Min / Max

Module Interface

class torchmetrics.MinMaxMetric(base_metric, **kwargs)

Wrapper Metric that tracks both the minimum and maximum of a scalar/tensor across an experiment. The min/max value will be updated each time .compute is called.

Parameters

• base_metric (Metric) – The metric of which you want to keep track of its maximum and minimum values.

• kwargs (Any) – Additional keyword arguments, see Advanced metric settings for more info.

Raises

ValueError – If base_metric` argument is not a subclasses instance of ``torchmetrics.Metric

Example::

>>> import torch
>>> from torchmetrics import MinMaxMetric
>>> from torchmetrics.classification import BinaryAccuracy
>>> from pprint import pprint
>>> base_metric = BinaryAccuracy()
>>> minmax_metric = MinMaxMetric(base_metric)
>>> preds_1 = torch.Tensor([[0.1, 0.9], [0.2, 0.8]])
>>> preds_2 = torch.Tensor([[0.9, 0.1], [0.2, 0.8]])
>>> labels = torch.Tensor([[0, 1], [0, 1]]).long()
>>> pprint(minmax_metric(preds_1, labels))
{'max': tensor(1.), 'min': tensor(1.), 'raw': tensor(1.)}
>>> pprint(minmax_metric.compute())
{'max': tensor(1.), 'min': tensor(1.), 'raw': tensor(1.)}
>>> minmax_metric.update(preds_2, labels)
>>> pprint(minmax_metric.compute())
{'max': tensor(1.), 'min': tensor(0.7500), 'raw': tensor(0.7500)}

Initializes internal Module state, shared by both nn.Module and ScriptModule.

compute()

Computes the underlying metric as well as max and min values for this metric.

Returns a dictionary that consists of the computed value (raw), as well as the minimum (min) and maximum (max) values.

Return type

Dict[str, Tensor]

reset()

Sets max_val and min_val to the initialization bounds and resets the base metric.

Return type

None

update(*args, **kwargs)

Updates the underlying metric.

Return type

None