F.cosine_similarity will fail to compute the similarity along a specified dimension when the other dimensions differ. For example, if x is a 10x64 tensor, and y is a 20x64 tensor, then it is expected to get a 10x20 matrix when calling cosine_scores. However, that function won't work:
>>> x = torch.randn(10, 64)
>>> y = torch.randn(20, 64)
>>> F.cosine_similarity(x, y, dim=1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
RuntimeError: The size of tensor a (10) must match the size of tensor b (20) at non-singleton dimension 0
Since it is not used anywhere else in the repo and the paper, maybe it would be a good idea to remove cosine_scores?
Reference: https://github.com/facebookresearch/DPR/blob/d9f3e41bb0087687fa182a4d580711188fd82df9/dpr/models/biencoder.py#L57
F.cosine_similaritywill fail to compute the similarity along a specified dimension when the other dimensions differ. For example, ifxis a 10x64 tensor, and y is a 20x64 tensor, then it is expected to get a 10x20 matrix when callingcosine_scores. However, that function won't work:Since it is not used anywhere else in the repo and the paper, maybe it would be a good idea to remove
cosine_scores?