i! ZddlZddlmZmZddlZddlmZmZddlmZm Z ddl m Z ddl m Z ddlmZded ed ed ed eeeeff d Zdedeeeedffd efdZdeded efdZdeded efdZded eded eeeeffdZded ed ed ed ef dZ dded ed eed edee dd ef dZy)N)OptionalUnion)Tensortensor)conv2dpad)Literal)_check_same_shape)reducepredstarget hp_filter window_sizereturnc  |j|jk7r|j|j}t|||jdvr&t d|j d|j dt |j dk(r"|jd}|jd}|dkDst d|d||jd kDs||jdkDr3t d |d |jd d |jdd|jtj}|jtj}|d d d d fj|j|j}|||fS)aUpdate and returns variables required to compute Spatial Correlation Coefficient. Args: preds: Predicted tensor target: Ground truth tensor hp_filter: High-pass filter tensor window_size: Local window size integer Return: Tuple of (preds, target, hp_filter) tensors Raises: ValueError: If ``preds`` and ``target`` have different number of channels If ``preds`` and ``target`` have different shapes If ``preds`` and ``target`` have invalid shapes If ``window_size`` is not a positive integer If ``window_size`` is greater than the size of the image )zExpected `preds` and `target` to have batch of colored images with BxCxHxW shape or batch of grayscale images of BxHxW shape. Got preds: z and target: .rrz5Expected `window_size` to be a positive integer. Got z[Expected `window_size` to be less than or equal to the size of the image. Got window_size: z and image size: xN)dtypedevice) rtor ndim ValueErrorshapelen unsqueezesizetorchfloat32r)r r rrs v/Volumes/fast/ai/experiments/voice-extract-mac/.venv/lib/python3.12/site-packages/torchmetrics/functional/image/scc.py _scc_updater$sq* {{fll"5;;'eV$ zz  ;;-}V\\N! E   5;;1"!!!$ ?PQ\P]]^_``UZZ]"kEJJqM&A!!, ->uzz!}oQuzzZ[}o]^ `  HHU]] #E YYu}} %F$a-(++%++ell+SI &) ## input_imgr.c t|tr||||f}t|dk7rtdt||ddddddd|dfj dg}|dddddd|d dfj dg}t j |||gd}|ddddd|d ddfj d g}|dddd|d dddfj d g}t j |||gd S) zlApplies symmetric padding to the 2D image tensor input using ``reflect`` mode (d c b a | a b c d | d c b a).rz+Expected padding to have length 4, but got Nrr)dimsrdimr) isinstanceintrrflipr!cat)r&rleft_pad right_padpaddedtop_pad bottom_pads r#_symmetric_reflect_pad_2dr4Ls#sCc" 3x1}Fs3xjQRRAq!c!f*,-222 preds_mean target_mean preds_var target_vartarget_preds_covs r#_local_variance_covariancerKms 99fkk!nq0A56LJJ A 2a78M  m\=Q RE , |]S TFva;J1=Kuax1= A MI 6!Q?+q.PJfunfQJ[[eMee j"2 22r%cp|j}|j}tjdd||f|||dzz }t ||}t ||}t |||\} } } d| | dk<d| | dk<tj | tj | z} | dk(} d| | dk(<| | z }d|| <|S)a[Computes per channel Spatial Correlation Coefficient. Args: preds: estimated image of Bx1xHxW shape. target: ground truth image of Bx1xHxW shape. hp_filter: 2D high-pass filter. window_size: size of window for local mean calculation. Return: Tensor with Spatial Correlation Coefficient score r)r rrrr)rrr!onesrCrKsqrt)r r rrrrrDpreds_hp target_hprHrIrJdenidxsccs r#_scc_per_channel_computerTs KKE \\F ZZaK=USY Z^ikl^l mFy1H 3I.HS\^d.e+Iz+ Ii!m!"JzA~ **Z 5::i#8 8C (CCqM S CCH Jr% reduction)meannoneNc |tgdgdgdg}|d}|dvrtd|t||||\}}}t|j dDcgc]H}t |dd|ddddfj d|dd|ddddfj d||J}}|dk(r.tjtj|dgd S|d k(r"ttj|dd Sycc}w) aCompute Spatial Correlation Coefficient (SCC_). Args: preds: predicted images of shape ``(N,C,H,W)`` or ``(N,H,W)``. target: ground truth images of shape ``(N,C,H,W)`` or ``(N,H,W)``. hp_filter: High-pass filter tensor. default: tensor([[-1,-1,-1],[-1,8,-1],[-1,-1,-1]]) window_size: Local window size integer. default: 8, reduction: Reduction method for output tensor. If ``None`` or ``"none"``, returns a tensor with the per sample results. default: ``"mean"``. Return: Tensor with scc score Example: >>> from torch import randn >>> from torchmetrics.functional.image import spatial_correlation_coefficient as scc >>> x = randn(5, 3, 16, 16) >>> scc(x, x) tensor(1.) >>> x = randn(5, 16, 16) >>> scc(x, x) tensor(1.) >>> x = randn(5, 3, 16, 16) >>> y = randn(5, 3, 16, 16) >>> scc(x, y, reduction="none") tensor([0.0223, 0.0256, 0.0616, 0.0159, 0.0170]) N)rYrY)rYrYrW)rVrWz3Expected reduction to be 'mean' or 'none', but got rr))rrrrVelementwise_mean)rU) rrr$ranger rTrr!rVr.r )r r rrrUi per_channels r#spatial_correlation_coefficientr_sFL+|DE  ((NykZ[[*5&)[QE69 uzz!}% &A ! !Q1*  ' ' *F1aA:,>,H,H,KYXc &  Fzz%))KQ7YGGFeii 3?QRR sA D)NrZrV)r:typingrrr!rrtorch.nn.functionalrrtyping_extensionsr torchmetrics.utilities.checksr "torchmetrics.utilities.distributedr r,tupler$r4rArCrKrTr_r%r#rgsm " +%;5/$v/$v/$&/$s/$W\]cekms]sWt/$d ; ;eCsCx