added emb similarity (#3349)

docs/source/metrics.rst

@@ -315,6 +315,12 @@ dice_score (F)
 .. autofunction:: pytorch_lightning.metrics.functional.dice_score
     :noindex:
 
+embedding_similarity (F)
+^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autofunction:: pytorch_lightning.metrics.functional.embedding_similarity
+    :noindex:
+
 f1_score (F)
 ^^^^^^^^^^^^
 

pytorch_lightning/metrics/functional/__init__.py

@@ -29,3 +29,6 @@
     rmsle,
     ssim
 )
+from pytorch_lightning.metrics.functional.self_supervised import (
+    embedding_similarity
+)

pytorch_lightning/metrics/functional/self_supervised.py

@@ -0,0 +1,49 @@
+import torch
+
+
+def embedding_similarity(
+        batch: torch.Tensor,
+        similarity: str = 'cosine',
+        reduction: str = 'none',
+        zero_diagonal: bool = True
+) -> torch.Tensor:
+    """
+    Computes representation similarity
+
+    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:
+        batch: (batch, dim)
+        similarity: 'dot' or 'cosine'
+        reduction: 'none', 'sum', 'mean' (all along dim -1)
+        zero_diagonal: if True, the diagonals are set to zero
+
+    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
+    """
+    if similarity == 'cosine':
+        norm = torch.norm(batch, p=2, dim=1)
+        batch = batch / norm.unsqueeze(1)
+
+    sqr_mtx = batch.mm(batch.transpose(1, 0))
+
+    if zero_diagonal:
+        sqr_mtx = sqr_mtx.fill_diagonal_(0)
+
+    if reduction == 'mean':
+        sqr_mtx = sqr_mtx.mean(dim=-1)
+
+    return sqr_mtx
+
+
+if __name__ == '__main__':
+    a = torch.rand(3, 5)
+
+    print(embedding_similarity(a, 'cosine'))