o .wiv@sddlmZddlmZmZmZmZddlmZddl m Z ddl m Z m Z ddlmZddlmZddlmZdd lmZdd lmZmZesKd gZGd d d eZdS))Sequence)AnyListOptionalUnion)Tensor)Literal)"_spectral_distortion_index_compute!_spectral_distortion_index_update)Metric)rank_zero_warn) dim_zero_cat)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPESpectralDistortionIndex.plotc seZdZUdZdZeed<dZeed<dZeed<dZ e ed<d Z e ed <e e ed <e e ed < ddededdeddffdd Zd e d e ddfddZde fddZ d deee ee fdeedefddZZS)!SpectralDistortionIndexagCompute Spectral Distortion Index (SpectralDistortionIndex_) also now as D_lambda. The metric is used to compare the spectral distortion between two images. As input to ``forward`` and ``update`` the metric accepts the following input - ``preds`` (:class:`~torch.Tensor`): Low resolution multispectral image of shape ``(N,C,H,W)`` - ``target``(:class:`~torch.Tensor`): High resolution fused image of shape ``(N,C,H,W)`` As output of `forward` and `compute` the metric returns the following output - ``sdi`` (:class:`~torch.Tensor`): if ``reduction!='none'`` returns float scalar tensor with average SDI value over sample else returns tensor of shape ``(N,)`` with SDI values per sample Args: p: Large spectral differences reduction: a method to reduce metric score over labels. - ``'elementwise_mean'``: takes the mean (default) - ``'sum'``: takes the sum - ``'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 SpectralDistortionIndex >>> preds = rand([16, 3, 16, 16]) >>> target = rand([16, 3, 16, 16]) >>> sdi = SpectralDistortionIndex() >>> sdi(preds, target) tensor(0.0234) Thigher_is_betteris_differentiableFfull_state_updategplot_lower_boundg?plot_upper_boundpredstargetelementwise_meanp reductionrsumnonekwargsreturnNc stjd i|tdt|tr|dkrtd|d||_d}||vr1td|d|||_|jdgd d |jd gd d dS) NzMetric `SpectralDistortionIndex` will save all targets and predictions in buffer. For large datasets this may lead to large memory footprint.rz.Expected `p` to be a positive integer. Got p: .rz(Expected argument `reduction` be one of z but got rcat)defaultdist_reduce_fxr) super__init__r isinstanceint ValueErrorrr add_state)selfrrr!allowed_reductions __class__r'X/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/image/d_lambda.pyr)Lsz SpectralDistortionIndex.__init__cCs*t||\}}|j||j|dS)z#Update state with preds and target.N)r rappendrr.rrr'r'r2update`s zSpectralDistortionIndex.updatecCs&t|j}t|j}t|||j|jS)z.Compute and returns spectral distortion index.)r rrr rrr4r'r'r2computefs  zSpectralDistortionIndex.computevalaxcCs |||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 SpectralDistortionIndex >>> preds = rand([16, 3, 16, 16]) >>> target = rand([16, 3, 16, 16]) >>> metric = SpectralDistortionIndex() >>> metric.update(preds, target) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> from torch import rand >>> from torchmetrics.image import SpectralDistortionIndex >>> preds = rand([16, 3, 16, 16]) >>> target = rand([16, 3, 16, 16]) >>> metric = SpectralDistortionIndex() >>> values = [ ] >>> for _ in range(10): ... values.append(metric(preds, target)) >>> fig_, ax_ = metric.plot(values) )_plot)r.r7r8r'r'r2plotls ,r)rr)NN)__name__ __module__ __qualname____doc__rbool__annotations__rrrfloatrrrr+rrr)r5r6rrrrrr: __classcell__r'r'r0r2rs:  #      rN)collections.abcrtypingrrrrtorchrtyping_extensionsr&torchmetrics.functional.image.d_lambdar r torchmetrics.metricr torchmetrics.utilitiesr torchmetrics.utilities.datar torchmetrics.utilities.importsrtorchmetrics.utilities.plotrr__doctest_skip__rr'r'r'r2s