class ignite.contrib.metrics.regression.ManhattanDistance(output_transform=<function Metric.<lambda>>, device=device(type='cpu'))[source]#

Calculates the Manhattan Distance.

MD=j=1nAjPj\text{MD} = \sum_{j=1}^n |A_j - P_j|

where AjA_j is the ground truth and PjP_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__.

  • output_transform (Callable) – a callable that is used to transform the 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 (Union[str, torch.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.

Changed in version 0.4.3:

  • Fixed sklearn compatibility.

  • Workes with DDP.



Computes the metric based on it's accumulated state.


Resets the metric to it's initial state.


Computes the metric based on it’s accumulated state.

By default, this is called at the end of each epoch.


the actual quantity of interest. However, if a Mapping is returned, it will be (shallow) flattened into engine.state.metrics when completed() is called.

Return type



NotComputableError – raised when the metric cannot be computed.


Resets the metric to it’s initial state.

By default, this is called at the start of each epoch.

Return type