o .wiG @sddlZddlmZddlmZddlmZddlmZdededeeeffd d Z ddeded e deddef ddZ ddeded e deddef ddZ dS)N)Tensor)Literal)_check_same_shape)reducepredstargetreturncCs`|j|jkrtd|jd|jdt||t|jdkr,td|jd|jd||fS)uUpdate and returns variables required to compute Erreur Relative Globale Adimensionnelle de Synthèse. 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)rrr`/home/ubuntu/sommelier/.venv/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: >>> from torch import rand >>> preds = rand([16, 1, 16, 16]) >>> target = preds * 0.75 >>> preds, target = _ergas_update(preds, target) >>> torch.round(_ergas_compute(preds, target)) tensor(10.) )dimd)rreshapetorchrsqrtmeanr) rrrrbchwdiffsum_squared_error rmse_per_band mean_target ergas_scorerrr_ergas_compute,s( r)cCst||\}}t||||S)aCalculates `Error relative global dimensionless synthesis`_ (ERGAS) metric. 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 torch import rand >>> from torchmetrics.functional.image import error_relative_global_dimensionless_synthesis >>> preds = rand([16, 1, 16, 16]) >>> target = preds * 0.75 >>> error_relative_global_dimensionless_synthesis(preds, target) tensor(9.6193) )rr))rrrrrrr-error_relative_global_dimensionless_synthesisTs$r*)r r) rrtyping_extensionsrtorchmetrics.utilities.checksr"torchmetrics.utilities.distributedrtuplerfloatr)r*rrrrs@     +