// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2015 Google Inc. All rights reserved.
// http://ceres-solver.org/
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
//
// * Redistributions of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
// * Redistributions in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
// * Neither the name of Google Inc. nor the names of its contributors may be
// used to endorse or promote products derived from this software without
// specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
// POSSIBILITY OF SUCH DAMAGE.
//
// Author: sameeragarwal@google.com (Sameer Agarwal)
#include <cmath>
#include <limits>
#include "Eigen/Geometry"
#include "ceres/autodiff_local_parameterization.h"
#include "ceres/fpclassify.h"
#include "ceres/householder_vector.h"
#include "ceres/internal/autodiff.h"
#include "ceres/internal/eigen.h"
#include "ceres/local_parameterization.h"
#include "ceres/random.h"
#include "ceres/rotation.h"
#include "gtest/gtest.h"
namespace ceres {
namespace internal {
TEST(IdentityParameterization, EverythingTest) {
IdentityParameterization parameterization(3);
EXPECT_EQ(parameterization.GlobalSize(), 3);
EXPECT_EQ(parameterization.LocalSize(), 3);
double x[3] = {1.0, 2.0, 3.0};
double delta[3] = {0.0, 1.0, 2.0};
double x_plus_delta[3] = {0.0, 0.0, 0.0};
parameterization.Plus(x, delta, x_plus_delta);
EXPECT_EQ(x_plus_delta[0], 1.0);
EXPECT_EQ(x_plus_delta[1], 3.0);
EXPECT_EQ(x_plus_delta[2], 5.0);
double jacobian[9];
parameterization.ComputeJacobian(x, jacobian);
int k = 0;
for (int i = 0; i < 3; ++i) {
for (int j = 0; j < 3; ++j, ++k) {
EXPECT_EQ(jacobian[k], (i == j) ? 1.0 : 0.0);
}
}
Matrix global_matrix = Matrix::Ones(10, 3);
Matrix local_matrix = Matrix::Zero(10, 3);
parameterization.MultiplyByJacobian(x,
10,
global_matrix.data(),
local_matrix.data());
EXPECT_EQ((local_matrix - global_matrix).norm(), 0.0);
}
TEST(SubsetParameterization, NegativeParameterIndexDeathTest) {
std::vector<int> constant_parameters;
constant_parameters.push_back(-1);
EXPECT_DEATH_IF_SUPPORTED(
SubsetParameterization parameterization(2, constant_parameters),
"greater than zero");
}
TEST(SubsetParameterization, GreaterThanSizeParameterIndexDeathTest) {
std::vector<int> constant_parameters;
constant_parameters.push_back(2);
EXPECT_DEATH_IF_SUPPORTED(
SubsetParameterization parameterization(2, constant_parameters),
"less than the size");
}
TEST(SubsetParameterization, DuplicateParametersDeathTest) {
std::vector<int> constant_parameters;
constant_parameters.push_back(1);
constant_parameters.push_back(1);
EXPECT_DEATH_IF_SUPPORTED(
SubsetParameterization parameterization(2, constant_parameters),
"duplicates");
}
TEST(SubsetParameterization,
ProductParameterizationWithZeroLocalSizeSubsetParameterization1) {
std::vector<int> constant_parameters;
constant_parameters.push_back(0);
LocalParameterization* subset_param =
new SubsetParameterization(1, constant_parameters);
LocalParameterization* identity_param = new IdentityParameterization(2);
ProductParameterization product_param(subset_param, identity_param);
EXPECT_EQ(product_param.GlobalSize(), 3);
EXPECT_EQ(product_param.LocalSize(), 2);
double x[] = {1.0, 1.0, 1.0};
double delta[] = {2.0, 3.0};
double x_plus_delta[] = {0.0, 0.0, 0.0};
EXPECT_TRUE(product_param.Plus(x, delta, x_plus_delta));
EXPECT_EQ(x_plus_delta[0], x[0]);
EXPECT_EQ(x_plus_delta[1], x[1] + delta[0]);
EXPECT_EQ(x_plus_delta[2], x[2] + delta[1]);
Matrix actual_jacobian(3, 2);
EXPECT_TRUE(product_param.ComputeJacobian(x, actual_jacobian.data()));
}
TEST(SubsetParameterization,
ProductParameterizationWithZeroLocalSizeSubsetParameterization2) {
std::vector<int> constant_parameters;
constant_parameters.push_back(0);
LocalParameterization* subset_param =
new SubsetParameterization(1, constant_parameters);
LocalParameterization* identity_param = new IdentityParameterization(2);
ProductParameterization product_param(identity_param, subset_param);
EXPECT_EQ(product_param.GlobalSize(), 3);
EXPECT_EQ(product_param.LocalSize(), 2);
double x[] = {1.0, 1.0, 1.0};
double delta[] = {2.0, 3.0};
double x_plus_delta[] = {0.0, 0.0, 0.0};
EXPECT_TRUE(product_param.Plus(x, delta, x_plus_delta));
EXPECT_EQ(x_plus_delta[0], x[0] + delta[0]);
EXPECT_EQ(x_plus_delta[1], x[1] + delta[1]);
EXPECT_EQ(x_plus_delta[2], x[2]);
Matrix actual_jacobian(3, 2);
EXPECT_TRUE(product_param.ComputeJacobian(x, actual_jacobian.data()));
}
TEST(SubsetParameterization, NormalFunctionTest) {
const int kGlobalSize = 4;
const int kLocalSize = 3;
double x[kGlobalSize] = {1.0, 2.0, 3.0, 4.0};
for (int i = 0; i < kGlobalSize; ++i) {
std::vector<int> constant_parameters;
constant_parameters.push_back(i);
SubsetParameterization parameterization(kGlobalSize, constant_parameters);
double delta[kLocalSize] = {1.0, 2.0, 3.0};
double x_plus_delta[kGlobalSize] = {0.0, 0.0, 0.0};
parameterization.Plus(x, delta, x_plus_delta);
int k = 0;
for (int j = 0; j < kGlobalSize; ++j) {
if (j == i) {
EXPECT_EQ(x_plus_delta[j], x[j]);
} else {
EXPECT_EQ(x_plus_delta[j], x[j] + delta[k++]);
}
}
double jacobian[kGlobalSize * kLocalSize];
parameterization.ComputeJacobian(x, jacobian);
int delta_cursor = 0;
int jacobian_cursor = 0;
for (int j = 0; j < kGlobalSize; ++j) {
if (j != i) {
for (int k = 0; k < kLocalSize; ++k, jacobian_cursor++) {
EXPECT_EQ(jacobian[jacobian_cursor], delta_cursor == k ? 1.0 : 0.0);
}
++delta_cursor;
} else {
for (int k = 0; k < kLocalSize; ++k, jacobian_cursor++) {
EXPECT_EQ(jacobian[jacobian_cursor], 0.0);
}
}
}
Matrix global_matrix = Matrix::Ones(10, kGlobalSize);
for (int row = 0; row < kGlobalSize; ++row) {
for (int col = 0; col < kGlobalSize; ++col) {
global_matrix(row, col) = col;
}
}
Matrix local_matrix = Matrix::Zero(10, kLocalSize);
parameterization.MultiplyByJacobian(x,
10,
global_matrix.data(),
local_matrix.data());
Matrix expected_local_matrix =
global_matrix * MatrixRef(jacobian, kGlobalSize, kLocalSize);
EXPECT_EQ((local_matrix - expected_local_matrix).norm(), 0.0);
}
}
// Functor needed to implement automatically differentiated Plus for
// quaternions.
struct QuaternionPlus {
template<typename T>
bool operator()(const T* x, const T* delta, T* x_plus_delta) const {
const T squared_norm_delta =
delta[0] * delta[0] + delta[1] * delta[1] + delta[2] * delta[2];
T q_delta[4];
if (squared_norm_delta > T(0.0)) {
T norm_delta = sqrt(squared_norm_delta);
const T sin_delta_by_delta = sin(norm_delta) / norm_delta;
q_delta[0] = cos(norm_delta);
q_delta[1] = sin_delta_by_delta * delta[0];
q_delta[2] = sin_delta_by_delta * delta[1];
q_delta[3] = sin_delta_by_delta * delta[2];
} else {
// We do not just use q_delta = [1,0,0,0] here because that is a
// constant and when used for automatic differentiation will
// lead to a zero derivative. Instead we take a first order
// approximation and evaluate it at zero.
q_delta[0] = T(1.0);
q_delta[1] = delta[0];
q_delta[2] = delta[1];
q_delta[3] = delta[2];
}
QuaternionProduct(q_delta, x, x_plus_delta);
return true;
}
};
template<typename Parameterization, typename Plus>
void QuaternionParameterizationTestHelper(
const double* x, const double* delta,
const double* x_plus_delta_ref) {
const int kGlobalSize = 4;
const int kLocalSize = 3;
const double kTolerance = 1e-14;
double x_plus_delta[kGlobalSize] = {0.0, 0.0, 0.0, 0.0};
Parameterization parameterization;
parameterization.Plus(x, delta, x_plus_delta);
for (int i = 0; i < kGlobalSize; ++i) {
EXPECT_NEAR(x_plus_delta[i], x_plus_delta[i], kTolerance);
}
const double x_plus_delta_norm =
sqrt(x_plus_delta[0] * x_plus_delta[0] +
x_plus_delta[1] * x_plus_delta[1] +
x_plus_delta[2] * x_plus_delta[2] +
x_plus_delta[3] * x_plus_delta[3]);
EXPECT_NEAR(x_plus_delta_norm, 1.0, kTolerance);
double jacobian_ref[12];
double zero_delta[kLocalSize] = {0.0, 0.0, 0.0};
const double* parameters[2] = {x, zero_delta};
double* jacobian_array[2] = { NULL, jacobian_ref };
// Autodiff jacobian at delta_x = 0.
internal::AutoDiff<Plus,
double,
kGlobalSize,
kLocalSize>::Differentiate(Plus(),
parameters,
kGlobalSize,
x_plus_delta,
jacobian_array);
double jacobian[12];
parameterization.ComputeJacobian(x, jacobian);
for (int i = 0; i < 12; ++i) {
EXPECT_TRUE(IsFinite(jacobian[i]));
EXPECT_NEAR(jacobian[i], jacobian_ref[i], kTolerance)
<< "Jacobian mismatch: i = " << i
<< "\n Expected \n"
<< ConstMatrixRef(jacobian_ref, kGlobalSize, kLocalSize)
<< "\n Actual \n"
<< ConstMatrixRef(jacobian, kGlobalSize, kLocalSize);
}
Matrix global_matrix = Matrix::Random(10, kGlobalSize);
Matrix local_matrix = Matrix::Zero(10, kLocalSize);
parameterization.MultiplyByJacobian(x,
10,
global_matrix.data(),
local_matrix.data());
Matrix expected_local_matrix =
global_matrix * MatrixRef(jacobian, kGlobalSize, kLocalSize);
EXPECT_NEAR((local_matrix - expected_local_matrix).norm(),
0.0,
10.0 * std::numeric_limits<double>::epsilon());
}
template <int N>
void Normalize(double* x) {
VectorRef(x, N).normalize();
}
TEST(QuaternionParameterization, ZeroTest) {
double x[4] = {0.5, 0.5, 0.5, 0.5};
double delta[3] = {0.0, 0.0, 0.0};
double q_delta[4] = {1.0, 0.0, 0.0, 0.0};
double x_plus_delta[4] = {0.0, 0.0, 0.0, 0.0};
QuaternionProduct(q_delta, x, x_plus_delta);
QuaternionParameterizationTestHelper<QuaternionParameterization,
QuaternionPlus>(x, delta, x_plus_delta);
}
TEST(QuaternionParameterization, NearZeroTest) {
double x[4] = {0.52, 0.25, 0.15, 0.45};
Normalize<4>(x);
double delta[3] = {0.24, 0.15, 0.10};
for (int i = 0; i < 3; ++i) {
delta[i] = delta[i] * 1e-14;
}
double q_delta[4];
q_delta[0] = 1.0;
q_delta[1] = delta[0];
q_delta[2] = delta[1];
q_delta[3] = delta[2];
double x_plus_delta[4] = {0.0, 0.0, 0.0, 0.0};
QuaternionProduct(q_delta, x, x_plus_delta);
QuaternionParameterizationTestHelper<QuaternionParameterization,
QuaternionPlus>(x, delta, x_plus_delta);
}
TEST(QuaternionParameterization, AwayFromZeroTest) {
double x[4] = {0.52, 0.25, 0.15, 0.45};
Normalize<4>(x);
double delta[3] = {0.24, 0.15, 0.10};
const double delta_norm = sqrt(delta[0] * delta[0] +
delta[1] * delta[1] +
delta[2] * delta[2]);
double q_delta[4];
q_delta[0] = cos(delta_norm);
q_delta[1] = sin(delta_norm) / delta_norm * delta[0];
q_delta[2] = sin(delta_norm) / delta_norm * delta[1];
q_delta[3] = sin(delta_norm) / delta_norm * delta[2];
double x_plus_delta[4] = {0.0, 0.0, 0.0, 0.0};
QuaternionProduct(q_delta, x, x_plus_delta);
QuaternionParameterizationTestHelper<QuaternionParameterization,
QuaternionPlus>(x, delta, x_plus_delta);
}
// Functor needed to implement automatically differentiated Plus for
// Eigen's quaternion.
struct EigenQuaternionPlus {
template<typename T>
bool operator()(const T* x, const T* delta, T* x_plus_delta) const {
const T norm_delta =
sqrt(delta[0] * delta[0] + delta[1] * delta[1] + delta[2] * delta[2]);
Eigen::Quaternion<T> q_delta;
if (norm_delta > T(0.0)) {
const T sin_delta_by_delta = sin(norm_delta) / norm_delta;
q_delta.coeffs() << sin_delta_by_delta * delta[0],
sin_delta_by_delta * delta[1], sin_delta_by_delta * delta[2],
cos(norm_delta);
} else {
// We do not just use q_delta = [0,0,0,1] here because that is a
// constant and when used for automatic differentiation will
// lead to a zero derivative. Instead we take a first order
// approximation and evaluate it at zero.
q_delta.coeffs() << delta[0], delta[1], delta[2], T(1.0);
}
Eigen::Map<Eigen::Quaternion<T> > x_plus_delta_ref(x_plus_delta);
Eigen::Map<const Eigen::Quaternion<T> > x_ref(x);
x_plus_delta_ref = q_delta * x_ref;
return true;
}
};
TEST(EigenQuaternionParameterization, ZeroTest) {
Eigen::Quaterniond x(0.5, 0.5, 0.5, 0.5);
double delta[3] = {0.0, 0.0, 0.0};
Eigen::Quaterniond q_delta(1.0, 0.0, 0.0, 0.0);
Eigen::Quaterniond x_plus_delta = q_delta * x;
QuaternionParameterizationTestHelper<EigenQuaternionParameterization,
EigenQuaternionPlus>(
x.coeffs().data(), delta, x_plus_delta.coeffs().data());
}
TEST(EigenQuaternionParameterization, NearZeroTest) {
Eigen::Quaterniond x(0.52, 0.25, 0.15, 0.45);
x.normalize();
double delta[3] = {0.24, 0.15, 0.10};
for (int i = 0; i < 3; ++i) {
delta[i] = delta[i] * 1e-14;
}
// Note: w is first in the constructor.
Eigen::Quaterniond q_delta(1.0, delta[0], delta[1], delta[2]);
Eigen::Quaterniond x_plus_delta = q_delta * x;
QuaternionParameterizationTestHelper<EigenQuaternionParameterization,
EigenQuaternionPlus>(
x.coeffs().data(), delta, x_plus_delta.coeffs().data());
}
TEST(EigenQuaternionParameterization, AwayFromZeroTest) {
Eigen::Quaterniond x(0.52, 0.25, 0.15, 0.45);
x.normalize();
double delta[3] = {0.24, 0.15, 0.10};
const double delta_norm = sqrt(delta[0] * delta[0] +
delta[1] * delta[1] +
delta[2] * delta[2]);
// Note: w is first in the constructor.
Eigen::Quaterniond q_delta(cos(delta_norm),
sin(delta_norm) / delta_norm * delta[0],
sin(delta_norm) / delta_norm * delta[1],
sin(delta_norm) / delta_norm * delta[2]);
Eigen::Quaterniond x_plus_delta = q_delta * x;
QuaternionParameterizationTestHelper<EigenQuaternionParameterization,
EigenQuaternionPlus>(
x.coeffs().data(), delta, x_plus_delta.coeffs().data());
}
// Functor needed to implement automatically differentiated Plus for
// homogeneous vectors. Note this explicitly defined for vectors of size 4.
struct HomogeneousVectorParameterizationPlus {
template<typename Scalar>
bool operator()(const Scalar* p_x, const Scalar* p_delta,
Scalar* p_x_plus_delta) const {
Eigen::Map<const Eigen::Matrix<Scalar, 4, 1> > x(p_x);
Eigen::Map<const Eigen::Matrix<Scalar, 3, 1> > delta(p_delta);
Eigen::Map<Eigen::Matrix<Scalar, 4, 1> > x_plus_delta(p_x_plus_delta);
const Scalar squared_norm_delta =
delta[0] * delta[0] + delta[1] * delta[1] + delta[2] * delta[2];
Eigen::Matrix<Scalar, 4, 1> y;
Scalar one_half(0.5);
if (squared_norm_delta > Scalar(0.0)) {
Scalar norm_delta = sqrt(squared_norm_delta);
Scalar norm_delta_div_2 = 0.5 * norm_delta;
const Scalar sin_delta_by_delta = sin(norm_delta_div_2) /
norm_delta_div_2;
y[0] = sin_delta_by_delta * delta[0] * one_half;
y[1] = sin_delta_by_delta * delta[1] * one_half;
y[2] = sin_delta_by_delta * delta[2] * one_half;
y[3] = cos(norm_delta_div_2);
} else {
// We do not just use y = [0,0,0,1] here because that is a
// constant and when used for automatic differentiation will
// lead to a zero derivative. Instead we take a first order
// approximation and evaluate it at zero.
y[0] = delta[0] * one_half;
y[1] = delta[1] * one_half;
y[2] = delta[2] * one_half;
y[3] = Scalar(1.0);
}
Eigen::Matrix<Scalar, Eigen::Dynamic, 1> v(4);
Scalar beta;
internal::ComputeHouseholderVector<Scalar>(x, &v, &beta);
x_plus_delta = x.norm() * (y - v * (beta * v.dot(y)));
return true;
}
};
void HomogeneousVectorParameterizationHelper(const double* x,
const double* delta) {
const double kTolerance = 1e-14;
HomogeneousVectorParameterization homogeneous_vector_parameterization(4);
// Ensure the update maintains the norm.
double x_plus_delta[4] = {0.0, 0.0, 0.0, 0.0};
homogeneous_vector_parameterization.Plus(x, delta, x_plus_delta);
const double x_plus_delta_norm =
sqrt(x_plus_delta[0] * x_plus_delta[0] +
x_plus_delta[1] * x_plus_delta[1] +
x_plus_delta[2] * x_plus_delta[2] +
x_plus_delta[3] * x_plus_delta[3]);
const double x_norm = sqrt(x[0] * x[0] + x[1] * x[1] +
x[2] * x[2] + x[3] * x[3]);
EXPECT_NEAR(x_plus_delta_norm, x_norm, kTolerance);
// Autodiff jacobian at delta_x = 0.
AutoDiffLocalParameterization<HomogeneousVectorParameterizationPlus, 4, 3>
autodiff_jacobian;
double jacobian_autodiff[12];
double jacobian_analytic[12];
homogeneous_vector_parameterization.ComputeJacobian(x, jacobian_analytic);
autodiff_jacobian.ComputeJacobian(x, jacobian_autodiff);
for (int i = 0; i < 12; ++i) {
EXPECT_TRUE(ceres::IsFinite(jacobian_analytic[i]));
EXPECT_NEAR(jacobian_analytic[i], jacobian_autodiff[i], kTolerance)
<< "Jacobian mismatch: i = " << i << ", " << jacobian_analytic[i] << " "
<< jacobian_autodiff[i];
}
}
TEST(HomogeneousVectorParameterization, ZeroTest) {
double x[4] = {0.0, 0.0, 0.0, 1.0};
Normalize<4>(x);
double delta[3] = {0.0, 0.0, 0.0};
HomogeneousVectorParameterizationHelper(x, delta);
}
TEST(HomogeneousVectorParameterization, NearZeroTest1) {
double x[4] = {1e-5, 1e-5, 1e-5, 1.0};
Normalize<4>(x);
double delta[3] = {0.0, 1.0, 0.0};
HomogeneousVectorParameterizationHelper(x, delta);
}
TEST(HomogeneousVectorParameterization, NearZeroTest2) {
double x[4] = {0.001, 0.0, 0.0, 0.0};
double delta[3] = {0.0, 1.0, 0.0};
HomogeneousVectorParameterizationHelper(x, delta);
}
TEST(HomogeneousVectorParameterization, AwayFromZeroTest1) {
double x[4] = {0.52, 0.25, 0.15, 0.45};
Normalize<4>(x);
double delta[3] = {0.0, 1.0, -0.5};
HomogeneousVectorParameterizationHelper(x, delta);
}
TEST(HomogeneousVectorParameterization, AwayFromZeroTest2) {
double x[4] = {0.87, -0.25, -0.34, 0.45};
Normalize<4>(x);
double delta[3] = {0.0, 0.0, -0.5};
HomogeneousVectorParameterizationHelper(x, delta);
}
TEST(HomogeneousVectorParameterization, AwayFromZeroTest3) {
double x[4] = {0.0, 0.0, 0.0, 2.0};
double delta[3] = {0.0, 0.0, 0};
HomogeneousVectorParameterizationHelper(x, delta);
}
TEST(HomogeneousVectorParameterization, AwayFromZeroTest4) {
double x[4] = {0.2, -1.0, 0.0, 2.0};
double delta[3] = {1.4, 0.0, -0.5};
HomogeneousVectorParameterizationHelper(x, delta);
}
TEST(HomogeneousVectorParameterization, AwayFromZeroTest5) {
double x[4] = {2.0, 0.0, 0.0, 0.0};
double delta[3] = {1.4, 0.0, -0.5};
HomogeneousVectorParameterizationHelper(x, delta);
}
TEST(HomogeneousVectorParameterization, DeathTests) {
EXPECT_DEATH_IF_SUPPORTED(HomogeneousVectorParameterization x(1), "size");
}
class ProductParameterizationTest : public ::testing::Test {
protected :
virtual void SetUp() {
const int global_size1 = 5;
std::vector<int> constant_parameters1;
constant_parameters1.push_back(2);
param1_.reset(new SubsetParameterization(global_size1,
constant_parameters1));
const int global_size2 = 3;
std::vector<int> constant_parameters2;
constant_parameters2.push_back(0);
constant_parameters2.push_back(1);
param2_.reset(new SubsetParameterization(global_size2,
constant_parameters2));
const int global_size3 = 4;
std::vector<int> constant_parameters3;
constant_parameters3.push_back(1);
param3_.reset(new SubsetParameterization(global_size3,
constant_parameters3));
const int global_size4 = 2;
std::vector<int> constant_parameters4;
constant_parameters4.push_back(1);
param4_.reset(new SubsetParameterization(global_size4,
constant_parameters4));
}
scoped_ptr<LocalParameterization> param1_;
scoped_ptr<LocalParameterization> param2_;
scoped_ptr<LocalParameterization> param3_;
scoped_ptr<LocalParameterization> param4_;
};
TEST_F(ProductParameterizationTest, LocalAndGlobalSize2) {
LocalParameterization* param1 = param1_.release();
LocalParameterization* param2 = param2_.release();
ProductParameterization product_param(param1, param2);
EXPECT_EQ(product_param.LocalSize(),
param1->LocalSize() + param2->LocalSize());
EXPECT_EQ(product_param.GlobalSize(),
param1->GlobalSize() + param2->GlobalSize());
}
TEST_F(ProductParameterizationTest, LocalAndGlobalSize3) {
LocalParameterization* param1 = param1_.release();
LocalParameterization* param2 = param2_.release();
LocalParameterization* param3 = param3_.release();
ProductParameterization product_param(param1, param2, param3);
EXPECT_EQ(product_param.LocalSize(),
param1->LocalSize() + param2->LocalSize() + param3->LocalSize());
EXPECT_EQ(product_param.GlobalSize(),
param1->GlobalSize() + param2->GlobalSize() + param3->GlobalSize());
}
TEST_F(ProductParameterizationTest, LocalAndGlobalSize4) {
LocalParameterization* param1 = param1_.release();
LocalParameterization* param2 = param2_.release();
LocalParameterization* param3 = param3_.release();
LocalParameterization* param4 = param4_.release();
ProductParameterization product_param(param1, param2, param3, param4);
EXPECT_EQ(product_param.LocalSize(),
param1->LocalSize() +
param2->LocalSize() +
param3->LocalSize() +
param4->LocalSize());
EXPECT_EQ(product_param.GlobalSize(),
param1->GlobalSize() +
param2->GlobalSize() +
param3->GlobalSize() +
param4->GlobalSize());
}
TEST_F(ProductParameterizationTest, Plus) {
LocalParameterization* param1 = param1_.release();
LocalParameterization* param2 = param2_.release();
LocalParameterization* param3 = param3_.release();
LocalParameterization* param4 = param4_.release();
ProductParameterization product_param(param1, param2, param3, param4);
std::vector<double> x(product_param.GlobalSize(), 0.0);
std::vector<double> delta(product_param.LocalSize(), 0.0);
std::vector<double> x_plus_delta_expected(product_param.GlobalSize(), 0.0);
std::vector<double> x_plus_delta(product_param.GlobalSize(), 0.0);
for (int i = 0; i < product_param.GlobalSize(); ++i) {
x[i] = RandNormal();
}
for (int i = 0; i < product_param.LocalSize(); ++i) {
delta[i] = RandNormal();
}
EXPECT_TRUE(product_param.Plus(&x[0], &delta[0], &x_plus_delta_expected[0]));
int x_cursor = 0;
int delta_cursor = 0;
EXPECT_TRUE(param1->Plus(&x[x_cursor],
&delta[delta_cursor],
&x_plus_delta[x_cursor]));
x_cursor += param1->GlobalSize();
delta_cursor += param1->LocalSize();
EXPECT_TRUE(param2->Plus(&x[x_cursor],
&delta[delta_cursor],
&x_plus_delta[x_cursor]));
x_cursor += param2->GlobalSize();
delta_cursor += param2->LocalSize();
EXPECT_TRUE(param3->Plus(&x[x_cursor],
&delta[delta_cursor],
&x_plus_delta[x_cursor]));
x_cursor += param3->GlobalSize();
delta_cursor += param3->LocalSize();
EXPECT_TRUE(param4->Plus(&x[x_cursor],
&delta[delta_cursor],
&x_plus_delta[x_cursor]));
x_cursor += param4->GlobalSize();
delta_cursor += param4->LocalSize();
for (int i = 0; i < x.size(); ++i) {
EXPECT_EQ(x_plus_delta[i], x_plus_delta_expected[i]);
}
}
TEST_F(ProductParameterizationTest, ComputeJacobian) {
LocalParameterization* param1 = param1_.release();
LocalParameterization* param2 = param2_.release();
LocalParameterization* param3 = param3_.release();
LocalParameterization* param4 = param4_.release();
ProductParameterization product_param(param1, param2, param3, param4);
std::vector<double> x(product_param.GlobalSize(), 0.0);
for (int i = 0; i < product_param.GlobalSize(); ++i) {
x[i] = RandNormal();
}
Matrix jacobian = Matrix::Random(product_param.GlobalSize(),
product_param.LocalSize());
EXPECT_TRUE(product_param.ComputeJacobian(&x[0], jacobian.data()));
int x_cursor = 0;
int delta_cursor = 0;
Matrix jacobian1(param1->GlobalSize(), param1->LocalSize());
EXPECT_TRUE(param1->ComputeJacobian(&x[x_cursor], jacobian1.data()));
jacobian.block(x_cursor, delta_cursor,
param1->GlobalSize(),
param1->LocalSize())
-= jacobian1;
x_cursor += param1->GlobalSize();
delta_cursor += param1->LocalSize();
Matrix jacobian2(param2->GlobalSize(), param2->LocalSize());
EXPECT_TRUE(param2->ComputeJacobian(&x[x_cursor], jacobian2.data()));
jacobian.block(x_cursor, delta_cursor,
param2->GlobalSize(),
param2->LocalSize())
-= jacobian2;
x_cursor += param2->GlobalSize();
delta_cursor += param2->LocalSize();
Matrix jacobian3(param3->GlobalSize(), param3->LocalSize());
EXPECT_TRUE(param3->ComputeJacobian(&x[x_cursor], jacobian3.data()));
jacobian.block(x_cursor, delta_cursor,
param3->GlobalSize(),
param3->LocalSize())
-= jacobian3;
x_cursor += param3->GlobalSize();
delta_cursor += param3->LocalSize();
Matrix jacobian4(param4->GlobalSize(), param4->LocalSize());
EXPECT_TRUE(param4->ComputeJacobian(&x[x_cursor], jacobian4.data()));
jacobian.block(x_cursor, delta_cursor,
param4->GlobalSize(),
param4->LocalSize())
-= jacobian4;
x_cursor += param4->GlobalSize();
delta_cursor += param4->LocalSize();
EXPECT_NEAR(jacobian.norm(), 0.0, std::numeric_limits<double>::epsilon());
}
} // namespace internal
} // namespace ceres