a  d@sddlmZmZmZmZmZddlmZddlm Z ddl m Z m Z ddl mZddlmZddlmZddlmZdd lmZmZesd gZGd d d eZd S))AnyListOptionalSequenceUnion)Tensor)Literal)_ergas_compute _ergas_update)Metric)rank_zero_warn) dim_zero_cat)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPE.ErrorRelativeGlobalDimensionlessSynthesis.plotcseZdZUdZdZeed<dZeed<dZeed<dZ e ed<e e ed <e e ed <de ee fed eddfdd Ze e ddddZe dddZdee e ee feeedddZZS))ErrorRelativeGlobalDimensionlessSynthesisaCalculate `Relative dimensionless global error synthesis`_ (ERGAS). This metric is used to calculate the accuracy of Pan sharpened image considering normalized average error of each band of the result 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: >>> import torch >>> from torchmetrics.image import ErrorRelativeGlobalDimensionlessSynthesis >>> preds = torch.rand([16, 1, 16, 16], generator=torch.manual_seed(42)) >>> target = preds * 0.75 >>> ergas = ErrorRelativeGlobalDimensionlessSynthesis() >>> torch.round(ergas(preds, target)) tensor(154.) Fhigher_is_betterTis_differentiablefull_state_updategplot_lower_boundpredstargetelementwise_mean)rsumnoneNN)ratio reductionkwargsreturnc sJtjfi|td|jdgdd|jdgdd||_||_dS)NzMetric `UniversalImageQualityIndex` will save all targets and predictions in buffer. For large datasets this may lead to large memory footprint.rcat)defaultZdist_reduce_fxr)super__init__r Z add_staterr)selfrrr __class__a/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/image/ergas.pyr$Lsz2ErrorRelativeGlobalDimensionlessSynthesis.__init__)rrr cCs*t||\}}|j||j|dS)z*Update state with predictions and targets.N)r rappendrr%rrr(r(r)update^s z0ErrorRelativeGlobalDimensionlessSynthesis.update)r cCs&t|j}t|j}t|||j|jS)z&Compute explained variance over state.)r rrr rrr+r(r(r)computeds  z1ErrorRelativeGlobalDimensionlessSynthesis.compute)valaxr cCs |||S)a:Plot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> # Example plotting a single value >>> import torch >>> from torchmetrics.image import ErrorRelativeGlobalDimensionlessSynthesis >>> preds = torch.rand([16, 1, 16, 16], generator=torch.manual_seed(42)) >>> target = preds * 0.75 >>> metric = ErrorRelativeGlobalDimensionlessSynthesis() >>> metric.update(preds, target) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> import torch >>> from torchmetrics.image import ErrorRelativeGlobalDimensionlessSynthesis >>> preds = torch.rand([16, 1, 16, 16], generator=torch.manual_seed(42)) >>> target = preds * 0.75 >>> metric = ErrorRelativeGlobalDimensionlessSynthesis() >>> values = [ ] >>> for _ in range(10): ... values.append(metric(preds, target)) >>> fig_, ax_ = metric.plot(values) )Z_plot)r%r.r/r(r(r)plotjs,r)rr)NN)__name__ __module__ __qualname____doc__rbool__annotations__rrrfloatrrrintrrr$r,r-rrrrr0 __classcell__r(r(r&r)rs, $       rN)typingrrrrrZtorchrZtyping_extensionsrZ#torchmetrics.functional.image.ergasr r Ztorchmetrics.metricr Ztorchmetrics.utilitiesr Ztorchmetrics.utilities.datar Ztorchmetrics.utilities.importsrZtorchmetrics.utilities.plotrrZ__doctest_skip__rr(r(r(r)s