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Source code for ignite.metrics.mean_pairwise_distance

from typing import Callable, Sequence, Union

import torch
from torch.nn.functional import pairwise_distance

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

__all__ = ["MeanPairwiseDistance"]


[docs]class MeanPairwiseDistance(Metric): """Calculates the mean :class:`~torch.nn.PairwiseDistance`. Average of pairwise distances computed on provided batches. - ``update`` must receive output of the form ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. Args: p: the norm degree. Default: 2 eps: Small value to avoid division by zero. Default: 1e-6 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 = MeanPairwiseDistance(p=4) metric.attach(default_evaluator, 'mpd') preds = torch.tensor([ [1, 2, 4, 1], [2, 3, 1, 5], [1, 3, 5, 1], [1, 5, 1 ,11] ]) target = preds * 0.75 state = default_evaluator.run([[preds, target]]) print(state.metrics['mpd']) .. testoutput:: 1.5955... """ def __init__( self, p: int = 2, eps: float = 1e-6, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu"), ) -> None: super(MeanPairwiseDistance, self).__init__(output_transform, device=device) self._p = p self._eps = eps
[docs] @reinit__is_reduced def reset(self) -> None: self._sum_of_distances = 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, y = output[0].detach(), output[1].detach() distances = pairwise_distance(y_pred, y, p=self._p, eps=self._eps) self._sum_of_distances += torch.sum(distances).to(self._device) self._num_examples += y.shape[0]
[docs] @sync_all_reduce("_sum_of_distances", "_num_examples") def compute(self) -> Union[float, torch.Tensor]: if self._num_examples == 0: raise NotComputableError("MeanAbsoluteError must have at least one example before it can be computed.") return self._sum_of_distances.item() / self._num_examples

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