:j`ddlmZddlmZmZmZmZddlmZddl m Z ddl m Z m Z ddlmZddlmZddlmZdd lmZdd lmZmZesd gZGd d eZy))Sequence)AnyListOptionalUnion)Tensor)Literal)_ergas_compute _ergas_update)Metric)rank_zero_warn) dim_zero_cat)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPEz.ErrorRelativeGlobalDimensionlessSynthesis.plotc eZdZUdZdZeed<dZeed<dZeed<dZ e ed<e e ed <e e ed < dd e d e d deddffd Zd e d e ddfdZde fdZ ddeee ee fdeedefdZxZS))ErrorRelativeGlobalDimensionlessSynthesisaCalculate the `Error relative global dimensionless synthesis`_ (ERGAS) metric. This metric is used to calculate the accuracy of Pan sharpened image considering normalized average error of each band of the result image. It is defined as: .. math:: ERGAS = \frac{100}{r} \cdot \sqrt{\frac{1}{N} \sum_{k=1}^{N} \frac{RMSE(B_k)^2}{\mu_k^2}} where :math:`r=h/l` denote the ratio in spatial resolution (pixel size) between the high and low resolution images. :math:`N` is the number of spectral bands, :math:`RMSE(B_k)` is the root mean square error of the k-th band between low and high resolution images, and :math:`\\mu_k` is the mean value of the k-th band of the reference image. As input to ``forward`` and ``update`` the metric accepts the following input - ``preds`` (:class:`~torch.Tensor`): Predictions from model - ``target`` (:class:`~torch.Tensor`): Ground truth values As output of `forward` and `compute` the metric returns the following output - ``ergas`` (:class:`~torch.Tensor`): if ``reduction!='none'`` returns float scalar tensor with average ERGAS value over sample else returns tensor of shape ``(N,)`` with ERGAS values per sample Args: ratio: ratio of high resolution to low resolution. reduction: a method to reduce metric score over labels. - ``'elementwise_mean'``: takes the mean (default) - ``'sum'``: takes the sum - ``'none'`` or ``None``: no reduction will be applied kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Example: >>> from torch import rand >>> from torchmetrics.image import ErrorRelativeGlobalDimensionlessSynthesis >>> preds = rand([16, 1, 16, 16]) >>> target = preds * 0.75 >>> ergas = ErrorRelativeGlobalDimensionlessSynthesis() >>> ergas(preds, target).round() tensor(10.) Fhigher_is_betterTis_differentiablefull_state_updategplot_lower_boundpredstargetratio reduction)elementwise_meansumnoneNkwargsreturnNc t|di|td|jdgd|jdgd||_||_y)NzMetric `UniversalImageQualityIndex` will save all targets and predictions in buffer. For large datasets this may lead to large memory footprint.rcat)defaultdist_reduce_fxr)super__init__r add_staterr)selfrrr __class__s m/home/mcse/projects/srt_converter/srt-converter-venv/lib/python3.12/site-packages/torchmetrics/image/ergas.pyr'z2ErrorRelativeGlobalDimensionlessSynthesis.__init__TsV "6" * w5A xEB "ct||\}}|jj||jj|y)z*Update state with predictions and targets.N)r rappendrr)rrs r+updatez0ErrorRelativeGlobalDimensionlessSynthesis.updatefs6%eV4 v %  6"r,ct|j}t|j}t|||j|j S)z&Compute explained variance over state.)rrrr rrr/s r+computez1ErrorRelativeGlobalDimensionlessSynthesis.computels7TZZ(dkk*eVTZZHHr,valaxc&|j||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 >>> from torch import rand >>> from torchmetrics.image import ErrorRelativeGlobalDimensionlessSynthesis >>> preds = rand([16, 1, 16, 16]) >>> target = preds * 0.75 >>> metric = ErrorRelativeGlobalDimensionlessSynthesis() >>> metric.update(preds, target) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> from torch import rand >>> from torchmetrics.image import ErrorRelativeGlobalDimensionlessSynthesis >>> preds = rand([16, 1, 16, 16]) >>> target = preds * 0.75 >>> metric = ErrorRelativeGlobalDimensionlessSynthesis() >>> values = [ ] >>> for _ in range(10): ... values.append(metric(preds, target)) >>> fig_, ax_ = metric.plot(values) )_plot)r)r3r4s r+plotz.ErrorRelativeGlobalDimensionlessSynthesis.plotrsXzz#r""r,)r)NN)__name__ __module__ __qualname____doc__rbool__annotations__rrrfloatrrr rr'r0r2rrrrrr7 __classcell__)r*s@r+rr s)V#d""t"#t#!e! < LFX##BC# #  #$#F#F#t# II_c,#E&(6*:":;<,#IQRZI[,# ,#r,rN)collections.abcrtypingrrrrtorchrtyping_extensionsr #torchmetrics.functional.image.ergasr r torchmetrics.metricr torchmetrics.utilitiesr torchmetrics.utilities.datartorchmetrics.utilities.importsrtorchmetrics.utilities.plotrr__doctest_skip__rr%r,r+rLsB%--%M&14@@HI~#~#r,