74 lines
2.6 KiB
Python
74 lines
2.6 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import Any
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import torch
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from pytorch_lightning.metrics.functional.self_supervised import embedding_similarity
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from pytorch_lightning.metrics.metric import TensorMetric
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class EmbeddingSimilarity(TensorMetric):
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"""
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Computes similarity between embeddings
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Example:
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>>> embeddings = torch.tensor([[1., 2., 3., 4.], [1., 2., 3., 4.], [4., 5., 6., 7.]])
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>>> embedding_similarity(embeddings)
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tensor([[0.0000, 1.0000, 0.9759],
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[1.0000, 0.0000, 0.9759],
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[0.9759, 0.9759, 0.0000]])
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"""
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def __init__(
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self,
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similarity: str = 'cosine',
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zero_diagonal: bool = True,
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reduction: str = 'mean',
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reduce_group: Any = None
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):
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"""
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Args:
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similarity: 'dot' or 'cosine'
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reduction: 'none', 'sum', 'mean' (all along dim -1)
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zero_diagonal: if True, the diagonals are set to zero
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reduce_group: the process group to reduce metric results from DDP
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"""
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super().__init__(name='embedding_similarity',
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reduce_group=reduce_group)
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assert similarity in ('dot', 'cosine')
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self.similarity = similarity
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isinstance(zero_diagonal, bool)
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self.zero_diagonal = zero_diagonal
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assert reduction in ('none', 'sum', 'mean')
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self.reduction = reduction
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def forward(self, batch: torch.Tensor) -> torch.Tensor:
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"""
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Actual metric computation
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Args:
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batch: tensor containing embeddings with shape (batch_size, dim)
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Return:
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A square matrix (batch, batch) with the similarity scores between all elements
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If sum or mean are used, then returns (b, 1) with the reduced value for each row
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"""
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return embedding_similarity(batch,
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similarity=self.similarity,
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zero_diagonal=self.zero_diagonal,
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reduction=self.reduction)
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