Source code for ignite.metrics.regression.maximum_absolute_error

from typing import Tuple

import torch

from ignite.exceptions import NotComputableError
from ignite.metrics.metric import reinit__is_reduced, sync_all_reduce

from ignite.metrics.regression._base import _BaseRegression

[docs]class MaximumAbsoluteError(_BaseRegression): r"""Calculates the Maximum Absolute Error. .. math:: \text{MaxAE} = \max_{j=1,n} \left( \lvert A_j-P_j \rvert \right) where :math:`A_j` is the ground truth and :math:`P_j` is the predicted value. More details can be found in `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 = MaximumAbsoluteError() metric.attach(default_evaluator, 'mae') y_true = torch.tensor([0., 1., 2., 3., 4., 5.]) y_pred = y_true * 0.75 state =[[y_pred, y_true]]) print(state.metrics['mae']) .. testoutput:: 1.25... .. versionchanged:: 0.4.5 - Works with DDP. """ _state_dict_all_req_keys = ("_max_of_absolute_errors",)
[docs] @reinit__is_reduced def reset(self) -> None: self._max_of_absolute_errors: float = -1
def _update(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None: y_pred, y = output[0].detach(), output[1].detach() mae = torch.abs(y_pred - y.view_as(y_pred)).max().item() if self._max_of_absolute_errors < mae: self._max_of_absolute_errors = mae
[docs] @sync_all_reduce("_max_of_absolute_errors:MAX") def compute(self) -> float: if self._max_of_absolute_errors < 0: raise NotComputableError("MaximumAbsoluteError must have at least one example before it can be computed.") return self._max_of_absolute_errors

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