:jf ddlZddlmZddlmZddlmZddlmZdededeeeffd Z ddeded ed defd Z ddeded ed defd Z y)N)Tensor)Literal)_check_same_shape)reducepredstargetreturnc|j|jk7r&td|jd|jdt||t|jdk7r&t d|jd|jd|jddks|jddkr,t d|jdd|jdd||fS)zUpdate and returns variables required to compute Spectral Angle Mapper. Args: preds: Predicted tensor target: Ground truth tensor zEExpected `preds` and `target` to have the same data type. Got preds: z and target: .z@Expected `preds` and `target` to have BxCxHxW shape. Got preds: zSExpected channel dimension of `preds` and `target` to be larger than 1. Got preds: )dtype TypeErrorrlenshape ValueError)rrs v/home/mcse/projects/srt_converter/srt-converter-venv/lib/python3.12/site-packages/torchmetrics/functional/image/sam.py _sam_updaters {{fll"  ;;-}V\\N! E  eV$ 5;;1Nu{{m[hioiuiuhvvw x    A!aA!5  ;;q>*- Q7H K   &= reduction)elementwise_meansumnoneNc||zjd}|jd}|jd}tj|||zz ddj }t ||S)aWCompute Spectral Angle Mapper. Args: preds: estimated image target: ground truth image 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 Example: >>> from torch import rand >>> preds = rand([16, 3, 16, 16]) >>> target = rand([16, 3, 16, 16]) >>> preds, target = _sam_update(preds, target) >>> _sam_compute(preds, target) tensor(0.5914) r )dim)rnormtorchclampacosr)rrr dot_product preds_norm target_norm sam_scores r _sam_computer%1sm26>&&1&-K"J++!+$K K: +CDb!LQQSI )Y ''rc:t||\}}t|||S)aUniversal Spectral Angle Mapper. Args: preds: estimated image target: ground truth image reduction: a method to reduce metric score over labels. - ``'elementwise_mean'``: takes the mean (default) - ``'sum'``: takes the sum - ``'none'`` or ``None``: no reduction will be applied Return: Tensor with Spectral Angle Mapper score Raises: TypeError: If ``preds`` and ``target`` don't have the same data type. ValueError: If ``preds`` and ``target`` don't have ``BxCxHxW shape``. Example: >>> from torch import rand >>> from torchmetrics.functional.image import spectral_angle_mapper >>> preds = rand([16, 3, 16, 16],) >>> target = rand([16, 3, 16, 16]) >>> spectral_angle_mapper(preds, target) tensor(0.5914) References: [1] Roberta H. Yuhas, Alexander F. H. Goetz and Joe W. Boardman, "Discrimination among semi-arid landscape endmembers using the Spectral Angle Mapper (SAM) algorithm" in PL, Summaries of the Third Annual JPL Airborne Geoscience Workshop, vol. 1, June 1, 1992. )rr%)rrrs rspectral_angle_mapperr'Qs$N v.ME6 vy 11r)r) rrtyping_extensionsrtorchmetrics.utilities.checksr"torchmetrics.utilities.distributedrtuplerr%r'rrr-s %;5vv%2G:CU( ( (>?( (FCU(2 (2 (2>?(2 (2r