o .wi@sddlmZddlmZmZmZmZddlmZm Z ddl m Z ddl m Z mZddlmZddlmZddlmZdd lmZdd lmZmZesMd gZGd d d eZdS))Sequence)AnyListOptionalUnion)Tensortensor)Literal) _sam_compute _sam_update)Metric)rank_zero_warn) dim_zero_cat)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPESpectralAngleMapper.plotcseZdZUdZdZeed<dZeed<dZeed<dZ e ed<d Z e ed <e e ed <e e ed <e ed <e ed< ddeeddeddffdd Zd e d e ddfddZde fddZ d deee ee fdeedefddZZS)!SpectralAngleMappera`Spectral Angle Mapper`_ determines the spectral similarity between image spectra and reference spectra. It works by calculating the angle between the spectra, where small angles between indicate high similarity and high angles indicate low similarity. 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 - ``sam`` (:class:`~torch.Tensor`): if ``reduction!='none'`` returns float scalar tensor with average SAM value over sample else returns tensor of shape ``(N,)`` with SAM values per sample Args: 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. Return: Tensor with SpectralAngleMapper score Example: >>> from torch import rand >>> from torchmetrics.image import SpectralAngleMapper >>> preds = rand([16, 3, 16, 16]) >>> target = rand([16, 3, 16, 16]) >>> sam = SpectralAngleMapper() >>> sam(preds, target) tensor(0.5914) Fhigher_is_betterTis_differentiablefull_state_updateplot_lower_boundg?plot_upper_boundpredstargetsum_samnumelelementwise_mean reduction)rsumnonekwargsreturnNc stjdi||dvrtd|d|dks|dur2td|jdgdd|jd gddn|jd td d d |jdtdd d ||_dS)N)rr r!NzThe `reduction` zI is not valid. Valid options are `elementwise_mean`, `sum`, `none`, None.r!zMetric `SpectralAngleMapper` will save all targets and predictions in the buffer when using`reduction=None` or `reduction='none'. For large datasets, this may lead to a large memory footprint.rcat)defaultdist_reduce_fxrrrr )r&rr)super__init__ ValueErrorr add_staterr)selfrr" __class__r'S/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/image/sam.pyr)Qs  zSpectralAngleMapper.__init__cCst||\}}|jdks|jdur|j||j|dSt||dd}|j|7_|j}|j|d|d|d7_dS)z*Update state with predictions and targets.r!Nr )rr) r rrappendrr rshaper)r,rr sam_scorep_shaper'r'r/updategs &zSpectralAngleMapper.updatecCsR|jdks |jdurt|j}t|j}t|||jS|jdkr&|j|jS|jS)zCompute spectra over state.r!Nr)rrrrr rr)r,rrr'r'r/computess   zSpectralAngleMapper.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 single value >>> from torch import rand >>> from torchmetrics.image import SpectralAngleMapper >>> preds = rand([16, 3, 16, 16]) >>> target = rand([16, 3, 16, 16]) >>> metric = SpectralAngleMapper() >>> metric.update(preds, target) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> from torch import rand >>> from torchmetrics.image import SpectralAngleMapper >>> preds = rand([16, 3, 16, 16]) >>> target = rand([16, 3, 16, 16]) >>> metric = SpectralAngleMapper() >>> values = [ ] >>> for _ in range(10): ... values.append(metric(preds, target)) >>> fig_, ax_ = metric.plot(values) )_plot)r,r8r9r'r'r/plot{s ,r)r)NN)__name__ __module__ __qualname____doc__rbool__annotations__rrrfloatrrrrr rr)r6r7rrrrr; __classcell__r'r'r-r/rs:  &         rN)collections.abcrtypingrrrrtorchrrtyping_extensionsr !torchmetrics.functional.image.samr r torchmetrics.metricr torchmetrics.utilitiesr torchmetrics.utilities.datartorchmetrics.utilities.importsrtorchmetrics.utilities.plotrr__doctest_skip__rr'r'r'r/s