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

from __future__ import division

import torch

from ignite.exceptions import NotComputableError
from ignite.metrics.metric import Metric

[docs]class ManhattanDistance(Metric): 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 `Botchkarev 2018`__. - `update` must receive output of the form `(y_pred, y)`. - `y` and `y_pred` must be of same shape. __ """
[docs] def reset(self): self._sum_of_errors = 0.0
[docs] def update(self, output): y_pred, y = output errors = y.view_as(y_pred) - y_pred self._sum_of_errors += torch.sum(errors).item()
[docs] def compute(self): return self._sum_of_errors

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