253 lines
8.5 KiB
C++
253 lines
8.5 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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#include "ceres/loss_function.h"
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#include <cstddef>
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#include "glog/logging.h"
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#include "gtest/gtest.h"
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namespace ceres {
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namespace internal {
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namespace {
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// Helper function for testing a LossFunction callback.
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//
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// Compares the values of rho'(s) and rho''(s) computed by the
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// callback with estimates obtained by symmetric finite differencing
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// of rho(s).
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void AssertLossFunctionIsValid(const LossFunction& loss, double s) {
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CHECK_GT(s, 0);
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// Evaluate rho(s), rho'(s) and rho''(s).
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double rho[3];
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loss.Evaluate(s, rho);
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// Use symmetric finite differencing to estimate rho'(s) and
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// rho''(s).
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const double kH = 1e-4;
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// Values at s + kH.
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double fwd[3];
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// Values at s - kH.
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double bwd[3];
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loss.Evaluate(s + kH, fwd);
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loss.Evaluate(s - kH, bwd);
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// First derivative.
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const double fd_1 = (fwd[0] - bwd[0]) / (2 * kH);
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ASSERT_NEAR(fd_1, rho[1], 1e-6);
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// Second derivative.
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const double fd_2 = (fwd[0] - 2*rho[0] + bwd[0]) / (kH * kH);
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ASSERT_NEAR(fd_2, rho[2], 1e-6);
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}
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} // namespace
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// Try two values of the scaling a = 0.7 and 1.3
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// (where scaling makes sense) and of the squared norm
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// s = 0.357 and 1.792
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//
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// Note that for the Huber loss the test exercises both code paths
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// (i.e. both small and large values of s).
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TEST(LossFunction, TrivialLoss) {
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AssertLossFunctionIsValid(TrivialLoss(), 0.357);
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AssertLossFunctionIsValid(TrivialLoss(), 1.792);
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}
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TEST(LossFunction, HuberLoss) {
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AssertLossFunctionIsValid(HuberLoss(0.7), 0.357);
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AssertLossFunctionIsValid(HuberLoss(0.7), 1.792);
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AssertLossFunctionIsValid(HuberLoss(1.3), 0.357);
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AssertLossFunctionIsValid(HuberLoss(1.3), 1.792);
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}
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TEST(LossFunction, SoftLOneLoss) {
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AssertLossFunctionIsValid(SoftLOneLoss(0.7), 0.357);
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AssertLossFunctionIsValid(SoftLOneLoss(0.7), 1.792);
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AssertLossFunctionIsValid(SoftLOneLoss(1.3), 0.357);
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AssertLossFunctionIsValid(SoftLOneLoss(1.3), 1.792);
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}
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TEST(LossFunction, CauchyLoss) {
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AssertLossFunctionIsValid(CauchyLoss(0.7), 0.357);
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AssertLossFunctionIsValid(CauchyLoss(0.7), 1.792);
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AssertLossFunctionIsValid(CauchyLoss(1.3), 0.357);
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AssertLossFunctionIsValid(CauchyLoss(1.3), 1.792);
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}
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TEST(LossFunction, ArctanLoss) {
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AssertLossFunctionIsValid(ArctanLoss(0.7), 0.357);
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AssertLossFunctionIsValid(ArctanLoss(0.7), 1.792);
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AssertLossFunctionIsValid(ArctanLoss(1.3), 0.357);
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AssertLossFunctionIsValid(ArctanLoss(1.3), 1.792);
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}
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TEST(LossFunction, TolerantLoss) {
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AssertLossFunctionIsValid(TolerantLoss(0.7, 0.4), 0.357);
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AssertLossFunctionIsValid(TolerantLoss(0.7, 0.4), 1.792);
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AssertLossFunctionIsValid(TolerantLoss(0.7, 0.4), 55.5);
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AssertLossFunctionIsValid(TolerantLoss(1.3, 0.1), 0.357);
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AssertLossFunctionIsValid(TolerantLoss(1.3, 0.1), 1.792);
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AssertLossFunctionIsValid(TolerantLoss(1.3, 0.1), 55.5);
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// Check the value at zero is actually zero.
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double rho[3];
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TolerantLoss(0.7, 0.4).Evaluate(0.0, rho);
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ASSERT_NEAR(rho[0], 0.0, 1e-6);
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// Check that loss before and after the approximation threshold are good.
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// A threshold of 36.7 is used by the implementation.
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AssertLossFunctionIsValid(TolerantLoss(20.0, 1.0), 20.0 + 36.6);
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AssertLossFunctionIsValid(TolerantLoss(20.0, 1.0), 20.0 + 36.7);
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AssertLossFunctionIsValid(TolerantLoss(20.0, 1.0), 20.0 + 36.8);
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AssertLossFunctionIsValid(TolerantLoss(20.0, 1.0), 20.0 + 1000.0);
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}
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TEST(LossFunction, TukeyLoss) {
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AssertLossFunctionIsValid(TukeyLoss(0.7), 0.357);
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AssertLossFunctionIsValid(TukeyLoss(0.7), 1.792);
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AssertLossFunctionIsValid(TukeyLoss(1.3), 0.357);
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AssertLossFunctionIsValid(TukeyLoss(1.3), 1.792);
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}
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TEST(LossFunction, ComposedLoss) {
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{
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HuberLoss f(0.7);
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CauchyLoss g(1.3);
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ComposedLoss c(&f, DO_NOT_TAKE_OWNERSHIP, &g, DO_NOT_TAKE_OWNERSHIP);
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AssertLossFunctionIsValid(c, 0.357);
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AssertLossFunctionIsValid(c, 1.792);
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}
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{
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CauchyLoss f(0.7);
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HuberLoss g(1.3);
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ComposedLoss c(&f, DO_NOT_TAKE_OWNERSHIP, &g, DO_NOT_TAKE_OWNERSHIP);
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AssertLossFunctionIsValid(c, 0.357);
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AssertLossFunctionIsValid(c, 1.792);
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}
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}
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TEST(LossFunction, ScaledLoss) {
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// Wrap a few loss functions, and a few scale factors. This can't combine
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// construction with the call to AssertLossFunctionIsValid() because Apple's
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// GCC is unable to eliminate the copy of ScaledLoss, which is not copyable.
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{
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ScaledLoss scaled_loss(NULL, 6, TAKE_OWNERSHIP);
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AssertLossFunctionIsValid(scaled_loss, 0.323);
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}
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{
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ScaledLoss scaled_loss(new TrivialLoss(), 10, TAKE_OWNERSHIP);
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AssertLossFunctionIsValid(scaled_loss, 0.357);
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}
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{
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ScaledLoss scaled_loss(new HuberLoss(0.7), 0.1, TAKE_OWNERSHIP);
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AssertLossFunctionIsValid(scaled_loss, 1.792);
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}
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{
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ScaledLoss scaled_loss(new SoftLOneLoss(1.3), 0.1, TAKE_OWNERSHIP);
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AssertLossFunctionIsValid(scaled_loss, 1.792);
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}
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{
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ScaledLoss scaled_loss(new CauchyLoss(1.3), 10, TAKE_OWNERSHIP);
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AssertLossFunctionIsValid(scaled_loss, 1.792);
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}
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{
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ScaledLoss scaled_loss(new ArctanLoss(1.3), 10, TAKE_OWNERSHIP);
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AssertLossFunctionIsValid(scaled_loss, 1.792);
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}
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{
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ScaledLoss scaled_loss(
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new TolerantLoss(1.3, 0.1), 10, TAKE_OWNERSHIP);
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AssertLossFunctionIsValid(scaled_loss, 1.792);
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}
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{
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ScaledLoss scaled_loss(
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new ComposedLoss(
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new HuberLoss(0.8), TAKE_OWNERSHIP,
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new TolerantLoss(1.3, 0.5), TAKE_OWNERSHIP), 10, TAKE_OWNERSHIP);
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AssertLossFunctionIsValid(scaled_loss, 1.792);
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}
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}
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TEST(LossFunction, LossFunctionWrapper) {
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// Initialization
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HuberLoss loss_function1(1.0);
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LossFunctionWrapper loss_function_wrapper(new HuberLoss(1.0),
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TAKE_OWNERSHIP);
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double s = 0.862;
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double rho_gold[3];
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double rho[3];
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loss_function1.Evaluate(s, rho_gold);
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loss_function_wrapper.Evaluate(s, rho);
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for (int i = 0; i < 3; ++i) {
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EXPECT_NEAR(rho[i], rho_gold[i], 1e-12);
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}
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// Resetting
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HuberLoss loss_function2(0.5);
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loss_function_wrapper.Reset(new HuberLoss(0.5), TAKE_OWNERSHIP);
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loss_function_wrapper.Evaluate(s, rho);
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loss_function2.Evaluate(s, rho_gold);
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for (int i = 0; i < 3; ++i) {
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EXPECT_NEAR(rho[i], rho_gold[i], 1e-12);
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}
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// Not taking ownership.
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HuberLoss loss_function3(0.3);
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loss_function_wrapper.Reset(&loss_function3, DO_NOT_TAKE_OWNERSHIP);
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loss_function_wrapper.Evaluate(s, rho);
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loss_function3.Evaluate(s, rho_gold);
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for (int i = 0; i < 3; ++i) {
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EXPECT_NEAR(rho[i], rho_gold[i], 1e-12);
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}
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// Set to NULL
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TrivialLoss loss_function4;
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loss_function_wrapper.Reset(NULL, TAKE_OWNERSHIP);
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loss_function_wrapper.Evaluate(s, rho);
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loss_function4.Evaluate(s, rho_gold);
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for (int i = 0; i < 3; ++i) {
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EXPECT_NEAR(rho[i], rho_gold[i], 1e-12);
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}
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// Set to NULL, not taking ownership
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loss_function_wrapper.Reset(NULL, DO_NOT_TAKE_OWNERSHIP);
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loss_function_wrapper.Evaluate(s, rho);
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loss_function4.Evaluate(s, rho_gold);
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for (int i = 0; i < 3; ++i) {
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EXPECT_NEAR(rho[i], rho_gold[i], 1e-12);
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}
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}
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} // namespace internal
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} // namespace ceres
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