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)`.
__ https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.DistanceMetric.html
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:: 0.4.3
- Fixed sklearn compatibility.
- Workes with DDP.
"""
[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()