Source code for ignite.metrics.cosine_similarity

from typing import Callable, Sequence, Union

import torch

from ignite.exceptions import NotComputableError
from ignite.metrics.metric import Metric, reinit__is_reduced, sync_all_reduce

__all__ = ["CosineSimilarity"]

[docs]class CosineSimilarity(Metric): r"""Calculates the mean of the `cosine similarity <>`_. .. math:: \text{cosine\_similarity} = \frac{1}{N} \sum_{i=1}^N \frac{x_i \cdot y_i}{\max ( \| x_i \|_2 \| y_i \|_2 , \epsilon)} where :math:`y_{i}` is the prediction tensor and :math:`x_{i}` is ground true tensor. - ``update`` must receive output of the form ``(y_pred, y)``. Args: eps: a small value to avoid division by zero. Default: 1e-8 output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. Examples: To use with ``Engine`` and ``process_function``, simply attach the metric instance to the engine. The output of the engine's ``process_function`` needs to be in the format of ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y, ...}``. If not, ``output_tranform`` can be added to the metric to transform the output into the form expected by the metric. ``y_pred`` and ``y`` should have the same shape. For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`. .. include:: defaults.rst :start-after: :orphan: .. testcode:: metric = CosineSimilarity() metric.attach(default_evaluator, 'cosine_similarity') preds = torch.tensor([ [1, 2, 4, 1], [2, 3, 1, 5], [1, 3, 5, 1], [1, 5, 1 ,11] ]).float() target = torch.tensor([ [1, 5, 1 ,11], [1, 3, 5, 1], [2, 3, 1, 5], [1, 2, 4, 1] ]).float() state =[[preds, target]]) print(state.metrics['cosine_similarity']) .. testoutput:: 0.5080491304397583 """ def __init__( self, eps: float = 1e-8, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu"), ): super().__init__(output_transform, device) self.eps = eps _state_dict_all_req_keys = ("_sum_of_cos_similarities", "_num_examples")
[docs] @reinit__is_reduced def reset(self) -> None: self._sum_of_cos_similarities = torch.tensor(0.0, device=self._device) self._num_examples = 0
[docs] @reinit__is_reduced def update(self, output: Sequence[torch.Tensor]) -> None: y_pred = output[0].flatten(start_dim=1).detach() y = output[1].flatten(start_dim=1).detach() cos_similarities = torch.nn.functional.cosine_similarity(y_pred, y, dim=1, eps=self.eps) self._sum_of_cos_similarities += torch.sum(cos_similarities).to(self._device) self._num_examples += y.shape[0]
[docs] @sync_all_reduce("_sum_of_cos_similarities", "_num_examples") def compute(self) -> float: if self._num_examples == 0: raise NotComputableError("CosineSimilarity must have at least one example before it can be computed.") return self._sum_of_cos_similarities.item() / self._num_examples

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