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cosine_similarity.py
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cosine_similarity.py
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# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Any, List
import torch
from torch import Tensor
from typing_extensions import Literal
from torchmetrics.functional.regression.cosine_similarity import _cosine_similarity_compute, _cosine_similarity_update
from torchmetrics.metric import Metric
from torchmetrics.utilities.data import dim_zero_cat
class CosineSimilarity(Metric):
r"""
Computes the `Cosine Similarity`_
between targets and predictions:
.. math::
cos_{sim}(x,y) = \frac{x \cdot y}{||x|| \cdot ||y||} =
\frac{\sum_{i=1}^n x_i y_i}{\sqrt{\sum_{i=1}^n x_i^2}\sqrt{\sum_{i=1}^n y_i^2}}
where :math:`y` is a tensor of target values, and :math:`x` is a tensor of predictions.
Forward accepts
- ``preds`` (float tensor): ``(N,d)``
- ``target`` (float tensor): ``(N,d)``
Args:
reduction: how to reduce over the batch dimension using 'sum', 'mean' or 'none' (taking the individual scores)
kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
Example:
>>> from torchmetrics import CosineSimilarity
>>> target = torch.tensor([[0, 1], [1, 1]])
>>> preds = torch.tensor([[0, 1], [0, 1]])
>>> cosine_similarity = CosineSimilarity(reduction = 'mean')
>>> cosine_similarity(preds, target)
tensor(0.8536)
"""
is_differentiable: bool = True
higher_is_better: bool = True
full_state_update: bool = False
preds: List[Tensor]
target: List[Tensor]
def __init__(
self,
reduction: Literal["mean", "sum", "none", None] = "sum",
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
allowed_reduction = ("sum", "mean", "none", None)
if reduction not in allowed_reduction:
raise ValueError(f"Expected argument `reduction` to be one of {allowed_reduction} but got {reduction}")
self.reduction = reduction
self.add_state("preds", [], dist_reduce_fx="cat")
self.add_state("target", [], dist_reduce_fx="cat")
def update(self, preds: Tensor, target: Tensor) -> None: # type: ignore
"""Update metric states with predictions and targets.
Args:
preds: Predicted tensor with shape ``(N,d)``
target: Ground truth tensor with shape ``(N,d)``
"""
preds, target = _cosine_similarity_update(preds, target)
self.preds.append(preds)
self.target.append(target)
def compute(self) -> Tensor:
preds = dim_zero_cat(self.preds)
target = dim_zero_cat(self.target)
return _cosine_similarity_compute(preds, target, self.reduction)