o yi @sddlmZmZddlZddlmZddlmZddlmZddl m Z deded eeeffd d Z ddededee e fdedd ef ddZ ddededee e fdedd ef ddZdS))TupleUnionN)Tensor)Literal)_check_same_shape)reducepredstargetreturncCs`|j|jkrtd|jd|jdt||t|jdkr,td|jd|jd||fS)uUpdates and returns variables required to compute Erreur Relative Globale Adimensionnelle de Synthèse. 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: )dtype TypeErrorrlenshape ValueError)rr rW/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/functional/image/ergas.py _ergas_updates$  rr elementwise_meanratio reduction)rsumnoneNc Cs|j\}}}}|||||}|||||}||}tj||dd} t| ||} tj|dd} d|ttj| | ddd|} t| |S)uErreur Relative Globale Adimensionnelle de Synthèse. Args: preds: estimated image target: ground truth image ratio: ratio of high resolution to low resolution 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, 1, 16, 16], generator=torch.manual_seed(42)) >>> target = preds * 0.75 >>> preds, target = _ergas_update(preds, target) >>> torch.round(_ergas_compute(preds, target)) tensor(154.) )dimd)rreshapetorchrsqrtmeanr) rr rrbchwdiffsum_squared_error rmse_per_band mean_target ergas_scorerrr_ergas_compute/s( r+cCst||\}}t||||S)uErreur Relative Globale Adimensionnelle de Synthèse. Args: preds: estimated image target: ground truth image ratio: ratio of high resolution to low resolution 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 RelativeG 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 error_relative_global_dimensionless_synthesis >>> preds = torch.rand([16, 1, 16, 16], generator=torch.manual_seed(42)) >>> target = preds * 0.75 >>> ergds = error_relative_global_dimensionless_synthesis(preds, target) >>> torch.round(ergds) tensor(154.) References: [1] Qian Du; Nicholas H. Younan; Roger King; Vijay P. Shah, "On the Performance Evaluation of Pan-Sharpening Techniques" in IEEE Geoscience and Remote Sensing Letters, vol. 4, no. 4, pp. 518-522, 15 October 2007, doi: 10.1109/LGRS.2007.896328. )rr+)rr rrrrr-error_relative_global_dimensionless_synthesisUs(r,)r r)typingrrrrtyping_extensionsrtorchmetrics.utilities.checksr"torchmetrics.utilities.distributedrrintfloatr+r,rrrrsB       )