 * 5, -100000);
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std::vector<double*> jacobian;
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jacobian.push_back(nullptr);
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jacobian.push_back(jacobian_vect[1].data());
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// Test jacobian computation.
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EXPECT_TRUE(cost_function.Evaluate(
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parameter_blocks.data(), residuals.data(), jacobian.data()));
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for (int r = 0; r < 10; ++r) {
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EXPECT_EQ(-1.0 * r, residuals.at(r * 2));
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EXPECT_EQ(+1.0 * r, residuals.at(r * 2 + 1));
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}
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EXPECT_EQ(420, residuals.at(20));
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// Check "C" Jacobian for second parameter block.
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for (int p = 0; p < 5; ++p) {
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EXPECT_NEAR(1.0, jacobian_vect[1][20 * 5 + p], kTolerance);
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jacobian_vect[1][20 * 5 + p] = 0.0;
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}
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for (double& i : jacobian_vect[1]) {
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EXPECT_EQ(0.0, i);
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}
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}
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TEST(DynamicNumericdiffCostFunctionTest,
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JacobianWithSecondParameterBlockConstant) { // NOLINT
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// Test the residual counting.
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std::vector<double> param_block_0(10, 0.0);
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for (int i = 0; i < 10; ++i) {
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param_block_0[i] = 2 * i;
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}
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std::vector<double> param_block_1(5, 0.0);
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DynamicNumericDiffCostFunction<MyCostFunctor> cost_function(
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new MyCostFunctor());
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cost_function.AddParameterBlock(param_block_0.size());
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cost_function.AddParameterBlock(param_block_1.size());
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cost_function.SetNumResiduals(21);
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// Prepare the residuals.
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std::vector<double> residuals(21, -100000);
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// Prepare the parameters.
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std::vector<double*> parameter_blocks(2);
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parameter_blocks[0] = ¶m_block_0[0];
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parameter_blocks[1] = ¶m_block_1[0];
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// Prepare the jacobian.
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std::vector<std::vector<double>> jacobian_vect(2);
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jacobian_vect[0].resize(21 * 10, -100000);
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jacobian_vect[1].resize(21 * 5, -100000);
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std::vector<double*> jacobian;
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jacobian.push_back(jacobian_vect[0].data());
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jacobian.push_back(nullptr);
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// Test jacobian computation.
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EXPECT_TRUE(cost_function.Evaluate(
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parameter_blocks.data(), residuals.data(), jacobian.data()));
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for (int r = 0; r < 10; ++r) {
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EXPECT_EQ(-1.0 * r, residuals.at(r * 2));
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EXPECT_EQ(+1.0 * r, residuals.at(r * 2 + 1));
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}
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EXPECT_EQ(420, residuals.at(20));
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for (int p = 0; p < 10; ++p) {
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// Check "A" Jacobian.
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EXPECT_NEAR(-1.0, jacobian_vect[0][2 * p * 10 + p], kTolerance);
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// Check "B" Jacobian.
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EXPECT_NEAR(+1.0, jacobian_vect[0][(2 * p + 1) * 10 + p], kTolerance);
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jacobian_vect[0][2 * p * 10 + p] = 0.0;
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jacobian_vect[0][(2 * p + 1) * 10 + p] = 0.0;
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}
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// Check "C" Jacobian for first parameter block.
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for (int p = 0; p < 10; ++p) {
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EXPECT_NEAR(4 * p - 8, jacobian_vect[0][20 * 10 + p], kTolerance);
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jacobian_vect[0][20 * 10 + p] = 0.0;
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}
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for (double& i : jacobian_vect[0]) {
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EXPECT_EQ(0.0, i);
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}
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}
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// Takes 3 parameter blocks:
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// parameters[0] (x) is size 1.
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// parameters[1] (y) is size 2.
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// parameters[2] (z) is size 3.
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// Emits 7 residuals:
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// A: x[0] (= sum_x)
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// B: y[0] + 2.0 * y[1] (= sum_y)
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// C: z[0] + 3.0 * z[1] + 6.0 * z[2] (= sum_z)
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// D: sum_x * sum_y
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// E: sum_y * sum_z
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// F: sum_x * sum_z
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// G: sum_x * sum_y * sum_z
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class MyThreeParameterCostFunctor {
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public:
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template <typename T>
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bool operator()(T const* const* parameters, T* residuals) const {
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const T* x = parameters[0];
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const T* y = parameters[1];
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const T* z = parameters[2];
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T sum_x = x[0];
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T sum_y = y[0] + 2.0 * y[1];
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T sum_z = z[0] + 3.0 * z[1] + 6.0 * z[2];
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residuals[0] = sum_x;
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residuals[1] = sum_y;
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residuals[2] = sum_z;
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residuals[3] = sum_x * sum_y;
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residuals[4] = sum_y * sum_z;
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residuals[5] = sum_x * sum_z;
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residuals[6] = sum_x * sum_y * sum_z;
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return true;
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}
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};
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class ThreeParameterCostFunctorTest : public ::testing::Test {
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protected:
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void SetUp() final {
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// Prepare the parameters.
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x_.resize(1);
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x_[0] = 0.0;
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y_.resize(2);
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y_[0] = 1.0;
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y_[1] = 3.0;
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z_.resize(3);
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z_[0] = 2.0;
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z_[1] = 4.0;
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z_[2] = 6.0;
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parameter_blocks_.resize(3);
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parameter_blocks_[0] = &x_[0];
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parameter_blocks_[1] = &y_[0];
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parameter_blocks_[2] = &z_[0];
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// Prepare the cost function.
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using DynamicMyThreeParameterCostFunction =
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DynamicNumericDiffCostFunction<MyThreeParameterCostFunctor>;
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auto cost_function = std::make_unique<DynamicMyThreeParameterCostFunction>(
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new MyThreeParameterCostFunctor());
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cost_function->AddParameterBlock(1);
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cost_function->AddParameterBlock(2);
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cost_function->AddParameterBlock(3);
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cost_function->SetNumResiduals(7);
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cost_function_ = std::move(cost_function);
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// Setup jacobian data.
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jacobian_vect_.resize(3);
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jacobian_vect_[0].resize(7 * x_.size(), -100000);
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jacobian_vect_[1].resize(7 * y_.size(), -100000);
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jacobian_vect_[2].resize(7 * z_.size(), -100000);
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// Prepare the expected residuals.
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const double sum_x = x_[0];
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const double sum_y = y_[0] + 2.0 * y_[1];
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const double sum_z = z_[0] + 3.0 * z_[1] + 6.0 * z_[2];
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expected_residuals_.resize(7);
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expected_residuals_[0] = sum_x;
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expected_residuals_[1] = sum_y;
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expected_residuals_[2] = sum_z;
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expected_residuals_[3] = sum_x * sum_y;
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expected_residuals_[4] = sum_y * sum_z;
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expected_residuals_[5] = sum_x * sum_z;
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expected_residuals_[6] = sum_x * sum_y * sum_z;
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// Prepare the expected jacobian entries.
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expected_jacobian_x_.resize(7);
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expected_jacobian_x_[0] = 1.0;
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expected_jacobian_x_[1] = 0.0;
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expected_jacobian_x_[2] = 0.0;
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expected_jacobian_x_[3] = sum_y;
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expected_jacobian_x_[4] = 0.0;
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expected_jacobian_x_[5] = sum_z;
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expected_jacobian_x_[6] = sum_y * sum_z;
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expected_jacobian_y_.resize(14);
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expected_jacobian_y_[0] = 0.0;
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expected_jacobian_y_[1] = 0.0;
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expected_jacobian_y_[2] = 1.0;
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expected_jacobian_y_[3] = 2.0;
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expected_jacobian_y_[4] = 0.0;
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expected_jacobian_y_[5] = 0.0;
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expected_jacobian_y_[6] = sum_x;
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expected_jacobian_y_[7] = 2.0 * sum_x;
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expected_jacobian_y_[8] = sum_z;
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expected_jacobian_y_[9] = 2.0 * sum_z;
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expected_jacobian_y_[10] = 0.0;
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expected_jacobian_y_[11] = 0.0;
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expected_jacobian_y_[12] = sum_x * sum_z;
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expected_jacobian_y_[13] = 2.0 * sum_x * sum_z;
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expected_jacobian_z_.resize(21);
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expected_jacobian_z_[0] = 0.0;
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expected_jacobian_z_[1] = 0.0;
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expected_jacobian_z_[2] = 0.0;
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expected_jacobian_z_[3] = 0.0;
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expected_jacobian_z_[4] = 0.0;
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expected_jacobian_z_[5] = 0.0;
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expected_jacobian_z_[6] = 1.0;
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expected_jacobian_z_[7] = 3.0;
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expected_jacobian_z_[8] = 6.0;
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expected_jacobian_z_[9] = 0.0;
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expected_jacobian_z_[10] = 0.0;
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expected_jacobian_z_[11] = 0.0;
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expected_jacobian_z_[12] = sum_y;
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expected_jacobian_z_[13] = 3.0 * sum_y;
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expected_jacobian_z_[14] = 6.0 * sum_y;
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expected_jacobian_z_[15] = sum_x;
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expected_jacobian_z_[16] = 3.0 * sum_x;
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expected_jacobian_z_[17] = 6.0 * sum_x;
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expected_jacobian_z_[18] = sum_x * sum_y;
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expected_jacobian_z_[19] = 3.0 * sum_x * sum_y;
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expected_jacobian_z_[20] = 6.0 * sum_x * sum_y;
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}
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protected:
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std::vector<double> x_;
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std::vector<double> y_;
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std::vector<double> z_;
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std::vector<double*> parameter_blocks_;
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std::unique_ptr<CostFunction> cost_function_;
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std::vector<std::vector<double>> jacobian_vect_;
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std::vector<double> expected_residuals_;
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std::vector<double> expected_jacobian_x_;
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std::vector<double> expected_jacobian_y_;
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std::vector<double> expected_jacobian_z_;
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};
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TEST_F(ThreeParameterCostFunctorTest, TestThreeParameterResiduals) {
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std::vector<double> residuals(7, -100000);
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EXPECT_TRUE(cost_function_->Evaluate(
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parameter_blocks_.data(), residuals.data(), nullptr));
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for (int i = 0; i < 7; ++i) {
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EXPECT_EQ(expected_residuals_[i], residuals[i]);
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}
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}
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TEST_F(ThreeParameterCostFunctorTest, TestThreeParameterJacobian) {
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std::vector<double> residuals(7, -100000);
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std::vector<double*> jacobian;
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jacobian.push_back(jacobian_vect_[0].data());
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jacobian.push_back(jacobian_vect_[1].data());
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jacobian.push_back(jacobian_vect_[2].data());
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EXPECT_TRUE(cost_function_->Evaluate(
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parameter_blocks_.data(), residuals.data(), jacobian.data()));
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for (int i = 0; i < 7; ++i) {
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EXPECT_EQ(expected_residuals_[i], residuals[i]);
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}
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for (int i = 0; i < 7; ++i) {
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EXPECT_NEAR(expected_jacobian_x_[i], jacobian[0][i], kTolerance);
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}
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for (int i = 0; i < 14; ++i) {
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EXPECT_NEAR(expected_jacobian_y_[i], jacobian[1][i], kTolerance);
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}
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for (int i = 0; i < 21; ++i) {
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EXPECT_NEAR(expected_jacobian_z_[i], jacobian[2][i], kTolerance);
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}
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}
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TEST_F(ThreeParameterCostFunctorTest,
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ThreeParameterJacobianWithFirstAndLastParameterBlockConstant) {
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std::vector<double> residuals(7, -100000);
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std::vector<double*> jacobian;
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jacobian.push_back(nullptr);
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jacobian.push_back(jacobian_vect_[1].data());
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jacobian.push_back(nullptr);
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EXPECT_TRUE(cost_function_->Evaluate(
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parameter_blocks_.data(), residuals.data(), jacobian.data()));
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for (int i = 0; i < 7; ++i) {
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EXPECT_EQ(expected_residuals_[i], residuals[i]);
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}
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for (int i = 0; i < 14; ++i) {
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EXPECT_NEAR(expected_jacobian_y_[i], jacobian[1][i], kTolerance);
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}
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}
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TEST_F(ThreeParameterCostFunctorTest,
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ThreeParameterJacobianWithSecondParameterBlockConstant) {
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std::vector<double> residuals(7, -100000);
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|
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std::vector<double*> jacobian;
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jacobian.push_back(jacobian_vect_[0].data());
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jacobian.push_back(nullptr);
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jacobian.push_back(jacobian_vect_[2].data());
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EXPECT_TRUE(cost_function_->Evaluate(
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parameter_blocks_.data(), residuals.data(), jacobian.data()));
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|
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for (int i = 0; i < 7; ++i) {
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EXPECT_EQ(expected_residuals_[i], residuals[i]);
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}
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|
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for (int i = 0; i < 7; ++i) {
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EXPECT_NEAR(expected_jacobian_x_[i], jacobian[0][i], kTolerance);
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|
}
|
|
|
|
for (int i = 0; i < 21; ++i) {
|
|
EXPECT_NEAR(expected_jacobian_z_[i], jacobian[2][i], kTolerance);
|
|
}
|
|
}
|
|
|
|
TEST(DynamicNumericdiffCostFunctionTest, DeductionTemplateCompilationTest) {
|
|
// Ensure deduction guide to be working
|
|
(void)DynamicNumericDiffCostFunction{std::make_unique<MyCostFunctor>()};
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|
(void)DynamicNumericDiffCostFunction{std::make_unique<MyCostFunctor>(),
|
|
NumericDiffOptions{}};
|
|
(void)DynamicNumericDiffCostFunction{new MyCostFunctor};
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|
(void)DynamicNumericDiffCostFunction{new MyCostFunctor, TAKE_OWNERSHIP};
|
|
(void)DynamicNumericDiffCostFunction{
|
|
new MyCostFunctor, TAKE_OWNERSHIP, NumericDiffOptions{}};
|
|
}
|
|
|
|
TEST(DynamicNumericdiffCostFunctionTest, ArgumentForwarding) {
|
|
(void)DynamicNumericDiffCostFunction<MyCostFunctor>();
|
|
}
|
|
|
|
TEST(DynamicAutoDiffCostFunctionTest, UniquePtr) {
|
|
(void)DynamicNumericDiffCostFunction<MyCostFunctor>(
|
|
std::make_unique<MyCostFunctor>());
|
|
}
|
|
|
|
} // namespace ceres::internal
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