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Source code for ignite.metrics.mean_absolute_error

from typing import Sequence, Union

import torch

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

__all__ = ["MeanAbsoluteError"]


[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) -> None: self._sum_of_absolute_errors = 0.0 self._num_examples = 0 @reinit__is_reduced def update(self, output: Sequence[torch.Tensor]) -> None: 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) -> 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_absolute_errors / self._num_examples

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