o yi[@spddlmZmZddlmZddlmZddlmZm Z ddl m Z ddl m Z ddlmZGdd d e Zd S) )AnyList)Tensor)Literal) _sam_compute _sam_update)Metric)rank_zero_warn) dim_zero_catcseZdZUdZdZeed<dZeed<dZeed<e e ed<e e ed< dd e d d e d dffdd Z de de d dfddZd e fddZZS)SpectralAngleMapperaThe metric `Spectral Angle Mapper`_ determines the spectral similarity between image spectra and reference spectra 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: >>> import torch >>> from torchmetrics import SpectralAngleMapper >>> preds = torch.rand([16, 3, 16, 16], generator=torch.manual_seed(42)) >>> target = torch.rand([16, 3, 16, 16], generator=torch.manual_seed(123)) >>> sam = SpectralAngleMapper() >>> sam(preds, target) tensor(0.5943) Fhigher_is_betterTis_differentiablefull_state_updatepredstargetelementwise_mean reduction)rsumnonekwargsreturnNc sDtjdi|td|jdgdd|jdgdd||_dS)NzMetric `SpectralAngleMapper` will save all targets and predictions in the buffer. For large datasets, this may lead to a large memory footprint.rcat)defaultdist_reduce_fxr)super__init__r add_stater)selfrr __class__rJ/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/image/sam.pyrEs zSpectralAngleMapper.__init__cCs*t||\}}|j||j|dS)z*Update state with predictions and targets.N)rrappendrrrrrrr!updateTs zSpectralAngleMapper.updatecCs"t|j}t|j}t|||jS)zComputes spectra over state.)r rrrrr#rrr!computeZs  zSpectralAngleMapper.compute)r)__name__ __module__ __qualname____doc__r bool__annotations__r rrrrrrr$r% __classcell__rrrr!r s"  $    r N)typingrrtorchrtyping_extensionsr!torchmetrics.functional.image.samrrtorchmetrics.metricrtorchmetrics.utilitiesr torchmetrics.utilities.datar r rrrr!s