├── .gitattributes ├── jaccard_ce_loss_smp.py ├── .gitignore ├── README.md ├── unified_focal_loss_pytorch.py └── LICENSE /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | -------------------------------------------------------------------------------- /jaccard_ce_loss_smp.py: -------------------------------------------------------------------------------- 1 | import segmentation_models_pytorch as smp 2 | import torch.nn as nn 3 | 4 | class JaccardCELoss(smp.utils.base.Loss): 5 | def __init__(self, alpha=1.0, beta=0.5, **kwargs): 6 | super().__init__(**kwargs) 7 | self.alpha = alpha 8 | self.beta = beta 9 | 10 | self.jaccardloss=smp.losses.JaccardLoss(mode='multiclass') 11 | self.jaccardloss.__name__ = 'jaccard_loss' 12 | 13 | self.celoss = nn.CrossEntropyLoss() 14 | self.celoss.__name__ = 'ce_loss' 15 | 16 | def forward(self, y_pred, y_true): 17 | return self.alpha * self.celoss.forward(y_pred, y_true) - self.beta * self.jaccardloss.forward(y_pred, y_true) -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | MANIFEST 27 | 28 | # PyInstaller 29 | # Usually these files are written by a python script from a template 30 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 31 | *.manifest 32 | *.spec 33 | 34 | # Installer logs 35 | pip-log.txt 36 | pip-delete-this-directory.txt 37 | 38 | # Unit test / coverage reports 39 | htmlcov/ 40 | .tox/ 41 | .nox/ 42 | .coverage 43 | .coverage.* 44 | .cache 45 | nosetests.xml 46 | coverage.xml 47 | *.cover 48 | .hypothesis/ 49 | .pytest_cache/ 50 | 51 | # Translations 52 | *.mo 53 | *.pot 54 | 55 | # Django stuff: 56 | *.log 57 | local_settings.py 58 | db.sqlite3 59 | 60 | # Flask stuff: 61 | instance/ 62 | .webassets-cache 63 | 64 | # Scrapy stuff: 65 | .scrapy 66 | 67 | # Sphinx documentation 68 | docs/_build/ 69 | 70 | # PyBuilder 71 | target/ 72 | 73 | # Jupyter Notebook 74 | .ipynb_checkpoints 75 | 76 | # IPython 77 | profile_default/ 78 | ipython_config.py 79 | 80 | # pyenv 81 | .python-version 82 | 83 | # celery beat schedule file 84 | celerybeat-schedule 85 | 86 | # SageMath parsed files 87 | *.sage.py 88 | 89 | # Environments 90 | .env 91 | .venv 92 | env/ 93 | venv/ 94 | ENV/ 95 | env.bak/ 96 | venv.bak/ 97 | 98 | # Spyder project settings 99 | .spyderproject 100 | .spyproject 101 | 102 | # Rope project settings 103 | .ropeproject 104 | 105 | # mkdocs documentation 106 | /site 107 | 108 | # mypy 109 | .mypy_cache/ 110 | .dmypy.json 111 | dmypy.json 112 | 113 | # Pyre type checker 114 | .pyre/ 115 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # compound-loss-pytorch 2 | Compound loss for pytorch 3 | 4 | 5 | ## [jaccard_ce_loss_smp.py](https://github.com/oikosohn/compound-loss-pytorch/blob/main/jaccard_ce_loss_smp.py) 6 | Jaccard CE Loss from [Feature Pyramid Network for Multi-Class Land Segmentation](https://arxiv.org/abs/1806.03510) using segmentation_models.pytorch 7 | 8 | 9 | ## [unified_focal_loss_pytorch.py](https://github.com/oikosohn/compound-loss-pytorch/blob/main/unified_focal_loss_pytorch.py) 10 | PyTorch implementation of [Unified Focal loss](https://arxiv.org/abs/2102.04525) 11 | - [Official tensorflow repository](https://github.com/mlyg/unified-focal-loss) 12 | 13 | Losses 14 | - Focal loss (symmetric and asymmetric) 15 | - Focal Tversky loss (symmetric and asymmetric) 16 | - Unified Focal loss (symmetric and asymmetric) 17 | 18 | Todo 19 | - [ ] Fix [[Issue #3] Incompatible pytorch tensor shape](https://github.com/oikosohn/compound-loss-pytorch/issues/3) 20 | - [X] SymmetricFocalLoss 21 | - [X] AsymmetricFocalLoss 22 | - [ ] SymmetricFocalTverskyLoss 23 | - [ ] AsymmetricFocalTverskyLoss 24 | 25 | ## References 26 | ```bibtex 27 | @misc{https://doi.org/10.48550/arxiv.1806.03510, 28 | doi = {10.48550/ARXIV.1806.03510}, 29 | url = {https://arxiv.org/abs/1806.03510}, 30 | author = {Seferbekov, Selim S. and Iglovikov, Vladimir I. and Buslaev, Alexander V. and Shvets, Alexey A.}, 31 | keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences}, 32 | title = {Feature Pyramid Network for Multi-Class Land Segmentation}, 33 | publisher = {arXiv}, 34 | year = {2018}, 35 | copyright = {Creative Commons Attribution 4.0 International} 36 | } 37 | 38 | @misc{https://doi.org/10.48550/arxiv.2102.04525, 39 | doi = {10.48550/ARXIV.2102.04525}, 40 | url = {https://arxiv.org/abs/2102.04525}, 41 | author = {Yeung, Michael and Sala, Evis and Schönlieb, Carola-Bibiane and Rundo, Leonardo}, 42 | keywords = {Image and Video Processing (eess.IV), Computer Vision and Pattern Recognition (cs.CV), Machine Learning (cs.LG), FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Computer and information sciences, FOS: Computer and information sciences, I.4.6; J.3}, 43 | title = {Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation}, 44 | publisher = {arXiv}, 45 | year = {2021}, 46 | copyright = {arXiv.org perpetual, non-exclusive license} 47 | } 48 | 49 | ``` 50 | -------------------------------------------------------------------------------- /unified_focal_loss_pytorch.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | 4 | 5 | # Helper function to enable loss function to be flexibly used for 6 | # both 2D or 3D image segmentation - source: https://github.com/frankkramer-lab/MIScnn 7 | 8 | def identify_axis(shape): 9 | # Three dimensional 10 | if len(shape) == 5 : return [2,3,4] 11 | 12 | # Two dimensional 13 | elif len(shape) == 4 : return [2,3] 14 | 15 | # Exception - Unknown 16 | else : raise ValueError('Metric: Shape of tensor is neither 2D or 3D.') 17 | 18 | 19 | class SymmetricFocalLoss(nn.Module): 20 | """ 21 | Parameters 22 | ---------- 23 | delta : float, optional 24 | controls weight given to false positive and false negatives, by default 0.7 25 | gamma : float, optional 26 | Focal Tversky loss' focal parameter controls degree of down-weighting of easy examples, by default 2.0 27 | epsilon : float, optional 28 | clip values to prevent division by zero error 29 | """ 30 | def __init__(self, delta=0.7, gamma=2., epsilon=1e-07): 31 | super(SymmetricFocalLoss, self).__init__() 32 | self.delta = delta 33 | self.gamma = gamma 34 | self.epsilon = epsilon 35 | 36 | def forward(self, y_pred, y_true): 37 | 38 | y_pred = torch.clamp(y_pred, self.epsilon, 1. - self.epsilon) 39 | cross_entropy = -y_true * torch.log(y_pred) 40 | 41 | # Calculate losses separately for each class 42 | back_ce = torch.pow(1 - y_pred[:,0,:,:], self.gamma) * cross_entropy[:,0,:,:] 43 | back_ce = (1 - self.delta) * back_ce 44 | 45 | fore_ce = torch.pow(1 - y_pred[:,1,:,:], self.gamma) * cross_entropy[:,1,:,:] 46 | fore_ce = self.delta * fore_ce 47 | 48 | loss = torch.mean(torch.sum(torch.stack([back_ce, fore_ce], axis=-1), axis=-1)) 49 | 50 | return loss 51 | 52 | 53 | class AsymmetricFocalLoss(nn.Module): 54 | """For Imbalanced datasets 55 | Parameters 56 | ---------- 57 | delta : float, optional 58 | controls weight given to false positive and false negatives, by default 0.25 59 | gamma : float, optional 60 | Focal Tversky loss' focal parameter controls degree of down-weighting of easy examples, by default 2.0 61 | epsilon : float, optional 62 | clip values to prevent division by zero error 63 | """ 64 | def __init__(self, delta=0.7, gamma=2., epsilon=1e-07): 65 | super(AsymmetricFocalLoss, self).__init__() 66 | self.delta = delta 67 | self.gamma = gamma 68 | self.epsilon = epsilon 69 | 70 | def forward(self, y_pred, y_true): 71 | y_pred = torch.clamp(y_pred, self.epsilon, 1. - self.epsilon) 72 | cross_entropy = -y_true * torch.log(y_pred) 73 | 74 | # Calculate losses separately for each class, only suppressing background class 75 | back_ce = torch.pow(1 - y_pred[:,0,:,:], self.gamma) * cross_entropy[:,0,:,:] 76 | back_ce = (1 - self.delta) * back_ce 77 | 78 | fore_ce = cross_entropy[:,1,:,:] 79 | fore_ce = self.delta * fore_ce 80 | 81 | loss = torch.mean(torch.sum(torch.stack([back_ce, fore_ce], axis=-1), axis=-1)) 82 | 83 | return loss 84 | 85 | 86 | class SymmetricFocalTverskyLoss(nn.Module): 87 | """This is the implementation for binary segmentation. 88 | Parameters 89 | ---------- 90 | delta : float, optional 91 | controls weight given to false positive and false negatives, by default 0.7 92 | gamma : float, optional 93 | focal parameter controls degree of down-weighting of easy examples, by default 0.75 94 | smooth : float, optional 95 | smooithing constant to prevent division by 0 errors, by default 0.000001 96 | epsilon : float, optional 97 | clip values to prevent division by zero error 98 | """ 99 | def __init__(self, delta=0.7, gamma=0.75, epsilon=1e-07): 100 | super(SymmetricFocalTverskyLoss, self).__init__() 101 | self.delta = delta 102 | self.gamma = gamma 103 | self.epsilon = epsilon 104 | 105 | def forward(self, y_pred, y_true): 106 | y_pred = torch.clamp(y_pred, self.epsilon, 1. - self.epsilon) 107 | axis = identify_axis(y_true.size()) 108 | 109 | # Calculate true positives (tp), false negatives (fn) and false positives (fp) 110 | tp = torch.sum(y_true * y_pred, axis=axis) 111 | fn = torch.sum(y_true * (1-y_pred), axis=axis) 112 | fp = torch.sum((1-y_true) * y_pred, axis=axis) 113 | dice_class = (tp + self.epsilon)/(tp + self.delta*fn + (1-self.delta)*fp + self.epsilon) 114 | 115 | # Calculate losses separately for each class, enhancing both classes 116 | back_dice = (1-dice_class[:,0]) * torch.pow(1-dice_class[:,0], -self.gamma) 117 | fore_dice = (1-dice_class[:,1]) * torch.pow(1-dice_class[:,1], -self.gamma) 118 | 119 | # Average class scores 120 | loss = torch.mean(torch.stack([back_dice,fore_dice], axis=-1)) 121 | return loss 122 | 123 | 124 | class AsymmetricFocalTverskyLoss(nn.Module): 125 | """This is the implementation for binary segmentation. 126 | Parameters 127 | ---------- 128 | delta : float, optional 129 | controls weight given to false positive and false negatives, by default 0.7 130 | gamma : float, optional 131 | focal parameter controls degree of down-weighting of easy examples, by default 0.75 132 | smooth : float, optional 133 | smooithing constant to prevent division by 0 errors, by default 0.000001 134 | epsilon : float, optional 135 | clip values to prevent division by zero error 136 | """ 137 | def __init__(self, delta=0.7, gamma=0.75, epsilon=1e-07): 138 | super(AsymmetricFocalTverskyLoss, self).__init__() 139 | self.delta = delta 140 | self.gamma = gamma 141 | self.epsilon = epsilon 142 | 143 | def forward(self, y_pred, y_true): 144 | # Clip values to prevent division by zero error 145 | y_pred = torch.clamp(y_pred, self.epsilon, 1. - self.epsilon) 146 | axis = identify_axis(y_true.size()) 147 | 148 | # Calculate true positives (tp), false negatives (fn) and false positives (fp) 149 | tp = torch.sum(y_true * y_pred, axis=axis) 150 | fn = torch.sum(y_true * (1-y_pred), axis=axis) 151 | fp = torch.sum((1-y_true) * y_pred, axis=axis) 152 | dice_class = (tp + self.epsilon)/(tp + self.delta*fn + (1-self.delta)*fp + self.epsilon) 153 | 154 | # Calculate losses separately for each class, only enhancing foreground class 155 | back_dice = (1-dice_class[:,0]) 156 | fore_dice = (1-dice_class[:,1]) * torch.pow(1-dice_class[:,1], -self.gamma) 157 | 158 | # Average class scores 159 | loss = torch.mean(torch.stack([back_dice,fore_dice], axis=-1)) 160 | return loss 161 | 162 | 163 | class SymmetricUnifiedFocalLoss(nn.Module): 164 | """The Unified Focal loss is a new compound loss function that unifies Dice-based and cross entropy-based loss functions into a single framework. 165 | Parameters 166 | ---------- 167 | weight : float, optional 168 | represents lambda parameter and controls weight given to symmetric Focal Tversky loss and symmetric Focal loss, by default 0.5 169 | delta : float, optional 170 | controls weight given to each class, by default 0.6 171 | gamma : float, optional 172 | focal parameter controls the degree of background suppression and foreground enhancement, by default 0.5 173 | epsilon : float, optional 174 | clip values to prevent division by zero error 175 | """ 176 | def __init__(self, weight=0.5, delta=0.6, gamma=0.5): 177 | super(SymmetricUnifiedFocalLoss, self).__init__() 178 | self.weight = weight 179 | self.delta = delta 180 | self.gamma = gamma 181 | 182 | def forward(self, y_pred, y_true): 183 | symmetric_ftl = SymmetricFocalTverskyLoss(delta=self.delta, gamma=self.gamma)(y_pred, y_true) 184 | symmetric_fl = SymmetricFocalLoss(delta=self.delta, gamma=self.gamma)(y_pred, y_true) 185 | if self.weight is not None: 186 | return (self.weight * symmetric_ftl) + ((1-self.weight) * symmetric_fl) 187 | else: 188 | return symmetric_ftl + symmetric_fl 189 | 190 | 191 | class AsymmetricUnifiedFocalLoss(nn.Module): 192 | """The Unified Focal loss is a new compound loss function that unifies Dice-based and cross entropy-based loss functions into a single framework. 193 | Parameters 194 | ---------- 195 | weight : float, optional 196 | represents lambda parameter and controls weight given to asymmetric Focal Tversky loss and asymmetric Focal loss, by default 0.5 197 | delta : float, optional 198 | controls weight given to each class, by default 0.6 199 | gamma : float, optional 200 | focal parameter controls the degree of background suppression and foreground enhancement, by default 0.5 201 | epsilon : float, optional 202 | clip values to prevent division by zero error 203 | """ 204 | def __init__(self, weight=0.5, delta=0.6, gamma=0.2): 205 | super(AsymmetricUnifiedFocalLoss, self).__init__() 206 | self.weight = weight 207 | self.delta = delta 208 | self.gamma = gamma 209 | 210 | def forward(self, y_pred, y_true): 211 | # Obtain Asymmetric Focal Tversky loss 212 | asymmetric_ftl = AsymmetricFocalTverskyLoss(delta=self.delta, gamma=self.gamma)(y_pred, y_true) 213 | 214 | # Obtain Asymmetric Focal loss 215 | asymmetric_fl = AsymmetricFocalLoss(delta=self.delta, gamma=self.gamma)(y_pred, y_true) 216 | 217 | # Return weighted sum of Asymmetrical Focal loss and Asymmetric Focal Tversky loss 218 | if self.weight is not None: 219 | return (self.weight * asymmetric_ftl) + ((1-self.weight) * asymmetric_fl) 220 | else: 221 | return asymmetric_ftl + asymmetric_fl 222 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. Definitions. 8 | 9 | "License" shall mean the terms and conditions for use, reproduction, 10 | and distribution as defined by Sections 1 through 9 of this document. 11 | 12 | "Licensor" shall mean the copyright owner or entity authorized by 13 | the copyright owner that is granting the License. 14 | 15 | "Legal Entity" shall mean the union of the acting entity and all 16 | other entities that control, are controlled by, or are under common 17 | control with that entity. For the purposes of this definition, 18 | "control" means (i) the power, direct or indirect, to cause the 19 | direction or management of such entity, whether by contract or 20 | otherwise, or (ii) ownership of fifty percent (50%) or more of the 21 | outstanding shares, or (iii) beneficial ownership of such entity. 22 | 23 | "You" (or "Your") shall mean an individual or Legal Entity 24 | exercising permissions granted by this License. 25 | 26 | "Source" form shall mean the preferred form for making modifications, 27 | including but not limited to software source code, documentation 28 | source, and configuration files. 29 | 30 | "Object" form shall mean any form resulting from mechanical 31 | transformation or translation of a Source form, including but 32 | not limited to compiled object code, generated documentation, 33 | and conversions to other media types. 34 | 35 | "Work" shall mean the work of authorship, whether in Source or 36 | Object form, made available under the License, as indicated by a 37 | copyright notice that is included in or attached to the work 38 | (an example is provided in the Appendix below). 39 | 40 | "Derivative Works" shall mean any work, whether in Source or Object 41 | form, that is based on (or derived from) the Work and for which the 42 | editorial revisions, annotations, elaborations, or other modifications 43 | represent, as a whole, an original work of authorship. For the purposes 44 | of this License, Derivative Works shall not include works that remain 45 | separable from, or merely link (or bind by name) to the interfaces of, 46 | the Work and Derivative Works thereof. 47 | 48 | "Contribution" shall mean any work of authorship, including 49 | the original version of the Work and any modifications or additions 50 | to that Work or Derivative Works thereof, that is intentionally 51 | submitted to Licensor for inclusion in the Work by the copyright owner 52 | or by an individual or Legal Entity authorized to submit on behalf of 53 | the copyright owner. For the purposes of this definition, "submitted" 54 | means any form of electronic, verbal, or written communication sent 55 | to the Licensor or its representatives, including but not limited to 56 | communication on electronic mailing lists, source code control systems, 57 | and issue tracking systems that are managed by, or on behalf of, the 58 | Licensor for the purpose of discussing and improving the Work, but 59 | excluding communication that is conspicuously marked or otherwise 60 | designated in writing by the copyright owner as "Not a Contribution." 61 | 62 | "Contributor" shall mean Licensor and any individual or Legal Entity 63 | on behalf of whom a Contribution has been received by Licensor and 64 | subsequently incorporated within the Work. 65 | 66 | 2. Grant of Copyright License. Subject to the terms and conditions of 67 | this License, each Contributor hereby grants to You a perpetual, 68 | worldwide, non-exclusive, no-charge, royalty-free, irrevocable 69 | copyright license to reproduce, prepare Derivative Works of, 70 | publicly display, publicly perform, sublicense, and distribute the 71 | Work and such Derivative Works in Source or Object form. 72 | 73 | 3. Grant of Patent License. Subject to the terms and conditions of 74 | this License, each Contributor hereby grants to You a perpetual, 75 | worldwide, non-exclusive, no-charge, royalty-free, irrevocable 76 | (except as stated in this section) patent license to make, have made, 77 | use, offer to sell, sell, import, and otherwise transfer the Work, 78 | where such license applies only to those patent claims licensable 79 | by such Contributor that are necessarily infringed by their 80 | Contribution(s) alone or by combination of their Contribution(s) 81 | with the Work to which such Contribution(s) was submitted. If You 82 | institute patent litigation against any entity (including a 83 | cross-claim or counterclaim in a lawsuit) alleging that the Work 84 | or a Contribution incorporated within the Work constitutes direct 85 | or contributory patent infringement, then any patent licenses 86 | granted to You under this License for that Work shall terminate 87 | as of the date such litigation is filed. 88 | 89 | 4. Redistribution. You may reproduce and distribute copies of the 90 | Work or Derivative Works thereof in any medium, with or without 91 | modifications, and in Source or Object form, provided that You 92 | meet the following conditions: 93 | 94 | (a) You must give any other recipients of the Work or 95 | Derivative Works a copy of this License; and 96 | 97 | (b) You must cause any modified files to carry prominent notices 98 | stating that You changed the files; and 99 | 100 | (c) You must retain, in the Source form of any Derivative Works 101 | that You distribute, all copyright, patent, trademark, and 102 | attribution notices from the Source form of the Work, 103 | excluding those notices that do not pertain to any part of 104 | the Derivative Works; and 105 | 106 | (d) If the Work includes a "NOTICE" text file as part of its 107 | distribution, then any Derivative Works that You distribute must 108 | include a readable copy of the attribution notices contained 109 | within such NOTICE file, excluding those notices that do not 110 | pertain to any part of the Derivative Works, in at least one 111 | of the following places: within a NOTICE text file distributed 112 | as part of the Derivative Works; within the Source form or 113 | documentation, if provided along with the Derivative Works; or, 114 | within a display generated by the Derivative Works, if and 115 | wherever such third-party notices normally appear. The contents 116 | of the NOTICE file are for informational purposes only and 117 | do not modify the License. You may add Your own attribution 118 | notices within Derivative Works that You distribute, alongside 119 | or as an addendum to the NOTICE text from the Work, provided 120 | that such additional attribution notices cannot be construed 121 | as modifying the License. 122 | 123 | You may add Your own copyright statement to Your modifications and 124 | may provide additional or different license terms and conditions 125 | for use, reproduction, or distribution of Your modifications, or 126 | for any such Derivative Works as a whole, provided Your use, 127 | reproduction, and distribution of the Work otherwise complies with 128 | the conditions stated in this License. 129 | 130 | 5. Submission of Contributions. Unless You explicitly state otherwise, 131 | any Contribution intentionally submitted for inclusion in the Work 132 | by You to the Licensor shall be under the terms and conditions of 133 | this License, without any additional terms or conditions. 134 | Notwithstanding the above, nothing herein shall supersede or modify 135 | the terms of any separate license agreement you may have executed 136 | with Licensor regarding such Contributions. 137 | 138 | 6. Trademarks. This License does not grant permission to use the trade 139 | names, trademarks, service marks, or product names of the Licensor, 140 | except as required for reasonable and customary use in describing the 141 | origin of the Work and reproducing the content of the NOTICE file. 142 | 143 | 7. Disclaimer of Warranty. Unless required by applicable law or 144 | agreed to in writing, Licensor provides the Work (and each 145 | Contributor provides its Contributions) on an "AS IS" BASIS, 146 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or 147 | implied, including, without limitation, any warranties or conditions 148 | of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A 149 | PARTICULAR PURPOSE. You are solely responsible for determining the 150 | appropriateness of using or redistributing the Work and assume any 151 | risks associated with Your exercise of permissions under this License. 152 | 153 | 8. Limitation of Liability. In no event and under no legal theory, 154 | whether in tort (including negligence), contract, or otherwise, 155 | unless required by applicable law (such as deliberate and grossly 156 | negligent acts) or agreed to in writing, shall any Contributor be 157 | liable to You for damages, including any direct, indirect, special, 158 | incidental, or consequential damages of any character arising as a 159 | result of this License or out of the use or inability to use the 160 | Work (including but not limited to damages for loss of goodwill, 161 | work stoppage, computer failure or malfunction, or any and all 162 | other commercial damages or losses), even if such Contributor 163 | has been advised of the possibility of such damages. 164 | 165 | 9. Accepting Warranty or Additional Liability. While redistributing 166 | the Work or Derivative Works thereof, You may choose to offer, 167 | and charge a fee for, acceptance of support, warranty, indemnity, 168 | or other liability obligations and/or rights consistent with this 169 | License. However, in accepting such obligations, You may act only 170 | on Your own behalf and on Your sole responsibility, not on behalf 171 | of any other Contributor, and only if You agree to indemnify, 172 | defend, and hold each Contributor harmless for any liability 173 | incurred by, or claims asserted against, such Contributor by reason 174 | of your accepting any such warranty or additional liability. 175 | 176 | END OF TERMS AND CONDITIONS 177 | 178 | APPENDIX: How to apply the Apache License to your work. 179 | 180 | To apply the Apache License to your work, attach the following 181 | boilerplate notice, with the fields enclosed by brackets "[]" 182 | replaced with your own identifying information. (Don't include 183 | the brackets!) The text should be enclosed in the appropriate 184 | comment syntax for the file format. We also recommend that a 185 | file or class name and description of purpose be included on the 186 | same "printed page" as the copyright notice for easier 187 | identification within third-party archives. 188 | 189 | Copyright [yyyy] [name of copyright owner] 190 | 191 | Licensed under the Apache License, Version 2.0 (the "License"); 192 | you may not use this file except in compliance with the License. 193 | You may obtain a copy of the License at 194 | 195 | http://www.apache.org/licenses/LICENSE-2.0 196 | 197 | Unless required by applicable law or agreed to in writing, software 198 | distributed under the License is distributed on an "AS IS" BASIS, 199 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 200 | See the License for the specific language governing permissions and 201 | limitations under the License. 202 | --------------------------------------------------------------------------------