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Source code for ignite.metrics.mean_absolute_error

from __future__ import division

import torch

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


[docs]class MeanAbsoluteError(Metric): """ Calculates the mean absolute error. - `update` must receive output of the form `(y_pred, y)` or `{'y_pred': y_pred, 'y': y}`. """ @reinit__is_reduced def reset(self): self._sum_of_absolute_errors = 0.0 self._num_examples = 0 @reinit__is_reduced def update(self, output): y_pred, y = output absolute_errors = torch.abs(y_pred - y.view_as(y_pred)) self._sum_of_absolute_errors += torch.sum(absolute_errors).item() self._num_examples += y.shape[0] @sync_all_reduce("_sum_of_absolute_errors", "_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_absolute_errors / self._num_examples

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