o yi@s(ddlmZmZmZmZddlZddlmZddlmZ ddl m Z ddl m Z ddlmZddlmZd ed ed eeeffd d Z     dd ed edeedeedee ddeeded eeeeefffddZ    dd ed edeedeedee ddeed efddZdS))OptionalSequenceTupleUnionN)Tensor) functional)Literal)_gaussian_kernel_2d)_check_same_shape)reducepredstargetreturncCs`|j|jkrtd|jd|jdt||t|jdkr,td|jd|jd||fS)zUpdates and returns variables required to compute Universal Image Quality Index. 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 TypeErrorr lenshape ValueError)r r rU/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/functional/image/uqi.py _uqi_updates$  r r?relementwise_meanF kernel_sizesigma reduction)rsumnone data_rangereturn_contrast_sensitivitycCst|dks t|dkrtdt|dt|dtdd|Dr,td|dtdd|Dr=td |d|d urRt||||}|j}|d }|j} t|||| |} |d d d} |d d d} t j || | | | fd d}t j || | | | fd d}t ||||||||f} t j | | |d}||jd }|d d}|d d}|d |d }|d|}|d|}|d|}d|}||}d|||||}|d| | | | f}t||S)aComputes 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 data_range: Range of the image. If ``None``, it is determined from the image (max - min) 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: rcss$|] }|ddkp |dkVqdS)r%rNr).0xrrr Vs"z_uqi_compute..z8Expected `kernel_size` to have odd positive number. Got css|]}|dkVqdS)rNr)r&yrrrr(Ysz.Expected `sigma` to have positive number. Got Nrreflect)mode)groupsr.)rranymaxmindevicesizerr Fpadtorchcatconv2dsplitrpowr )r r rrr r#r$r2channelrkernelpad_hpad_w input_listoutputs output_list mu_pred_sq mu_target_sqmu_pred_target sigma_pred_sqsigma_target_sqsigma_pred_targetupperloweruqi_idxrrr _uqi_compute1sH"      rKcCs t||\}}t||||||S)a+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 data_range: Range of the image. If ``None``, it is determined from the image (max - min) 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 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 rrr r#rrruniversal_image_quality_index~s5rL)rrrNF)rrrN)typingrrrrr6rtorch.nnrr4typing_extensionsr$torchmetrics.functional.image.helperr torchmetrics.utilities.checksr "torchmetrics.utilities.distributedr rintfloatboolrKrLrrrrsd         P