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):
"""
Calculates the mean squared 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_squared_errors = 0.0
self._num_examples = 0
@reinit__is_reduced
def update(self, output: Sequence[torch.Tensor]) -> None:
y_pred, y = output
squared_errors = torch.pow(y_pred - y.view_as(y_pred), 2)
self._sum_of_squared_errors += torch.sum(squared_errors).item()
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 / self._num_examples