i8!ddlmZddlmZmZmZddlmZddlm Z ddl m Z ddl m Z ddlmZddlmZdd lmZdd lmZmZdd lmZdd lmZdd lmZGdde ZGddeZGdde ZGddeZ GddeZ!GddeZ"Gdde Z#GddeZ$GddeZ%Gd d!eZ&y")#)Sequence)AnyOptionalUnion)Literal)SpectralDistortionIndex))ErrorRelativeGlobalDimensionlessSynthesis)PeakSignalNoiseRatio)RelativeAverageSpectralError)&RootMeanSquaredErrorUsingSlidingWindow)SpectralAngleMapper)*MultiScaleStructuralSimilarityIndexMeasure StructuralSimilarityIndexMeasure)TotalVariation)UniversalImageQualityIndex)_deprecated_root_import_classc @eZdZdZ d dededdeddffd ZxZS) *_ErrorRelativeGlobalDimensionlessSynthesiszWrapper for deprecated import. >>> from torch import rand >>> preds = rand([16, 1, 16, 16]) >>> target = preds * 0.75 >>> ergas = _ErrorRelativeGlobalDimensionlessSynthesis() >>> ergas(preds, target).round() tensor(10.) ratio reductionelementwise_meansumnoneNkwargsreturnNc Btddt|d||d|y)Nr image)rrrsuper__init__)selfrrr __class__s s/Volumes/fast/ai/experiments/voice-extract-mac/.venv/lib/python3.12/site-packages/torchmetrics/image/_deprecated.pyr"z3_ErrorRelativeGlobalDimensionlessSynthesis.__init__s( &&QSZ[ Du DVD)r) __name__ __module__ __qualname____doc__floatrrr" __classcell__r$s@r%rrsL FXEEBCE E  EEr&rceZdZdZ ddedeeeefdeeeefde dde eee eeffd ed ed e ed fd e dde ddffd Z xZS)+_MultiScaleStructuralSimilarityIndexMeasurea Wrapper for deprecated import. >>> from torch import rand >>> preds = rand([3, 3, 256, 256]) >>> target = preds * 0.75 >>> ms_ssim = _MultiScaleStructuralSimilarityIndexMeasure(data_range=1.0) >>> ms_ssim(preds, target) tensor(0.9628) Ngaussian_kernel kernel_sizesigmarr data_rangek1k2betas. normalize)relusimpleNrrc Ptddt |d||||||||| d | y)Nrr) r1r2r3rr4r5r6r7r8rr ) r#r1r2r3rr4r5r6r7r8rr$s r%r"z4_MultiScaleStructuralSimilarityIndexMeasure.__init__4sH &&RT[\  +#!  r&) T ?rN{Gz?Q?)gǺ?g48EG?ga4?g??g9EGr?r9)r(r)r*r+boolrintrr,rrtuplerr"r-r.s@r%r0r0(s !%13/2FXBF#K5;  3 -. UHUO+,  BC  U5%u *=#=>?      UCZ  12     r&r0czeZdZdZ d deeeeeffdededdeee ee dffd e d df fd Z xZ S) _PeakSignalNoiseRatiozWrapper for deprecated import. >>> from torch import tensor >>> psnr = _PeakSignalNoiseRatio() >>> preds = tensor([[0.0, 1.0], [2.0, 3.0]]) >>> target = tensor([[3.0, 2.0], [1.0, 0.0]]) >>> psnr(preds, target) tensor(2.5527) Nr4baserrdim.rrc Ftddt|d||||d|y)Nr r)r4rErrFrr )r#r4rErrFrr$s r%r"z_PeakSignalNoiseRatio.__init__\s- &&eZdZdZ ddedeeefddffd ZxZ S)_RelativeAverageSpectralErrorzWrapper for deprecated import. >>> from torch import rand >>> preds = rand(4, 3, 16, 16) >>> target = rand(4, 3, 16, 16) >>> rase = _RelativeAverageSpectralError() >>> rase(preds, target) tensor(5326.40...) window_sizerrNc @tddt|dd|i|y)Nr rrJrr r#rJrr$s r%r"z&_RelativeAverageSpectralError.__init__ts% &&DgN ;[;F;r& r(r)r*r+rAdictstrrr"r-r.s@r%rIrIh; <<sCx.<  <eZdZdZ ddedeeefddffd ZxZ S)'_RootMeanSquaredErrorUsingSlidingWindowzWrapper for deprecated import. >>> from torch import rand >>> preds = rand(4, 3, 16, 16) >>> target = rand(4, 3, 16, 16) >>> rmse_sw = RootMeanSquaredErrorUsingSlidingWindow() >>> rmse_sw(preds, target) tensor(0.4158) rJrrNc @tddt|dd|i|y)Nr rrJrr rLs r%r"z0_RootMeanSquaredErrorUsingSlidingWindow.__init__s& &&NPWX ;[;F;r&rMrOr.s@r%rTrT}rRr&rTc:eZdZdZ ddeddeddffd ZxZS) _SpectralAngleMapperzWrapper for deprecated import. >>> from torch import rand >>> preds = rand([16, 3, 16, 16]) >>> target = rand([16, 3, 16, 16]) >>> sam = _SpectralAngleMapper() >>> sam(preds, target) tensor(0.5914) rrrrrrNc @tddt|dd|i|y)Nr rrrr r#rrr$s r%r"z_SpectralAngleMapper.__init__s% &&;WE 7977r&)rr(r)r*r+rrr"r-r.s@r%rWrWs; AS8<=88  88r&rWc >eZdZdZ d dededdeddffd ZxZS) _SpectralDistortionIndexzWrapper for deprecated import. >>> from torch import rand >>> preds = rand([16, 3, 16, 16]) >>> target = rand([16, 3, 16, 16]) >>> sdi = _SpectralDistortionIndex() >>> sdi(preds, target) tensor(0.0234) prrXrrNc Btddt|d||d|y)Nrr)r^rrr )r#r^rrr$s r%r"z!_SpectralDistortionIndex.__init__s' &&?I <1 >> import torch >>> preds = torch.rand([3, 3, 256, 256]) >>> target = preds * 0.75 >>> ssim = _StructuralSimilarityIndexMeasure(data_range=1.0) >>> ssim(preds, target) tensor(0.9219) Nr1r3r2rrr4r5r6return_full_imagereturn_contrast_sensitivityrrc Ptddt |d||||||||| d | y)Nrr) r1r3r2rr4r5r6rcrdrr ) r#r1r3r2rr4r5r6rcrdrr$s r%r"z*_StructuralSimilarityIndexMeasure.__init__sG &&H'R  +#!/(C  r&) Tr=r<rNr>r?FF)r(r)r*r+r@rr,rrArrrBrr"r-r.s@r%rbrbs !%/213FXBF"',1  UHUO+, 3 -.  BC  U5%u *=#=>?        &*     r&rbc8eZdZdZddeddeddffd ZxZS) _TotalVariationzWrapper for deprecated import. >>> from torch import rand >>> tv = _TotalVariation() >>> img = rand(5, 3, 28, 28) >>> tv(img) tensor(7546.8018) r)meanrrNrrNc @tddt|dd|i|y)Nrrrrr rZs r%r"z_TotalVariation.__init__s#%&6@ 7977r&)rr[r.s@r%rgrgs/8'*E"F8Z]8bf88r&rgc ReZdZdZ d deedeededdeddf fd Z xZ S) _UniversalImageQualityIndexzWrapper for deprecated import. >>> import torch >>> preds = torch.rand([16, 1, 16, 16]) >>> target = preds * 0.75 >>> uqi = _UniversalImageQualityIndex() >>> uqi(preds, target) tensor(0.9216) r2r3rrrrNc Dtddt|d|||d|y)Nrr)r2r3rrr )r#r2r3rrr$s r%r"z$_UniversalImageQualityIndex.__init__s* &&BGL ][]V\]r&))r<r<)r=r=r) r(r)r*r+rrAr,rrr"r-r.s@r%rkrksb &.!+FX ^c]^^BC ^  ^  ^^r&rkN)'collections.abcrtypingrrrtyping_extensionsrtorchmetrics.image.d_lambdartorchmetrics.image.ergasr torchmetrics.image.psnrr torchmetrics.image.raser torchmetrics.image.rmse_swr torchmetrics.image.samr torchmetrics.image.ssimrrtorchmetrics.image.tvrtorchmetrics.image.uqirtorchmetrics.utilities.printsrrr0rDrIrTrWr]rbrgrkrr&r%rzs$''%?N8@M6p0=GE1ZE,% 2\% Pc0c0<$@<*<.T<*8.8*=6=&% (H% P 8n 8 ^"<^r&