o .wÖinã @sžddlZddlZddlmZmZddededefdd„Zd ed ed ed edef d d„Zddedededeeeeffdd„Z ddedededefdd„Z dS)éN)ÚTensorÚtensoréÚxÚ block_sizeÚreturncCsb|j\}}}}|dkrtd|›dƒ‚t |d¡}t t|d|d|ƒ¡}t tt| ¡ƒ  | ¡¡ƒ¡}t |d¡} t t|d|d|ƒ¡} t tt|  ¡ƒ  |  ¡¡ƒ¡} |dd…dd…dd…|f|dd…dd…dd…|df  d¡  ¡} |dd…dd…dd…|f|dd…dd…dd…|df  d¡  ¡} | |dd…dd…| dd…f|dd…dd…| ddd…f  d¡  ¡7} | |dd…dd…| dd…f|dd…dd…| ddd…f  d¡  ¡7} |||d}||d|}|||d}||d|}| ||} | ||} | | kr)t   |¡t   t||ƒ¡nd}|| | S)zòCompute block effect. Args: x: input image block_size: integer indication the block size Returns: Computed block effect Raises: ValueError: If the image is not a grayscale image éz=`psnrb` metric expects grayscale images, but got images with z channels.Ng@r)ÚshapeÚ ValueErrorÚtorchÚarangerÚrangeÚlistÚsetÚtolistÚsymmetric_differenceÚpowÚsumÚmathÚlog2Úmin)rrÚ_ÚchannelsÚheightÚwidthÚhÚh_bÚh_bcÚvÚv_bÚv_bcÚd_bÚd_bcÚn_hbÚn_hbcÚn_vbÚn_vbcÚt©r(ú`/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/functional/image/psnrb.pyÚ _compute_befs4û  FFJJ  ( r*Úsum_squared_errorÚbefÚnum_obsÚ data_rangecCs<|||}|dkrdt |d|¡Sdt d|¡S)a/Computes peak signal-to-noise ratio. Args: sum_squared_error: Sum of square of errors over all observations bef: block effect num_obs: Number of predictions or observations data_range: the range of the data. If None, it is determined from the data (max - min). éé gð?)r Úlog10)r+r,r-r.r(r(r)Ú_psnrb_computeCs r2ÚpredsÚtargetcCs>t t ||d¡¡}t| ¡|jd}t||d}|||fS)zØUpdates and returns variables required to compute peak signal-to-noise ratio. Args: preds: Predicted tensor target: Ground truth tensor block_size: Integer indication the block size r/)Údevice©r)r rrrÚnumelr5r*)r3r4rr+r-r,r(r(r)Ú _psnrb_updateXs   r8cCs2| ¡| ¡}t|||d\}}}t||||ƒS)aComputes `Peak Signal to Noise Ratio With Blocked Effect` (PSNRB) metrics. .. math:: \text{PSNRB}(I, J) = 10 * \log_{10} \left(\frac{\max(I)^2}{\text{MSE}(I, J)-\text{B}(I, J)}\right) Where :math:`\text{MSE}` denotes the `mean-squared-error`_ function. Args: preds: estimated signal target: groun truth signal block_size: integer indication the block size Return: Tensor with PSNRB score Example: >>> from torch import rand >>> from torchmetrics.functional.image import peak_signal_noise_ratio_with_blocked_effect >>> preds = rand(1, 1, 28, 28) >>> target = rand(1, 1, 28, 28) >>> peak_signal_noise_ratio_with_blocked_effect(preds, target) tensor(7.8402) r6)Úmaxrr8r2)r3r4rr.r+r,r-r(r(r)Ú+peak_signal_noise_ratio_with_blocked_effectgsr:)r) rr rrÚintr*r2Útupler8r:r(r(r(r)Ús4 /ÿþýü û&ýÿþýü