:jddlZddlmZddlmZmZddlmZdedededed ed ed eeeeffd Z ded ed eded ef d Z ddededed efdZ y)N)Tensor)_rmse_sw_compute_rmse_sw_update)_uniform_filterpredstarget window_sizermse_map target_sum total_imagesreturnct|||d||\}}}|tjt|||dzz dz }|||fS)aCalculate the sum of RMSE map values for the batch of examples and update intermediate states. Args: preds: Deformed image target: Ground truth image window_size: Sliding window used for RMSE calculation rmse_map: Sum of RMSE map values over all examples target_sum: target... total_images: Total number of images Return: Intermediate state of RMSE map Updated total number of already processed images N rmse_val_sumr r r)dim)rtorchsumr)rrr r r r _s w/home/mcse/projects/srt_converter/srt-converter-venv/lib/python3.12/site-packages/torchmetrics/functional/image/rase.py _rase_updatersY$!0 v{Wc!Ax%))OFK@KQRNSYZ[[J Z --ctd||\}}||z }|jd}d|z tjtj|dzdz}t |dz }tj||| || fS)aCompute RASE. Args: rmse_map: Sum of RMSE map values over all examples target_sum: target... total_images: Total number of images. window_size: Sliding window used for rmse calculation Return: Relative Average Spectral Error (RASE) Nrrdr)rmeanrsqrtround)r r r r r target_meanrase_map crop_slides r _rase_computer!0s#xVbcKAx|+K""1%K[ 5::ejj1a.H#IIH{Q'J ::hz:+5z:+7MMN OOrct|trt|tr|dkr td|jdd}t j ||j |j}t j ||j |j}t jd|j}t||||||\}}}t||||S)aCompute Relative Average Spectral Error (RASE) (RelativeAverageSpectralError_). Args: preds: Deformed image target: Ground truth image window_size: Sliding window used for rmse calculation Return: Relative Average Spectral Error (RASE) Example: >>> from torch import rand >>> from torchmetrics.functional.image import relative_average_spectral_error >>> preds = rand(4, 3, 16, 16) >>> target = rand(4, 3, 16, 16) >>> relative_average_spectral_error(preds, target) tensor(5326.40...) Raises: ValueError: If ``window_size`` is not a positive integer. zr3s  S?. .!.03.?E.SY.io. 666 !".2PFPPfP[^PciP, J6 J6 JPS J\b Jr