o yi  @sddlmZddlZddlmZddlmZddlmZddlm Z deded eeeffd d Z ddeded edd efddZ ddeded edd efddZ dS))TupleN)Tensor)Literal)_check_same_shape)reducepredstargetreturncCs|j|jkrtd|jd|jdt||t|jdkr,td|jd|jd|jddks:|jddkrKtd|jdd|jdd||fS)zUpdates and returns variables required to compute Spectral Angle Mapper. Checks for same shape and type of the input tensors. 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)rrrU/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/functional/image/sam.py _sam_updates4  relementwise_mean reduction)rsumnoneNcCsL||jdd}|jdd}|jdd}t|||dd}t||S)aComputes 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: >>> preds = torch.rand([16, 3, 16, 16], generator=torch.manual_seed(42)) >>> target = torch.rand([16, 3, 16, 16], generator=torch.manual_seed(123)) >>> preds, target = _sam_update(preds, target) >>> _sam_compute(preds, target) tensor(0.5943) r )dim)rnormtorchclampacosr)rrr dot_product preds_norm target_norm sam_scorerrr _sam_compute4s    r#cCst||\}}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 torchmetrics.functional import spectral_angle_mapper >>> preds = torch.rand([16, 3, 16, 16], generator=torch.manual_seed(42)) >>> target = torch.rand([16, 3, 16, 16], generator=torch.manual_seed(123)) >>> spectral_angle_mapper(preds, target) tensor(0.5943) 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#)rrrrrrspectral_angle_mapperRs% r$)r) typingrrrtyping_extensionsrtorchmetrics.utilities.checksr"torchmetrics.utilities.distributedrrr#r$rrrrs6      !