:jG ddlZddlmZddlmZddlmZddlmZdededeeeffd Z ddeded e d ed def d Z ddeded e d ed def dZ y)N)Tensor)Literal)_check_same_shape)reducepredstargetreturnc|j|jk7r&td|jd|jdt||t|jdk7r&t d|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)rrs x/home/mcse/projects/srt_converter/srt-converter-venv/lib/python3.12/site-packages/torchmetrics/functional/image/ergas.py _ergas_updaters {{fll"  ;;-}V\\N! E  eV$ 5;;1Nu{{m[hioiuiuhvvw x   &=ratio reduction)elementwise_meansumnoneNc|j\}}}}|j||||z}|j||||z}||z }tj||zd} tj| ||zz } tj |d} d|z tjtj| | z dzd|z z} 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_scores r_ergas_computer,,s6JAq!Q MM!QA &E ^^Aq!a% (F 6>D $+15JJ0AE:;M**V+K+ 599mk6QVW5W]^+_bc+c ddK +y ))rc<t||\}}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,)rrrrs r-error_relative_global_dimensionless_synthesisr.Ts&H"%0ME6 % ::r)r r) r rtyping_extensionsrtorchmetrics.utilities.checksr"torchmetrics.utilities.distributedrtuplerfloatr,r.rrr5s %;5E&&.4I0BT %* %* %* %*>? %*  %*VBT %; %; %; %;>? %;  %;r