MYNT-EYE-S-SDK/3rdparty/ceres-solver-1.11.0/internal/ceres/gradient_checker_test.cc

196 lines
5.8 KiB
C++
Raw Normal View History

2019-01-03 10:25:18 +02:00
// 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: wjr@google.com (William Rucklidge)
//
// This file contains tests for the GradientChecker class.
#include "ceres/gradient_checker.h"
#include <cmath>
#include <cstdlib>
#include <vector>
#include "ceres/cost_function.h"
#include "ceres/random.h"
#include "glog/logging.h"
#include "gtest/gtest.h"
namespace ceres {
namespace internal {
using std::vector;
// We pick a (non-quadratic) function whose derivative are easy:
//
// f = exp(- a' x).
// df = - f a.
//
// where 'a' is a vector of the same size as 'x'. In the block
// version, they are both block vectors, of course.
class GoodTestTerm : public CostFunction {
public:
GoodTestTerm(int arity, int const *dim) : arity_(arity) {
// Make 'arity' random vectors.
a_.resize(arity_);
for (int j = 0; j < arity_; ++j) {
a_[j].resize(dim[j]);
for (int u = 0; u < dim[j]; ++u) {
a_[j][u] = 2.0 * RandDouble() - 1.0;
}
}
for (int i = 0; i < arity_; i++) {
mutable_parameter_block_sizes()->push_back(dim[i]);
}
set_num_residuals(1);
}
bool Evaluate(double const* const* parameters,
double* residuals,
double** jacobians) const {
// Compute a . x.
double ax = 0;
for (int j = 0; j < arity_; ++j) {
for (int u = 0; u < parameter_block_sizes()[j]; ++u) {
ax += a_[j][u] * parameters[j][u];
}
}
// This is the cost, but also appears as a factor
// in the derivatives.
double f = *residuals = exp(-ax);
// Accumulate 1st order derivatives.
if (jacobians) {
for (int j = 0; j < arity_; ++j) {
if (jacobians[j]) {
for (int u = 0; u < parameter_block_sizes()[j]; ++u) {
// See comments before class.
jacobians[j][u] = - f * a_[j][u];
}
}
}
}
return true;
}
private:
int arity_;
vector<vector<double> > a_; // our vectors.
};
class BadTestTerm : public CostFunction {
public:
BadTestTerm(int arity, int const *dim) : arity_(arity) {
// Make 'arity' random vectors.
a_.resize(arity_);
for (int j = 0; j < arity_; ++j) {
a_[j].resize(dim[j]);
for (int u = 0; u < dim[j]; ++u) {
a_[j][u] = 2.0 * RandDouble() - 1.0;
}
}
for (int i = 0; i < arity_; i++) {
mutable_parameter_block_sizes()->push_back(dim[i]);
}
set_num_residuals(1);
}
bool Evaluate(double const* const* parameters,
double* residuals,
double** jacobians) const {
// Compute a . x.
double ax = 0;
for (int j = 0; j < arity_; ++j) {
for (int u = 0; u < parameter_block_sizes()[j]; ++u) {
ax += a_[j][u] * parameters[j][u];
}
}
// This is the cost, but also appears as a factor
// in the derivatives.
double f = *residuals = exp(-ax);
// Accumulate 1st order derivatives.
if (jacobians) {
for (int j = 0; j < arity_; ++j) {
if (jacobians[j]) {
for (int u = 0; u < parameter_block_sizes()[j]; ++u) {
// See comments before class.
jacobians[j][u] = - f * a_[j][u] + 0.001;
}
}
}
}
return true;
}
private:
int arity_;
vector<vector<double> > a_; // our vectors.
};
TEST(GradientChecker, SmokeTest) {
srand(5);
// Test with 3 blocks of size 2, 3 and 4.
int const arity = 3;
int const dim[arity] = { 2, 3, 4 };
// Make a random set of blocks.
FixedArray<double*> parameters(arity);
for (int j = 0; j < arity; ++j) {
parameters[j] = new double[dim[j]];
for (int u = 0; u < dim[j]; ++u) {
parameters[j][u] = 2.0 * RandDouble() - 1.0;
}
}
// Make a term and probe it.
GoodTestTerm good_term(arity, dim);
typedef GradientChecker<GoodTestTerm, 1, 2, 3, 4> GoodTermGradientChecker;
EXPECT_TRUE(GoodTermGradientChecker::Probe(
parameters.get(), 1e-6, &good_term, NULL));
BadTestTerm bad_term(arity, dim);
typedef GradientChecker<BadTestTerm, 1, 2, 3, 4> BadTermGradientChecker;
EXPECT_FALSE(BadTermGradientChecker::Probe(
parameters.get(), 1e-6, &bad_term, NULL));
for (int j = 0; j < arity; j++) {
delete[] parameters[j];
}
}
} // namespace internal
} // namespace ceres