MYNT-EYE-S-SDK/3rdparty/ceres-solver-1.11.0/internal/ceres/autodiff_cost_function_test.cc
2019-01-03 16:25:18 +08:00

147 lines
4.5 KiB
C++

// 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
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//
// Author: sameeragarwal@google.com (Sameer Agarwal)
#include "ceres/autodiff_cost_function.h"
#include <cstddef>
#include "gtest/gtest.h"
#include "ceres/cost_function.h"
namespace ceres {
namespace internal {
class BinaryScalarCost {
public:
explicit BinaryScalarCost(double a): a_(a) {}
template <typename T>
bool operator()(const T* const x, const T* const y,
T* cost) const {
cost[0] = x[0] * y[0] + x[1] * y[1] - T(a_);
return true;
}
private:
double a_;
};
TEST(AutodiffCostFunction, BilinearDifferentiationTest) {
CostFunction* cost_function =
new AutoDiffCostFunction<BinaryScalarCost, 1, 2, 2>(
new BinaryScalarCost(1.0));
double** parameters = new double*[2];
parameters[0] = new double[2];
parameters[1] = new double[2];
parameters[0][0] = 1;
parameters[0][1] = 2;
parameters[1][0] = 3;
parameters[1][1] = 4;
double** jacobians = new double*[2];
jacobians[0] = new double[2];
jacobians[1] = new double[2];
double residuals = 0.0;
cost_function->Evaluate(parameters, &residuals, NULL);
EXPECT_EQ(10.0, residuals);
cost_function->Evaluate(parameters, &residuals, jacobians);
EXPECT_EQ(3, jacobians[0][0]);
EXPECT_EQ(4, jacobians[0][1]);
EXPECT_EQ(1, jacobians[1][0]);
EXPECT_EQ(2, jacobians[1][1]);
delete[] jacobians[0];
delete[] jacobians[1];
delete[] parameters[0];
delete[] parameters[1];
delete[] jacobians;
delete[] parameters;
delete cost_function;
}
struct TenParameterCost {
template <typename T>
bool operator()(const T* const x0,
const T* const x1,
const T* const x2,
const T* const x3,
const T* const x4,
const T* const x5,
const T* const x6,
const T* const x7,
const T* const x8,
const T* const x9,
T* cost) const {
cost[0] = *x0 + *x1 + *x2 + *x3 + *x4 + *x5 + *x6 + *x7 + *x8 + *x9;
return true;
}
};
TEST(AutodiffCostFunction, ManyParameterAutodiffInstantiates) {
CostFunction* cost_function =
new AutoDiffCostFunction<
TenParameterCost, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1>(
new TenParameterCost);
double** parameters = new double*[10];
double** jacobians = new double*[10];
for (int i = 0; i < 10; ++i) {
parameters[i] = new double[1];
parameters[i][0] = i;
jacobians[i] = new double[1];
}
double residuals = 0.0;
cost_function->Evaluate(parameters, &residuals, NULL);
EXPECT_EQ(45.0, residuals);
cost_function->Evaluate(parameters, &residuals, jacobians);
EXPECT_EQ(residuals, 45.0);
for (int i = 0; i < 10; ++i) {
EXPECT_EQ(1.0, jacobians[i][0]);
}
for (int i = 0; i < 10; ++i) {
delete[] jacobians[i];
delete[] parameters[i];
}
delete[] jacobians;
delete[] parameters;
delete cost_function;
}
} // namespace internal
} // namespace ceres