# Copyright The 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 collections.abc import Sequence from typing import Any, List, Optional, Union 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 from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE if not _MATPLOTLIB_AVAILABLE: __doctest_skip__ = ["CosineSimilarity.plot"] class CosineSimilarity(Metric): r"""Compute the `Cosine Similarity`_. .. 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. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): Predicted float tensor with shape ``(N,d)`` - ``target`` (:class:`~torch.Tensor`): Ground truth float tensor with shape ``(N,d)`` As output of ``forward`` and ``compute`` the metric returns the following output: - ``cosine_similarity`` (:class:`~torch.Tensor`): A float tensor with the cosine similarity 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 torch import tensor >>> from torchmetrics.regression import CosineSimilarity >>> target = tensor([[0, 1], [1, 1]]) >>> preds = 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 plot_lower_bound: float = 0.0 plot_upper_bound: float = 1.0 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: """Update metric states with predictions and targets.""" preds, target = _cosine_similarity_update(preds, target) self.preds.append(preds) self.target.append(target) def compute(self) -> Tensor: """Compute metric.""" preds = dim_zero_cat(self.preds) target = dim_zero_cat(self.target) return _cosine_similarity_compute(preds, target, self.reduction) def plot( self, val: Optional[Union[Tensor, Sequence[Tensor]]] = None, ax: Optional[_AX_TYPE] = None ) -> _PLOT_OUT_TYPE: """Plot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> from torch import randn >>> # Example plotting a single value >>> from torchmetrics.regression import CosineSimilarity >>> metric = CosineSimilarity() >>> metric.update(randn(10,2), randn(10,2)) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> from torch import randn >>> # Example plotting multiple values >>> from torchmetrics.regression import CosineSimilarity >>> metric = CosineSimilarity() >>> values = [] >>> for _ in range(10): ... values.append(metric(randn(10,2), randn(10,2))) >>> fig, ax = metric.plot(values) """ return self._plot(val, ax)