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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"]
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.
skip_unrolling: specifies whether output should be unrolled before being fed to update method. Should be
true for multi-output model, for example, if ``y_pred`` contains multi-ouput as ``(y_pred_a, y_pred_b)``
Alternatively, ``output_transform`` can be used to handle this.
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
.. versionchanged:: 0.5.1
``skip_unrolling`` argument is added.
"""
_state_dict_all_req_keys = ("_sum_of_squared_errors", "_num_examples")
@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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