[Metrics] class based embedding similarity + tests

CHANGELOG.md

@@ -13,6 +13,10 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Added `LightningModule.to_torchscript` to support exporting as `ScriptModule` ([#3258](https://github.com/PyTorchLightning/pytorch-lightning/pull/3258/))
 
+- Added `EmbeddingSimilarity` metric:
+   * functional interface ([#3349](https://github.com/PyTorchLightning/pytorch-lightning/pull/3349))
+   * class based interface + tests ([#3358](https://github.com/PyTorchLightning/pytorch-lightning/pull/3358))
+
 ### Changed
 
 - Changed `LearningRateLogger` to `LearningRateMonitor` ([#3251](https://github.com/PyTorchLightning/pytorch-lightning/pull/3251))
@@ -134,7 +138,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed adding val step argument to metrics ([#2986](https://github.com/PyTorchLightning/pytorch-lightning/pull/2986))
 - Fixed an issue that caused `Trainer.test()` to stall in ddp mode ([#2997](https://github.com/PyTorchLightning/pytorch-lightning/pull/2997))
 - Fixed gathering of results with tensors of varying shape ([#3020](https://github.com/PyTorchLightning/pytorch-lightning/pull/3020))
-- Fixed batch size auto-scaling feature to set the new value on the correct model attribute ([#3043](https://github.com/PyTorchLightning/pytorch-lightning/pull/3043)) 
+- Fixed batch size auto-scaling feature to set the new value on the correct model attribute ([#3043](https://github.com/PyTorchLightning/pytorch-lightning/pull/3043))
 - Fixed automatic batch scaling not working with half precision ([#3045](https://github.com/PyTorchLightning/pytorch-lightning/pull/3045))
 - Fixed setting device to root gpu ([#3042](https://github.com/PyTorchLightning/pytorch-lightning/pull/3042))
 

docs/source/metrics.rst

@@ -162,7 +162,8 @@ EmbeddingSimilarity
 ^^^^^^^^^^^^^^^^^^^
 
 .. autoclass:: pytorch_lightning.metrics.self_supervised.EmbeddingSimilarity
-
+    :noindex:
+
 F1
 ^^
 
@@ -634,4 +635,3 @@ MeanTweedieDeviance (sk)
 
 .. autofunction:: pytorch_lightning.metrics.sklearns.MeanTweedieDeviance
     :noindex:
-

pytorch_lightning/metrics/self_supervised.py

@@ -25,35 +25,30 @@
 
 class EmbeddingSimilarity(TensorMetric):
     """
+    Computes similarity between embeddings
 
+    Example:
+        >>> embeddings = torch.tensor([[1., 2., 3., 4.], [1., 2., 3., 4.], [4., 5., 6., 7.]])
+        >>> embedding_similarity(embeddings)
+        tensor([[0.0000, 1.0000, 0.9759],
+                [1.0000, 0.0000, 0.9759],
+                [0.9759, 0.9759, 0.0000]])
 
     """
-
-    def __init__(self,
-                 similarity: str = 'cosine',
-                 zero_diagonal: bool = True,
-                 reduction: str = 'mean',
-                 reduce_group: Any = None):
+    def __init__(
+            self,
+            similarity: str = 'cosine',
+            zero_diagonal: bool = True,
+            reduction: str = 'mean',
+            reduce_group: Any = None
+    ):
         """
-        Computes similarity between embeddings
-
-        Example:
-
-            >>> embeddings = torch.tensor([[1., 2., 3., 4.], [1., 2., 3., 4.], [4., 5., 6., 7.]])
-            >>> embedding_similarity(embeddings)
-            tensor([[0.0000, 1.0000, 0.9759],
-                    [1.0000, 0.0000, 0.9759],
-                    [0.9759, 0.9759, 0.0000]])
-
         Args:
             similarity: 'dot' or 'cosine'
             reduction: 'none', 'sum', 'mean' (all along dim -1)
             zero_diagonal: if True, the diagonals are set to zero
             reduce_group: the process group to reduce metric results from DDP
 
-        Return:
-            A square matrix (batch, batch) with the similarity scores between all elements
-            If sum or mean are used, then returns (b, 1) with the reduced value for each row
         """
         super().__init__(name='embedding_similarity',
                          reduce_group=reduce_group)