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# 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 <https://en.wikipedia.org/wiki/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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