mirror of
https://github.com/ceres-solver/ceres-solver.git
synced 2026-08-30 00:50:37 +08:00
Sameer Agarwal
974513a41f
Add handling of constant parameter blocks. Change-Id: I8b2ea79f47e190604fc4bed27705798240689f71
275 lines
9.4 KiB
C++
275 lines
9.4 KiB
C++
// Ceres Solver - A fast non-linear least squares minimizer
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// Copyright 2012 Google Inc. All rights reserved.
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// http://code.google.com/p/ceres-solver/
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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: thadh@gmail.com (Thad Hughes)
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// mierle@gmail.com (Keir Mierle)
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// sameeragarwal@google.com (Sameer Agarwal)
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#include "ceres/dynamic_autodiff_cost_function.h"
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#include <cstddef>
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#include "gtest/gtest.h"
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namespace ceres {
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namespace internal {
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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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template <typename T>
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bool operator()(T const* const* parameters, T* residuals) const {
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const T* 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++] = T(i) - params0[i];
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residuals[r++] = params0[i] - T(i);
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}
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T 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) - T(8) * params0[i];
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}
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const T* 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(DynamicAutodiffCostFunctionTest, TestResiduals) {
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vector<double> param_block_0(10, 0.0);
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vector<double> param_block_1(5, 0.0);
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DynamicAutoDiffCostFunction<MyCostFunctor, 3> 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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vector<double> residuals(21, -100000);
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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(cost_function.Evaluate(¶meter_blocks[0],
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residuals.data(),
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NULL));
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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(DynamicAutodiffCostFunctionTest, TestJacobian) {
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// Test the residual counting.
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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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vector<double> param_block_1(5, 0.0);
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DynamicAutoDiffCostFunction<MyCostFunctor, 3> 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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vector<double> residuals(21, -100000);
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// Prepare the parameters.
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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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vector<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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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(parameter_blocks.data(),
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residuals.data(),
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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_EQ(-1.0, jacobian_vect[0][2*p * 10 + p]);
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// Check "B" Jacobian.
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EXPECT_EQ(+1.0, jacobian_vect[0][(2*p+1) * 10 + p]);
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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_EQ(4 * p - 8, jacobian_vect[0][20 * 10 + p]);
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jacobian_vect[0][20 * 10 + p] = 0.0;
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}
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for (int i = 0; i < jacobian_vect[0].size(); ++i) {
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EXPECT_EQ(0.0, jacobian_vect[0][i]);
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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_EQ(1.0, jacobian_vect[1][20 * 5 + p]);
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jacobian_vect[1][20 * 5 + p] = 0.0;
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}
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for (int i = 0; i < jacobian_vect[1].size(); ++i) {
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EXPECT_EQ(0.0, jacobian_vect[1][i]);
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}
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}
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TEST(DynamicAutodiffCostFunctionTest, JacobianWithFirstParameterBlockConstant) {
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// Test the residual counting.
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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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vector<double> param_block_1(5, 0.0);
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DynamicAutoDiffCostFunction<MyCostFunctor, 3> 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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vector<double> residuals(21, -100000);
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// Prepare the parameters.
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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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vector<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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vector<double*> jacobian;
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jacobian.push_back(NULL);
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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(parameter_blocks.data(),
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residuals.data(),
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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_EQ(1.0, jacobian_vect[1][20 * 5 + p]);
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jacobian_vect[1][20 * 5 + p] = 0.0;
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}
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for (int i = 0; i < jacobian_vect[1].size(); ++i) {
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EXPECT_EQ(0.0, jacobian_vect[1][i]);
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}
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}
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TEST(DynamicAutodiffCostFunctionTest, JacobianWithSecondParameterBlockConstant) {
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// Test the residual counting.
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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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vector<double> param_block_1(5, 0.0);
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DynamicAutoDiffCostFunction<MyCostFunctor, 3> 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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vector<double> residuals(21, -100000);
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// Prepare the parameters.
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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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vector<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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vector<double*> jacobian;
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jacobian.push_back(jacobian_vect[0].data());
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jacobian.push_back(NULL);
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// Test jacobian computation.
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EXPECT_TRUE(cost_function.Evaluate(parameter_blocks.data(),
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residuals.data(),
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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_EQ(-1.0, jacobian_vect[0][2*p * 10 + p]);
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// Check "B" Jacobian.
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EXPECT_EQ(+1.0, jacobian_vect[0][(2*p+1) * 10 + p]);
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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_EQ(4 * p - 8, jacobian_vect[0][20 * 10 + p]);
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jacobian_vect[0][20 * 10 + p] = 0.0;
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}
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for (int i = 0; i < jacobian_vect[0].size(); ++i) {
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EXPECT_EQ(0.0, jacobian_vect[0][i]);
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}
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}
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} // namespace internal
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} // namespace ceres
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