o yi:@sLddlmZmZmZmZddlZddlmZddlmZGdddeZ dS))AnyDictOptionalUnionN)Tensor)MetriccseZdZUdZdZeeed<eed<eed<de de dd ffd d Z d e de dd fd dZ de eeffddZdfdd ZedeeeefdefddZZS) MinMaxMetrica`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. Args: base_metric: The metric of which you want to keep track of its maximum and minimum values. kwargs: Additional keyword arguments, see :ref:`Metric kwargs` 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)} Tfull_state_updatemin_valmax_val base_metrickwargsreturnNc sTtjdi|t|tstd|||_ttd|_ ttd|_ dS)NzMExpected base metric to be an instance of `torchmetrics.Metric` but received infz-inf) super__init__ isinstancer ValueError _base_metrictorchtensorfloatr r )selfr r  __class__rP/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/wrappers/minmax.pyr;s zMinMaxMetric.__init__argscOs|jj|i|dS)zUpdates the underlying metric.N)rupdate)rrr rrrrIszMinMaxMetric.updatecCs~|j}||std||j|j|kr|n|j|j|_|j|j|kr/|n|j|j|_||j|jdS)zComputes 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. z\Returned value from base metric should be a scalar (int, float or tensor of size 1, but got )rawmaxmin)rcompute_is_suitable_val RuntimeErrorr todevicer )rvalrrrr"Ms  &&zMinMaxMetric.computecst|jdS)zYSets ``max_val`` and ``min_val`` to the initialization bounds and resets the base metric.N)rresetr)rrrrr(\s zMinMaxMetric.resetr'cCs,t|ttfr dSt|tr|dkSdS)z3Utility function that checks whether min/max value.TF)rintrrnumel)r'rrrr#as   zMinMaxMetric._is_suitable_val)rN)__name__ __module__ __qualname____doc__r rbool__annotations__rrrrrrstrr"r( staticmethodrr*rr# __classcell__rrrrrs" &r) typingrrrrrrtorchmetrics.metricrrrrrrs