i ddlmZddlmZddlZddlmZmZddlmZddl m Z ddl m Z ddl mZd ed ed eeeffd Z dd ed ed eedeedeedd ef dZ dd ed ed eedeedeedd ef dZy))Sequence)OptionalN)Tensornn)Literal)_gaussian_kernel_2d)_check_same_shape)reducepredstargetreturnc|j|jk7r&td|jd|jdt||t|jdk7r&t d|jd|jd||fS)zUpdate and returns variables required to compute Universal Image Quality Index. 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 TypeErrorr lenshape ValueError)r r s v/Volumes/fast/ai/experiments/voice-extract-mac/.venv/lib/python3.12/site-packages/torchmetrics/functional/image/uqi.py _uqi_updaters {{fll"  ;;-}V\\N! E  eV$ 5;;1Nu{{m[hioiuiuhvvw x   &= kernel_sizesigma reduction)elementwise_meansumnonect|dk7st|dk7r$tdt|dt|dtd|Drtd|dtd|Drtd|d|j}|j d }|j }t |||||}|d d z dz} |d d z dz} tjj|| | | | fd }tjj|| | | | fd }tj||||z||z||zf} tjj| || } | j|jd } | d jd}| d jd}| d | d z}tj | d|z d}tj | d|z d}| d|z }d|z}||z}tj"|j j$}d|z|z||z|z|zz }|d| | | | f}t'||S)aCompute Universal Image Quality Index. Args: preds: estimated image target: ground truth image kernel_size: size of the gaussian kernel sigma: Standard deviation of the gaussian kernel 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]) >>> target = preds * 0.75 >>> preds, target = _uqi_update(preds, target) >>> _uqi_compute(preds, target) tensor(0.9216) zOExpected `kernel_size` and `sigma` to have the length of two. Got kernel_size: z and sigma: rc3:K|]}|dzdk(xs|dkyw)r rN).0xs r z_uqi_compute..Qs% 5A1q5A: a sz8Expected `kernel_size` to have odd positive number. Got c3&K|] }|dk yw)rNr")r#ys rr%z_uqi_compute..Ts !5a165sz.Expected `sigma` to have positive number. Got rreflect)mode)groupsg)minr.)rranydevicesizerrr functionalpadtorchcatconv2dsplitrpowclampfinfoepsr )r r rrrr/channelrkernelpad_hpad_w input_listoutputs output_list mu_pred_sq mu_target_sqmu_pred_target sigma_pred_sqsigma_target_sqsigma_pred_targetupperlowerr:uqi_idxs r _uqi_computerK/s8 ;1E a !!$[!1 2,s5zl! M   5 55ST_S``abcc !5 !!I%PQRSS \\FjjmG KKE +ueV LF ^a A %E ^a A %E MM  eeUE5%A  RE ]]  vueU'C)  TFE655=&6/5SY>Z[Jmm"":vg"FG-- A/KQ##A&Jq>%%a(L ^k!n4NKK A ;EMkk+a.<"?SIO#A7 ! !E O +E ++m)) * . .CN"e+l1Je0SVY0YZGc5%<v56G '9 %%rc>t||\}}t|||||S)aUniversal Image Quality Index. Args: preds: estimated image target: ground truth image kernel_size: size of the gaussian kernel sigma: Standard deviation of the gaussian kernel 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 UniversalImageQualityIndex score Raises: TypeError: If ``preds`` and ``target`` don't have the same data type. ValueError: If ``preds`` and ``target`` don't have ``BxCxHxW shape``. ValueError: If the length of ``kernel_size`` or ``sigma`` is not ``2``. ValueError: If one of the elements of ``kernel_size`` is not an ``odd positive number``. ValueError: If one of the elements of ``sigma`` is not a ``positive number``. Example: >>> from torchmetrics.functional.image import universal_image_quality_index >>> preds = torch.rand([16, 1, 16, 16]) >>> target = preds * 0.75 >>> universal_image_quality_index(preds, target) tensor(0.9216) References: [1] Zhou Wang and A. C. Bovik, "A universal image quality index," in IEEE Signal Processing Letters, vol. 9, no. 3, pp. 81-84, March 2002, doi: 10.1109/97.995823. [2] Zhou Wang, A. C. Bovik, H. R. Sheikh and E. P. Simoncelli, "Image quality assessment: from error visibility to structural similarity," in IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 600-612, April 2004, doi: 10.1109/TIP.2003.819861. )rrK)r r rrrs runiversal_image_quality_indexrMws(f v.ME6 v{E9 EEr)) rN)?rOr)collections.abcrtypingrr3rrtyping_extensionsr#torchmetrics.functional.image.utilsrtorchmetrics.utilities.checksr "torchmetrics.utilities.distributedr tuplerintfloatrKrMr"rrrYs% %C;5vv%2G0"*'FX E& E& E&#E& E? E&  ABC E&  E&V"*'FX 4F 4F 4F#4F E? 4F  ABC 4F  4Fr