:jQddlmZddlmZmZmZmZddlmZddl m Z ddl m Z ddl mZddlmZddlmZmZesd gZGd d e Zy ) )Sequence)AnyListOptionalUnion)Tensor)relative_average_spectral_error)Metric) dim_zero_cat)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPEz!RelativeAverageSpectralError.plotceZdZUdZdZeed<dZeed<dZeed<dZ e ed<e e ed <e e ed < dd e d eeefd dffd Zd e d e d dfdZd e fdZ ddeee ee fdeed efdZxZS)RelativeAverageSpectralErrora+Computes Relative Average Spectral Error (RASE) (RelativeAverageSpectralError_). As input to ``forward`` and ``update`` the metric accepts the following input - ``preds`` (:class:`~torch.Tensor`): Predictions from model of shape ``(N,C,H,W)`` - ``target`` (:class:`~torch.Tensor`): Ground truth values of shape ``(N,C,H,W)`` As output of `forward` and `compute` the metric returns the following output - ``rase`` (:class:`~torch.Tensor`): returns float scalar tensor with average RASE value over sample Args: window_size: Sliding window used for rmse calculation kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Return: Relative Average Spectral Error (RASE) Example: >>> from torch import rand >>> preds = rand(4, 3, 16, 16) >>> target = rand(4, 3, 16, 16) >>> rase = RelativeAverageSpectralError() >>> rase(preds, target) tensor(5326.40...) Raises: ValueError: If ``window_size`` is not a positive integer. Fhigher_is_betterTis_differentiablefull_state_updategplot_lower_boundpredstarget window_sizekwargsreturnNc t|di|t|trt|tr|dkrt d|||_|j dgd|j dgdy)NzEArgument `window_size` is expected to be a positive integer, but got rcat)defaultdist_reduce_fxr)super__init__ isinstanceint ValueErrorr add_state)selfrr __class__s l/home/mcse/projects/srt_converter/srt-converter-venv/lib/python3.12/site-packages/torchmetrics/image/rase.pyr!z%RelativeAverageSpectralError.__init__Fsr "6"+s+ ;0LQ\_`Q`depdqrs s& w5A xEBcp|jj||jj|y)z*Update state with predictions and targets.N)rappendrr&rrs r(updatez#RelativeAverageSpectralError.updateTs& %  6"r)ct|j}t|j}t|||jS)z/Compute Relative Average Spectral Error (RASE).)r rrr rr,s r(computez$RelativeAverageSpectralError.computeYs3TZZ(dkk*.ufd>N>NOOr)valaxc&|j||S)aXPlot 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.image import RelativeAverageSpectralError >>> metric = RelativeAverageSpectralError() >>> metric.update(torch.rand(4, 3, 16, 16), torch.rand(4, 3, 16, 16)) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> from torch import rand >>> from torchmetrics.image import RelativeAverageSpectralError >>> metric = RelativeAverageSpectralError() >>> values = [ ] >>> for _ in range(10): ... values.append(metric(rand(4, 3, 16, 16), rand(4, 3, 16, 16))) >>> fig_, ax_ = metric.plot(values) )_plot)r&r0r1s r(plotz!RelativeAverageSpectralError.plot_sPzz#r""r)))NN)__name__ __module__ __qualname____doc__rbool__annotations__rrrfloatrrr#dictstrrr!r-r/rrrr rr4 __classcell__)r's@r(rrs>#d""t"#t#!e! < L C CsCx. C  C#F#F#t# PP_c(#E&(6*:":;<(#IQRZI[(# (#r)rN)collections.abcrtypingrrrrtorchr"torchmetrics.functional.image.raser torchmetrics.metricr torchmetrics.utilities.datar torchmetrics.utilities.importsr torchmetrics.utilities.plotr r__doctest_skip__rrr)r(rIs<%--N&4@@;<i#6i#r)