MeanPairwiseDistance#
- class ignite.metrics.MeanPairwiseDistance(p=2, eps=1e-06, output_transform=<function MeanPairwiseDistance.<lambda>>, device=device(type='cpu'))[source]#
Calculates the mean
PairwiseDistance
. Average of pairwise distances computed on provided batches.update
must receive output of the form(y_pred, y)
or{'y_pred': y_pred, 'y': y}
.
- Parameters
p (int) – the norm degree. Default: 2
eps (float) – Small value to avoid division by zero. Default: 1e-6
output_transform (Callable) – a callable that is used to transform the
Engine
’sprocess_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, device]) – specifies which device updates are accumulated on. Setting the metric’s device to be the same as your
update
arguments ensures theupdate
method is non-blocking. By default, CPU.
Examples
To use with
Engine
andprocess_function
, simply attach the metric instance to the engine. The output of the engine’sprocess_function
needs to be in the format of(y_pred, y)
or{'y_pred': y_pred, 'y': y, ...}
. If not,output_tranform
can be added to the metric to transform the output into the form expected by the metric.y_pred
andy
should have the same shape.metric = MeanPairwiseDistance(p=4) metric.attach(default_evaluator, 'mpd') preds = torch.Tensor([ [1, 2, 4, 1], [2, 3, 1, 5], [1, 3, 5, 1], [1, 5, 1 ,11] ]) target = preds * 0.75 state = default_evaluator.run([[preds, target]]) print(state.metrics['mpd'])
1.5955...
Methods
Computes the metric based on it's accumulated state.
Resets the metric to it's initial state.
Updates the metric's state using the passed batch output.
- compute()[source]#
Computes the metric based on it’s accumulated state.
By default, this is called at the end of each epoch.
- Returns
- the actual quantity of interest. However, if a
Mapping
is returned, it will be (shallow) flattened into engine.state.metrics whencompleted()
is called. - Return type
Any
- Raises
NotComputableError – raised when the metric cannot be computed.