mirror of
https://github.com/ceres-solver/ceres-solver.git
synced 2026-08-31 09:30:36 +08:00
Keir Mierle
7492b0d8de
Since Ceres is moving to using GitHub for issues, and the Google Code URL in the current copyright header will soon become invalid, update all the headers. Change-Id: I1fce70375d1bcf098591f07b4d8f01a5c1e0789c
235 lines
8.0 KiB
C++
235 lines
8.0 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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}
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} // namespace internal
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} // namespace ceres
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