Merge pull request #198 from kozistr/feature/loss-functions

[Feature] Implement loss functions

Author: kozistr

README.rst

@@ -16,7 +16,7 @@ pytorch-optimizer
 
 | **pytorch-optimizer** is optimizer & lr scheduler collections in PyTorch.
 | I just re-implemented (speed & memory tweaks, plug-ins) the algorithm while based on the original paper. Also, It includes useful and practical optimization ideas.
-| Currently, 59 optimizers, 10 lr schedulers, and 10 loss functions are supported!
+| Currently, 59 optimizers, 10 lr schedulers, and 13 loss functions are supported!
 |
 | Highly inspired by `pytorch-optimizer <https://github.com/jettify/pytorch-optimizer>`__.
 
@@ -270,6 +270,10 @@ You can check the supported loss functions with below code.
 +---------------------+-------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------+
 | Bi-Tempered         | *The Principle of Unchanged Optimality in Reinforcement Learning Generalization*                                        |                                                                                   | `https://arxiv.org/abs/1906.03361 <https://arxiv.org/abs/1906.03361>`__                       | `cite <https://ui.adsabs.harvard.edu/abs/2019arXiv190600336I/exportcitation>`__                                      |
 +---------------------+-------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------+
+| Tversky             | *Tversky loss function for image segmentation using 3D fully convolutional deep networks*                               |                                                                                   | `https://arxiv.org/abs/1706.05721 <https://arxiv.org/abs/1706.05721>`__                       | `cite <https://ui.adsabs.harvard.edu/abs/2017arXiv170605721S/exportcitation>`__                                      |
++---------------------+-------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------+
+| Lovasz Hinge        | *A tractable surrogate for the optimization of the intersection-over-union measure in neural networks*                  | `github <https://github.com/bermanmaxim/LovaszSoftmax>`__                         | `https://arxiv.org/abs/1705.08790 <https://arxiv.org/abs/1705.08790>`__                       | `cite <https://github.com/bermanmaxim/LovaszSoftmax#citation>`__                                                     |
++---------------------+-------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------+
 
 Useful Resources
 ----------------

docs/changelogs/v2.11.1.md

@@ -2,8 +2,14 @@
 
 ### Feature
 
+* Implement Tiger optimizer (#192)
+  * [A Tight-fisted Optimizer](https://github.com/bojone/tiger/blob/main/README_en.md)
 * Implement CAME optimizer (#196)
   * [Confidence-guided Adaptive Memory Efficient Optimization](https://aclanthology.org/2023.acl-long.243/) 
+* Implement loss functions (#198)
+  * Tversky Loss : [Tversky loss function for image segmentation using 3D fully convolutional deep networks](https://arxiv.org/abs/1706.05721)
+  * Focal Tversky Loss
+  * Lovasz Hinge Loss : [The Lovász-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks](https://arxiv.org/abs/1705.08790)
 
 ### Diff
 

docs/loss_api.rst

@@ -80,3 +80,27 @@ BinaryBiTemperedLogisticLoss
 
 .. autoclass:: pytorch_optimizer.BinaryBiTemperedLogisticLoss
     :members:
+
+.. _TverskyLoss:
+
+TverskyLoss
+-----------
+
+.. autoclass:: pytorch_optimizer.TverskyLoss
+    :members:
+
+.. _FocalTverskyLoss:
+
+FocalTverskyLoss
+----------------
+
+.. autoclass:: pytorch_optimizer.FocalTverskyLoss
+    :members:
+
+.. _LovaszHingeLoss:
+
+LovaszHingeLoss
+---------------
+
+.. autoclass:: pytorch_optimizer.LovaszHingeLoss
+    :members: