o .wi@sddlmZmZddlZddlmZddlmZddlmZdedede d eed eed eed e eeeffd dZ d eed ed ed e eeeffddZ ddedede de d eeee eeefff ddZdS))OptionalUnionN)Tensor)_uniform_filter)_check_same_shapepredstarget window_size rmse_val_sumrmse_map total_imagesreturnc Cs|j|jkrtd|jd|jdt||t|jdkr(td|jdt|d|jdks>t|d|jdkrVtdt|jd|jdd t|dd|d urb||jd 7}n tj |jd |j d }||d}t ||}t |}t|d}|d ur|d d d d || || f} || d 7}n|d d d d || || f d }|d ur|| d 7}n| d }|||fS) aCalculate the sum of RMSE values and RMSE map 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_val_sum: Sum of RMSE over all examples per individual channels rmse_map: Sum of RMSE map values over all examples total_images: Total number of images Return: (Optionally) Intermediate state of RMSE (using sliding window) over the accumulated examples. (Optionally) Intermediate state of RMSE map Updated total number of already processed images Raises: ValueError: If ``preds`` and ``target`` do not have the same data type. ValueError: If ``preds`` and ``target`` do not have ``BxCxWxH`` shape. ValueError: If ``round(window_size / 2)`` is greater or equal to width or height of the image. zBExpected `preds` and `target` to have the same data type. But got z and .z=Expected `preds` and `target` to have BxCxHxW shape. But got zBParameter `round(window_size / 2)` is expected to be smaller than z but got Nr)device)dtype TypeErrorrlenshape ValueErrorroundmintorchtensorrrsqrtsummean) rrr r r r error _rmse_map crop_slidermse_valr#b/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/functional/image/rmse_sw.py_rmse_sw_updates8  ,     $.  r%cCs,|dur||nd}|dur||}||fS)a\Compute RMSE from the aggregated RMSE value. Optionally also computes the mean value for RMSE map. Args: rmse_val_sum: Sum of RMSE over all examples rmse_map: Sum of RMSE map values over all examples total_images: Total number of images Return: RMSE using sliding window (Optionally) RMSE map Nr#)r r r rmser#r#r$_rmse_sw_computeZsr'Freturn_rmse_mapcCs^t|trt|tr|dkrtdt|||dddd\}}}t|||\}}|r-||fS|S)a Compute Root Mean Squared Error (RMSE) using sliding window. Args: preds: Deformed image target: Ground truth image window_size: Sliding window used for rmse calculation return_rmse_map: An indication whether the full rmse reduced image should be returned. Return: RMSE using sliding window (Optionally) RMSE map Example: >>> from torch import rand >>> from torchmetrics.functional.image import root_mean_squared_error_using_sliding_window >>> preds = rand(4, 3, 16, 16) >>> target = rand(4, 3, 16, 16) >>> root_mean_squared_error_using_sliding_window(preds, target) tensor(0.4158) Raises: ValueError: If ``window_size`` is not a positive integer. zsR     B