o .wiv@sddlmZddlmZmZmZddlZddlmZddlm Z ddl m Z ddl m Z mZddlmZe s9d gZGd d d eZdS) )Sequence)AnyOptionalUnionN)Tensor)Metric)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPE) WrapperMetricMinMaxMetric.plotcseZdZUdZdZeeed<eed<eed<de de dd ffd d Z d e de dd fd dZ de eeffddZd e de de ffdd Zdfdd ZedeeefdefddZ ddeeeeefdeedefddZZS) MinMaxMetricakWrapper 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.wrappers 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_metrictorchtensorfloatrr)selfrr __class__rY/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/wrappers/minmax.pyrDs zMinMaxMetric.__init__argscOs|jj|i|dS)zUpdate the underlying metric.N)rupdaterr"rrrr!r#RzMinMaxMetric.updatecCs|j}||std|d|j|j|kr|n|j|j|_|j|j|kr0|n|j|j|_||j|jdS)zCompute 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. zLReturned value from base metric should be a float or scalar tensor, but got .)rawmaxmin)rcompute_is_suitable_val RuntimeErrorrtodevicer)rvalrrr!r*Vs  &&zMinMaxMetric.computecstt|j|i|S)z9Use the original forward method of the base metric class.)rr forwardr$rrr!r0dr%zMinMaxMetric.forwardcst|jdS)zXSet ``max_val`` and ``min_val`` to the initialization bounds and resets the base metric.N)rresetr)rrrr!r1hs zMinMaxMetric.resetr/cCs,t|ttfr dSt|tr|dkSdS)z(Check whether min/max is a scalar value.TF)rintrrnumel)r/rrr!r+ms   zMinMaxMetric._is_suitable_valaxcCs |||S)aPlot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> # Example plotting a single value >>> import torch >>> from torchmetrics.wrappers import MinMaxMetric >>> from torchmetrics.classification import BinaryAccuracy >>> metric = MinMaxMetric(BinaryAccuracy()) >>> metric.update(torch.randint(2, (20,)), torch.randint(2, (20,))) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> import torch >>> from torchmetrics.wrappers import MinMaxMetric >>> from torchmetrics.classification import BinaryAccuracy >>> metric = MinMaxMetric(BinaryAccuracy()) >>> values = [ ] >>> for _ in range(3): ... values.append(metric(torch.randint(2, (20,)), torch.randint(2, (20,)))) >>> fig_, ax_ = metric.plot(values) )_plot)rr/r5rrr!plotvs *r )rN)NN)__name__ __module__ __qualname____doc__rrbool__annotations__rrrrr#dictstrr*r0r1 staticmethodrrr+rr r r7 __classcell__rrrr!r s4 ! r )collections.abcrtypingrrrrrtorchmetrics.metricrtorchmetrics.utilities.importsrtorchmetrics.utilities.plotr r torchmetrics.wrappers.abstractr __doctest_skip__r rrrr!s