Shortcuts

Source code for ignite.contrib.metrics.regression.maximum_absolute_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 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)`. __ https://arxiv.org/abs/1809.03006 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. .. versionchanged:: 0.4.5 - Works with DDP. """
[docs] @reinit__is_reduced def reset(self) -> None: self._max_of_absolute_errors = -1 # type: float
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

© Copyright 2022, PyTorch-Ignite Contributors. Last updated on 10/03/2022, 1:27:29 PM.

Built with Sphinx using a theme provided by Read the Docs.