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Source code for ignite.metrics.mean_pairwise_distance

from __future__ import division

import torch
from torch.nn.functional import pairwise_distance

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


[docs]class MeanPairwiseDistance(Metric): """ Calculates the mean pairwise distance: 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}`. """ def __init__(self, p=2, eps=1e-6, output_transform=lambda x: x, device=None): super(MeanPairwiseDistance, self).__init__(output_transform, device=device) self._p = p self._eps = eps @reinit__is_reduced def reset(self): self._sum_of_distances = 0.0 self._num_examples = 0 @reinit__is_reduced def update(self, output): y_pred, y = output distances = pairwise_distance(y_pred, y, p=self._p, eps=self._eps) self._sum_of_distances += torch.sum(distances).item() self._num_examples += y.shape[0] @sync_all_reduce("_sum_of_distances", "_num_examples") def compute(self): if self._num_examples == 0: raise NotComputableError('MeanAbsoluteError must have at least one example before it can be computed.') return self._sum_of_distances / self._num_examples

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