iddlmZmZmZmZddlZddlmZddlm Z ddl m Z Gdde Z Gdd e Z Gd d e Zy) )AnyCallableOptionalUnionN)MetricCollection)Metric) WrapperMetriccreZdZdZdeeefdeee fddffd Z de jde jfdZ d e jde jfd Zd e jdee jd ffd Zd e jdeee fddfdZde fdZd e jdeee fde fdZdfd ZxZS)MetricInputTransformerzAbstract base class for metric input transformations. Input transformations are characterized by them applying a transformation to the input data of a metric, and then forwarding all calls to the wrapped metric with modifications applied. wrapped_metrickwargsreturnNc zt|di|t|ttfst d|||_y)NzsExpected wrapped metric to be an instance of `torchmetrics.Metric` or `torchmetrics.MetricsCollection`but received )super__init__ isinstancerr TypeErrorr )selfr r __class__s z/Volumes/fast/ai/experiments/voice-extract-mac/.venv/lib/python3.12/site-packages/torchmetrics/wrappers/transformations.pyrzMetricInputTransformer.__init__sL "6".63C*DE@@N?OQ -predc|S)zuDefine transform operations on the prediction data. Overridden by subclasses. Identity by default. r)rrs rtransform_predz%MetricInputTransformer.transform_pred(s  rtargetc|S)zqDefine transform operations on the target data. Overridden by subclasses. Identity by default. rrrs rtransform_targetz'MetricInputTransformer.transform_target0s  rargs.ct|dk(r|j|dfSt|dk(r(|j|d|j|dfS|j|d|j|dg|ddS)zWWrap transformation functions to dispatch args to their individual transform functions.rN)lenrr)rr s r_wrap_transformz&MetricInputTransformer._wrap_transform8s t9>''Q02 2 t9>&&tAw/1F1FtAw1OO O""47+T-B-B47-KVdSTSUhVVrcZ|j|}|jj|i|y)z.Wrap the update call of the underlying metric.N)r%r updaterr r s rr'zMetricInputTransformer.update@s/#t##T*"""D3F3rc6|jjS)z/Wrap the compute call of the underlying metric.)r compute)rs rr*zMetricInputTransformer.computeEs""**,,rcX|j|}|jj|i|S)z/Wrap the forward call of the underlying metric.)r%r forwardr(s rr,zMetricInputTransformer.forwardIs2#t##T**t""**D;F;;rcV|jjt|y)z-Wrap the reset call of the underlying metric.N)r resetr)rrs rr.zMetricInputTransformer.resetNs !!#  r)rN)__name__ __module__ __qualname____doc__rrrdictstrrrtorchTensorrrtupler%r'r*r,r. __classcell__rs@rr r s-uV5E-E'F-RVWZ\_W_R`-ei-5<<ELLu|| WU\\WeELL#>> import torch >>> from torchmetrics.classification import BinaryAccuracy >>> from torchmetrics.wrappers import LambdaInputTransformer >>> >>> preds = torch.tensor([0.9, 0.8, 0.7, 0.6, 0.5, 0.6, 0.7, 0.8, 0.5, 0.4]) >>> targets = torch.tensor([1,0,0,0,0,1,1,0,0,0]) >>> >>> metric = LambdaInputTransformer(BinaryAccuracy(), lambda preds: 1 - preds) >>> metric.update(preds, targets) >>> metric.compute() tensor(0.6000) Nr rrr rc t||fi||!t|std|d||_|"t|std|d||_yy)NzAExpected `transform_pred` to be of type `Callable` but received ``zCExpected `transform_target` to be of type `Callable` but received `)rrcallablerrr)rr rrr rs rrzLambdaInputTransformer.__init__ts 262  %N+"cdrcsst uvv"0D   ',-YZjYkklm%5D ! (r)NN) r/r0r1r2rrrr5r6rrr8r9s@rr;r;TsDLPMQ 55!5<<.%,,*F!GH5#8U\\NELL,H#IJ 5  5  55rr;c xeZdZdZd deeefdededdffd Z de jde jfd Z xZ S) BinaryTargetTransformeraWrapper class for computing a metric on binarized targets. Useful when the given ground-truth targets are continuous, but the metric requires binary targets. Args: wrapped_metric: The underlying `Metric` or `MetricCollection`. threshold: The binarization threshold for the targets. Targets values `t` are cast to binary with `t > threshold`. Raises: TypeError: If `threshold` is not an `int` or `float`. Example: >>> import torch >>> from torchmetrics.retrieval import RetrievalMRR >>> from torchmetrics.wrappers import BinaryTargetTransformer >>> >>> preds = torch.tensor([0.9, 0.8, 0.7, 0.6, 0.5, 0.6, 0.7, 0.8, 0.5, 0.4]) >>> targets = torch.tensor([1,0,0,0,0,2,1,0,0,0]) >>> topics = torch.tensor([0,0,0,0,0,1,1,1,1,1]) >>> >>> metric = BinaryTargetTransformer(RetrievalMRR()) >>> metric.update(preds, targets, indexes=topics) >>> metric.compute() tensor(0.7500) r thresholdr rNc ~t||fi|t|ttfst d|d||_y)NzBExpected `threshold` to be of type `int` or `float` but received `r=)rrrintfloatrrA)rr rAr rs rrz BinaryTargetTransformer.__init__sB 262)c5\2`aj`kklmn n"rrcj|j|jj|jS)zyCast the target tensor to binary values according to the threshold. Output assumes same type as input. )gtrAtodtypers rrz(BinaryTargetTransformer.transform_targets& yy(++FLL99r)r)r/r0r1r2rrrrDrrr5r6rr8r9s@rr@r@sR<#uV5E-E'F#SX#hk#pt# :u||: :rr@)typingrrrrr5torchmetrics.collectionsrtorchmetrics.metricrtorchmetrics.wrappers.abstractr r r;r@rrrrMsA21 5&8:]:z25325j+:4+:r