a  d @sddlmZmZmZddlZddlmZddlmZddlm Z eee eeeeeeeeeefdddZ eeeeeeeefd d d Z deee e eeeeeeeffdddZdS))OptionalTupleUnionN)Tensor)_uniform_filter)_check_same_shape)predstarget window_size rmse_val_sumrmse_map total_imagesreturnc Cs|j|jkr&td|jd|jdt||t|jdkrPtd|jdt|d|jdks|t|d|jdkrtdt|jd|jdd t|dd|d ur||jd 7}ntj |jd |j d }||d}t ||}t |}t|d}|d urH|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)Zdtype TypeErrorrlenshape ValueErrorroundmintorchZtensorrrsqrtsummean) rr r r r r errorZ _rmse_mapZ crop_slideZrmse_valrn/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/functional/image/rmse_sw.py_rmse_sw_updates8  ,      $.  r!)r r r rcCs,|dur||nd}|dur$||}||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 rmserrr _rmse_sw_computeZsr#F)rr r return_rmse_maprcCs^t|trt|tr$|dkr$tdt|||dddd\}}}t|||\}}|rZ||fS|S)aCompute 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 torchmetrics.functional.image import root_mean_squared_error_using_sliding_window >>> g = torch.manual_seed(22) >>> preds = torch.rand(4, 3, 16, 16) >>> target = torch.rand(4, 3, 16, 16) >>> root_mean_squared_error_using_sliding_window(preds, target) tensor(0.3999) Raises: ValueError: If ``window_size`` is not a positive integer. zs(    C