Keir Mierle | 8ebb073 | 2012-04-30 23:09:08 -0700 | [diff] [blame] | 1 | // Ceres Solver - A fast non-linear least squares minimizer |
| 2 | // Copyright 2010, 2011, 2012 Google Inc. All rights reserved. |
| 3 | // http://code.google.com/p/ceres-solver/ |
| 4 | // |
| 5 | // Redistribution and use in source and binary forms, with or without |
| 6 | // modification, are permitted provided that the following conditions are met: |
| 7 | // |
| 8 | // * Redistributions of source code must retain the above copyright notice, |
| 9 | // this list of conditions and the following disclaimer. |
| 10 | // * Redistributions in binary form must reproduce the above copyright notice, |
| 11 | // this list of conditions and the following disclaimer in the documentation |
| 12 | // and/or other materials provided with the distribution. |
| 13 | // * Neither the name of Google Inc. nor the names of its contributors may be |
| 14 | // used to endorse or promote products derived from this software without |
| 15 | // specific prior written permission. |
| 16 | // |
| 17 | // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" |
| 18 | // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE |
| 19 | // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE |
| 20 | // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE |
| 21 | // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR |
| 22 | // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF |
| 23 | // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS |
| 24 | // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN |
| 25 | // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) |
| 26 | // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE |
| 27 | // POSSIBILITY OF SUCH DAMAGE. |
| 28 | // |
| 29 | // Author: sameeragarwal@google.com (Sameer Agarwal) |
| 30 | |
| 31 | #include "ceres/loss_function.h" |
| 32 | |
| 33 | #include <cstddef> |
| 34 | |
Sameer Agarwal | 0beab86 | 2012-08-13 15:12:01 -0700 | [diff] [blame] | 35 | #include "glog/logging.h" |
Keir Mierle | 8ebb073 | 2012-04-30 23:09:08 -0700 | [diff] [blame] | 36 | #include "gtest/gtest.h" |
| 37 | |
| 38 | namespace ceres { |
| 39 | namespace internal { |
| 40 | namespace { |
| 41 | |
| 42 | // Helper function for testing a LossFunction callback. |
| 43 | // |
| 44 | // Compares the values of rho'(s) and rho''(s) computed by the |
| 45 | // callback with estimates obtained by symmetric finite differencing |
| 46 | // of rho(s). |
| 47 | void AssertLossFunctionIsValid(const LossFunction& loss, double s) { |
| 48 | CHECK_GT(s, 0); |
| 49 | |
| 50 | // Evaluate rho(s), rho'(s) and rho''(s). |
| 51 | double rho[3]; |
| 52 | loss.Evaluate(s, rho); |
| 53 | |
| 54 | // Use symmetric finite differencing to estimate rho'(s) and |
| 55 | // rho''(s). |
| 56 | const double kH = 1e-4; |
| 57 | // Values at s + kH. |
| 58 | double fwd[3]; |
| 59 | // Values at s - kH. |
| 60 | double bwd[3]; |
| 61 | loss.Evaluate(s + kH, fwd); |
| 62 | loss.Evaluate(s - kH, bwd); |
| 63 | |
| 64 | // First derivative. |
| 65 | const double fd_1 = (fwd[0] - bwd[0]) / (2 * kH); |
| 66 | ASSERT_NEAR(fd_1, rho[1], 1e-6); |
| 67 | |
| 68 | // Second derivative. |
| 69 | const double fd_2 = (fwd[0] - 2*rho[0] + bwd[0]) / (kH * kH); |
| 70 | ASSERT_NEAR(fd_2, rho[2], 1e-6); |
| 71 | } |
| 72 | } // namespace |
| 73 | |
| 74 | // Try two values of the scaling a = 0.7 and 1.3 |
| 75 | // (where scaling makes sense) and of the squared norm |
| 76 | // s = 0.357 and 1.792 |
| 77 | // |
| 78 | // Note that for the Huber loss the test exercises both code paths |
| 79 | // (i.e. both small and large values of s). |
| 80 | |
| 81 | TEST(LossFunction, TrivialLoss) { |
| 82 | AssertLossFunctionIsValid(TrivialLoss(), 0.357); |
| 83 | AssertLossFunctionIsValid(TrivialLoss(), 1.792); |
| 84 | } |
| 85 | |
| 86 | TEST(LossFunction, HuberLoss) { |
| 87 | AssertLossFunctionIsValid(HuberLoss(0.7), 0.357); |
| 88 | AssertLossFunctionIsValid(HuberLoss(0.7), 1.792); |
| 89 | AssertLossFunctionIsValid(HuberLoss(1.3), 0.357); |
| 90 | AssertLossFunctionIsValid(HuberLoss(1.3), 1.792); |
| 91 | } |
| 92 | |
| 93 | TEST(LossFunction, SoftLOneLoss) { |
| 94 | AssertLossFunctionIsValid(SoftLOneLoss(0.7), 0.357); |
| 95 | AssertLossFunctionIsValid(SoftLOneLoss(0.7), 1.792); |
| 96 | AssertLossFunctionIsValid(SoftLOneLoss(1.3), 0.357); |
| 97 | AssertLossFunctionIsValid(SoftLOneLoss(1.3), 1.792); |
| 98 | } |
| 99 | |
| 100 | TEST(LossFunction, CauchyLoss) { |
| 101 | AssertLossFunctionIsValid(CauchyLoss(0.7), 0.357); |
| 102 | AssertLossFunctionIsValid(CauchyLoss(0.7), 1.792); |
| 103 | AssertLossFunctionIsValid(CauchyLoss(1.3), 0.357); |
| 104 | AssertLossFunctionIsValid(CauchyLoss(1.3), 1.792); |
| 105 | } |
| 106 | |
Sameer Agarwal | ad1f7b7 | 2012-08-20 11:10:34 -0700 | [diff] [blame] | 107 | TEST(LossFunction, ArctanLoss) { |
| 108 | AssertLossFunctionIsValid(ArctanLoss(0.7), 0.357); |
| 109 | AssertLossFunctionIsValid(ArctanLoss(0.7), 1.792); |
| 110 | AssertLossFunctionIsValid(ArctanLoss(1.3), 0.357); |
| 111 | AssertLossFunctionIsValid(ArctanLoss(1.3), 1.792); |
| 112 | } |
| 113 | |
| 114 | TEST(LossFunction, TolerantLoss) { |
| 115 | AssertLossFunctionIsValid(TolerantLoss(0.7, 0.4), 0.357); |
| 116 | AssertLossFunctionIsValid(TolerantLoss(0.7, 0.4), 1.792); |
| 117 | AssertLossFunctionIsValid(TolerantLoss(0.7, 0.4), 55.5); |
| 118 | AssertLossFunctionIsValid(TolerantLoss(1.3, 0.1), 0.357); |
| 119 | AssertLossFunctionIsValid(TolerantLoss(1.3, 0.1), 1.792); |
| 120 | AssertLossFunctionIsValid(TolerantLoss(1.3, 0.1), 55.5); |
| 121 | // Check the value at zero is actually zero. |
| 122 | double rho[3]; |
| 123 | TolerantLoss(0.7, 0.4).Evaluate(0.0, rho); |
| 124 | ASSERT_NEAR(rho[0], 0.0, 1e-6); |
| 125 | // Check that loss before and after the approximation threshold are good. |
| 126 | // A threshold of 36.7 is used by the implementation. |
| 127 | AssertLossFunctionIsValid(TolerantLoss(20.0, 1.0), 20.0 + 36.6); |
| 128 | AssertLossFunctionIsValid(TolerantLoss(20.0, 1.0), 20.0 + 36.7); |
| 129 | AssertLossFunctionIsValid(TolerantLoss(20.0, 1.0), 20.0 + 36.8); |
| 130 | AssertLossFunctionIsValid(TolerantLoss(20.0, 1.0), 20.0 + 1000.0); |
| 131 | } |
| 132 | |
Mike Vitus | 7c0ac8f | 2014-11-13 14:46:34 -0800 | [diff] [blame] | 133 | TEST(LossFunction, TukeyLoss) { |
| 134 | AssertLossFunctionIsValid(TukeyLoss(0.7), 0.357); |
| 135 | AssertLossFunctionIsValid(TukeyLoss(0.7), 1.792); |
| 136 | AssertLossFunctionIsValid(TukeyLoss(1.3), 0.357); |
| 137 | AssertLossFunctionIsValid(TukeyLoss(1.3), 1.792); |
| 138 | } |
| 139 | |
Sameer Agarwal | ad1f7b7 | 2012-08-20 11:10:34 -0700 | [diff] [blame] | 140 | TEST(LossFunction, ComposedLoss) { |
| 141 | { |
| 142 | HuberLoss f(0.7); |
| 143 | CauchyLoss g(1.3); |
| 144 | ComposedLoss c(&f, DO_NOT_TAKE_OWNERSHIP, &g, DO_NOT_TAKE_OWNERSHIP); |
| 145 | AssertLossFunctionIsValid(c, 0.357); |
| 146 | AssertLossFunctionIsValid(c, 1.792); |
| 147 | } |
| 148 | { |
| 149 | CauchyLoss f(0.7); |
| 150 | HuberLoss g(1.3); |
| 151 | ComposedLoss c(&f, DO_NOT_TAKE_OWNERSHIP, &g, DO_NOT_TAKE_OWNERSHIP); |
| 152 | AssertLossFunctionIsValid(c, 0.357); |
| 153 | AssertLossFunctionIsValid(c, 1.792); |
| 154 | } |
| 155 | } |
| 156 | |
Keir Mierle | 8ebb073 | 2012-04-30 23:09:08 -0700 | [diff] [blame] | 157 | TEST(LossFunction, ScaledLoss) { |
| 158 | // Wrap a few loss functions, and a few scale factors. This can't combine |
| 159 | // construction with the call to AssertLossFunctionIsValid() because Apple's |
| 160 | // GCC is unable to eliminate the copy of ScaledLoss, which is not copyable. |
| 161 | { |
| 162 | ScaledLoss scaled_loss(NULL, 6, TAKE_OWNERSHIP); |
| 163 | AssertLossFunctionIsValid(scaled_loss, 0.323); |
| 164 | } |
| 165 | { |
| 166 | ScaledLoss scaled_loss(new TrivialLoss(), 10, TAKE_OWNERSHIP); |
| 167 | AssertLossFunctionIsValid(scaled_loss, 0.357); |
| 168 | } |
| 169 | { |
| 170 | ScaledLoss scaled_loss(new HuberLoss(0.7), 0.1, TAKE_OWNERSHIP); |
| 171 | AssertLossFunctionIsValid(scaled_loss, 1.792); |
| 172 | } |
| 173 | { |
| 174 | ScaledLoss scaled_loss(new SoftLOneLoss(1.3), 0.1, TAKE_OWNERSHIP); |
| 175 | AssertLossFunctionIsValid(scaled_loss, 1.792); |
| 176 | } |
| 177 | { |
| 178 | ScaledLoss scaled_loss(new CauchyLoss(1.3), 10, TAKE_OWNERSHIP); |
| 179 | AssertLossFunctionIsValid(scaled_loss, 1.792); |
| 180 | } |
Sameer Agarwal | ad1f7b7 | 2012-08-20 11:10:34 -0700 | [diff] [blame] | 181 | { |
| 182 | ScaledLoss scaled_loss(new ArctanLoss(1.3), 10, TAKE_OWNERSHIP); |
| 183 | AssertLossFunctionIsValid(scaled_loss, 1.792); |
| 184 | } |
| 185 | { |
| 186 | ScaledLoss scaled_loss( |
| 187 | new TolerantLoss(1.3, 0.1), 10, TAKE_OWNERSHIP); |
| 188 | AssertLossFunctionIsValid(scaled_loss, 1.792); |
| 189 | } |
| 190 | { |
| 191 | ScaledLoss scaled_loss( |
| 192 | new ComposedLoss( |
| 193 | new HuberLoss(0.8), TAKE_OWNERSHIP, |
| 194 | new TolerantLoss(1.3, 0.5), TAKE_OWNERSHIP), 10, TAKE_OWNERSHIP); |
| 195 | AssertLossFunctionIsValid(scaled_loss, 1.792); |
| 196 | } |
Keir Mierle | 8ebb073 | 2012-04-30 23:09:08 -0700 | [diff] [blame] | 197 | } |
| 198 | |
| 199 | TEST(LossFunction, LossFunctionWrapper) { |
| 200 | // Initialization |
| 201 | HuberLoss loss_function1(1.0); |
| 202 | LossFunctionWrapper loss_function_wrapper(new HuberLoss(1.0), |
| 203 | TAKE_OWNERSHIP); |
| 204 | |
| 205 | double s = 0.862; |
| 206 | double rho_gold[3]; |
| 207 | double rho[3]; |
| 208 | loss_function1.Evaluate(s, rho_gold); |
| 209 | loss_function_wrapper.Evaluate(s, rho); |
| 210 | for (int i = 0; i < 3; ++i) { |
| 211 | EXPECT_NEAR(rho[i], rho_gold[i], 1e-12); |
| 212 | } |
| 213 | |
| 214 | // Resetting |
| 215 | HuberLoss loss_function2(0.5); |
| 216 | loss_function_wrapper.Reset(new HuberLoss(0.5), TAKE_OWNERSHIP); |
| 217 | loss_function_wrapper.Evaluate(s, rho); |
| 218 | loss_function2.Evaluate(s, rho_gold); |
| 219 | for (int i = 0; i < 3; ++i) { |
| 220 | EXPECT_NEAR(rho[i], rho_gold[i], 1e-12); |
| 221 | } |
| 222 | |
| 223 | // Not taking ownership. |
| 224 | HuberLoss loss_function3(0.3); |
| 225 | loss_function_wrapper.Reset(&loss_function3, DO_NOT_TAKE_OWNERSHIP); |
| 226 | loss_function_wrapper.Evaluate(s, rho); |
| 227 | loss_function3.Evaluate(s, rho_gold); |
| 228 | for (int i = 0; i < 3; ++i) { |
| 229 | EXPECT_NEAR(rho[i], rho_gold[i], 1e-12); |
| 230 | } |
| 231 | } |
| 232 | |
| 233 | } // namespace internal |
| 234 | } // namespace ceres |