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

from typing import Tuple

import torch

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

[docs]class ManhattanDistance(_BaseRegression): r"""Calculates the Manhattan Distance. .. math:: \text{MD} = \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 `scikit-learn distance metrics`__. - ``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 = ManhattanDistance() metric.attach(default_evaluator, 'manhattan') y_true = torch.tensor([0., 1., 2., 3., 4., 5.]) y_pred = y_true * 0.75 state =[[y_pred, y_true]]) print(state.metrics['manhattan']) .. testoutput:: 3.75... .. versionchanged:: 0.4.3 - Fixed sklearn compatibility. - Workes with DDP. """ _state_dict_all_req_keys = ("_sum_of_errors",)
[docs] @reinit__is_reduced def reset(self) -> None: self._sum_of_errors = torch.tensor(0.0, device=self._device)
def _update(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None: y_pred, y = output errors = torch.abs(y - y_pred) self._sum_of_errors += torch.sum(errors).to(self._device)
[docs] @sync_all_reduce("_sum_of_errors") def compute(self) -> float: return self._sum_of_errors.item()

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