Source code for ignite.metrics.mean_squared_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__ = ["MeanSquaredError"]

[docs]class MeanSquaredError(Metric): r"""Calculates the `mean squared error <>`_. .. math:: \text{MSE} = \frac{1}{N} \sum_{i=1}^N \left(y_{i} - x_{i} \right)^2 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)`` or ``{'y_pred': y_pred, 'y': y}``. """ @reinit__is_reduced def reset(self) -> None: self._sum_of_squared_errors = torch.tensor(0.0, device=self._device) self._num_examples = 0 @reinit__is_reduced def update(self, output: Sequence[torch.Tensor]) -> None: y_pred, y = output[0].detach(), output[1].detach() squared_errors = torch.pow(y_pred - y.view_as(y_pred), 2) self._sum_of_squared_errors += torch.sum(squared_errors).to(self._device) self._num_examples += y.shape[0] @sync_all_reduce("_sum_of_squared_errors", "_num_examples") def compute(self) -> Union[float, torch.Tensor]: if self._num_examples == 0: raise NotComputableError("MeanSquaredError must have at least one example before it can be computed.") return self._sum_of_squared_errors.item() / self._num_examples

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