# 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 \|y_{i} - x_{i}\|^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). Args: output_transform: a callable that is used to transform the :class:~ignite.engine.engine.Engine's process_function's output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. By default, metrics require the output as (y_pred, y) or {'y_pred': y_pred, 'y': y}. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your update arguments ensures the update method is non-blocking. By default, CPU. Examples: To use with Engine and process_function, simply attach the metric instance to the engine. The output of the engine's process_function needs to be in the format of (y_pred, y) or {'y_pred': y_pred, 'y': y, ...}. If not, output_tranform can be added to the metric to transform the output into the form expected by the metric. y_pred and y should have the same shape. For more information on how metric works with :class:~ignite.engine.engine.Engine, visit :ref:attach-engine. .. include:: defaults.rst :start-after: :orphan: .. testcode:: metric = MeanSquaredError() metric.attach(default_evaluator, 'mse') preds = torch.tensor([ [1, 2, 4, 1], [2, 3, 1, 5], [1, 3, 5, 1], [1, 5, 1 ,11] ]) target = preds * 0.75 state = default_evaluator.run([[preds, target]]) print(state.metrics['mse']) .. testoutput:: 3.828125 """
[docs] @reinit__is_reduced def reset(self) -> None: self._sum_of_squared_errors = torch.tensor(0.0, device=self._device) self._num_examples = 0
[docs] @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]
[docs] @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