Source code for ignite.contrib.metrics.regression.mean_error

from typing import Tuple

import torch

from ignite.contrib.metrics.regression._base import _BaseRegression
from ignite.exceptions import NotComputableError
from ignite.metrics.metric import reinit__is_reduced, sync_all_reduce

[docs]class MeanError(_BaseRegression): r"""Calculates the Mean Error. .. math:: \text{ME} = \frac{1}{n}\sum_{j=1}^n (A_j - P_j) where :math:`A_j` is the ground truth and :math:`P_j` is the predicted value. More details can be found in the reference `Botchkarev 2018`__. - ``update`` must receive output of the form ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. - `y` and `y_pred` must be of same shape `(N, )` or `(N, 1)`. __ Parameters are inherited from ``Metric.__init__``. 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 format of ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y, ...}``. .. include:: defaults.rst :start-after: :orphan: .. testcode:: metric = MeanError() metric.attach(default_evaluator, 'me') y_true = torch.tensor([0., 1., 2., 3., 4., 5.]) y_pred = y_true * 0.75 state =[[y_pred, y_true]]) print(state.metrics['me']) .. testoutput:: 0.625... """
[docs] @reinit__is_reduced def reset(self) -> None: self._sum_of_errors = torch.tensor(0.0, device=self._device) self._num_examples = 0
def _update(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None: y_pred, y = output[0].detach(), output[1].detach() errors = y.view_as(y_pred) - y_pred self._sum_of_errors += torch.sum(errors).item() self._num_examples += y.shape[0]
[docs] @sync_all_reduce("_sum_of_errors", "_num_examples") def compute(self) -> float: if self._num_examples == 0: raise NotComputableError("MeanError must have at least one example before it can be computed.") return self._sum_of_errors.item() / self._num_examples

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