forked from bartvdbraak/blender
6fb6a08bf8
Even tho it's currently only used by Libmv we might use it for something else in the future. Plus, it's actually where it logically belongs to.
175 lines
5.5 KiB
C++
175 lines
5.5 KiB
C++
// Ceres Solver - A fast non-linear least squares minimizer
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// Copyright 2015 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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//
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// Purpose: See .h file.
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#include "ceres/loss_function.h"
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#include <cmath>
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#include <cstddef>
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#include <limits>
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namespace ceres {
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void TrivialLoss::Evaluate(double s, double rho[3]) const {
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rho[0] = s;
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rho[1] = 1.0;
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rho[2] = 0.0;
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}
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void HuberLoss::Evaluate(double s, double rho[3]) const {
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if (s > b_) {
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// Outlier region.
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// 'r' is always positive.
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const double r = sqrt(s);
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rho[0] = 2.0 * a_ * r - b_;
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rho[1] = std::max(std::numeric_limits<double>::min(), a_ / r);
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rho[2] = - rho[1] / (2.0 * s);
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} else {
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// Inlier region.
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rho[0] = s;
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rho[1] = 1.0;
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rho[2] = 0.0;
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}
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}
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void SoftLOneLoss::Evaluate(double s, double rho[3]) const {
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const double sum = 1.0 + s * c_;
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const double tmp = sqrt(sum);
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// 'sum' and 'tmp' are always positive, assuming that 's' is.
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rho[0] = 2.0 * b_ * (tmp - 1.0);
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rho[1] = std::max(std::numeric_limits<double>::min(), 1.0 / tmp);
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rho[2] = - (c_ * rho[1]) / (2.0 * sum);
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}
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void CauchyLoss::Evaluate(double s, double rho[3]) const {
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const double sum = 1.0 + s * c_;
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const double inv = 1.0 / sum;
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// 'sum' and 'inv' are always positive, assuming that 's' is.
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rho[0] = b_ * log(sum);
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rho[1] = std::max(std::numeric_limits<double>::min(), inv);
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rho[2] = - c_ * (inv * inv);
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}
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void ArctanLoss::Evaluate(double s, double rho[3]) const {
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const double sum = 1 + s * s * b_;
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const double inv = 1 / sum;
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// 'sum' and 'inv' are always positive.
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rho[0] = a_ * atan2(s, a_);
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rho[1] = std::max(std::numeric_limits<double>::min(), inv);
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rho[2] = -2.0 * s * b_ * (inv * inv);
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}
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TolerantLoss::TolerantLoss(double a, double b)
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: a_(a),
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b_(b),
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c_(b * log(1.0 + exp(-a / b))) {
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CHECK_GE(a, 0.0);
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CHECK_GT(b, 0.0);
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}
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void TolerantLoss::Evaluate(double s, double rho[3]) const {
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const double x = (s - a_) / b_;
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// The basic equation is rho[0] = b ln(1 + e^x). However, if e^x is too
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// large, it will overflow. Since numerically 1 + e^x == e^x when the
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// x is greater than about ln(2^53) for doubles, beyond this threshold
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// we substitute x for ln(1 + e^x) as a numerically equivalent approximation.
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static const double kLog2Pow53 = 36.7; // ln(MathLimits<double>::kEpsilon).
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if (x > kLog2Pow53) {
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rho[0] = s - a_ - c_;
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rho[1] = 1.0;
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rho[2] = 0.0;
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} else {
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const double e_x = exp(x);
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rho[0] = b_ * log(1.0 + e_x) - c_;
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rho[1] = std::max(std::numeric_limits<double>::min(), e_x / (1.0 + e_x));
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rho[2] = 0.5 / (b_ * (1.0 + cosh(x)));
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}
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}
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void TukeyLoss::Evaluate(double s, double* rho) const {
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if (s <= a_squared_) {
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// Inlier region.
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const double value = 1.0 - s / a_squared_;
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const double value_sq = value * value;
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rho[0] = a_squared_ / 6.0 * (1.0 - value_sq * value);
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rho[1] = 0.5 * value_sq;
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rho[2] = -1.0 / a_squared_ * value;
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} else {
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// Outlier region.
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rho[0] = a_squared_ / 6.0;
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rho[1] = 0.0;
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rho[2] = 0.0;
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}
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}
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ComposedLoss::ComposedLoss(const LossFunction* f, Ownership ownership_f,
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const LossFunction* g, Ownership ownership_g)
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: f_(CHECK_NOTNULL(f)),
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g_(CHECK_NOTNULL(g)),
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ownership_f_(ownership_f),
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ownership_g_(ownership_g) {
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}
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ComposedLoss::~ComposedLoss() {
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if (ownership_f_ == DO_NOT_TAKE_OWNERSHIP) {
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f_.release();
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}
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if (ownership_g_ == DO_NOT_TAKE_OWNERSHIP) {
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g_.release();
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}
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}
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void ComposedLoss::Evaluate(double s, double rho[3]) const {
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double rho_f[3], rho_g[3];
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g_->Evaluate(s, rho_g);
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f_->Evaluate(rho_g[0], rho_f);
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rho[0] = rho_f[0];
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// f'(g(s)) * g'(s).
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rho[1] = rho_f[1] * rho_g[1];
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// f''(g(s)) * g'(s) * g'(s) + f'(g(s)) * g''(s).
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rho[2] = rho_f[2] * rho_g[1] * rho_g[1] + rho_f[1] * rho_g[2];
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}
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void ScaledLoss::Evaluate(double s, double rho[3]) const {
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if (rho_.get() == NULL) {
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rho[0] = a_ * s;
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rho[1] = a_;
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rho[2] = 0.0;
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} else {
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rho_->Evaluate(s, rho);
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rho[0] *= a_;
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rho[1] *= a_;
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rho[2] *= a_;
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}
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}
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} // namespace ceres
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