:jD ddlZddlZddlmZmZmZmZddlZddlmZm Z ddl m Z ddl m Z dddd Ze sd d gZd7d ed ede j$j&j(fdZGddejj,ZGddejj,ZGddejj,Zd8dededefdZd9dedeedfdefdZd:dededefdZd;ded edefd!Z Gd"d#e j,Z!Gd$d%e j,Z"Gd&d'e j,Z#Gd(d)e#Z$d*ed+edefd,Z%d-ed.ed e j,d+edef d/Z&dN)List NamedTupleOptionalUnion)Tensornn)Literal)_TORCHVISION_AVAILABLESqueezeNet1_1_WeightsAlexNet_Weights VGG16_Weights) squeezenet1_1alexnetvgg16)learned_perceptual_image_patch_similarity_get_tv_model_featuresnet pretrainedreturnc"ts tdddl}|rPt|jt |}t|j||j }|jSt|j|d}|jS)aAGet torchvision network. Args: net: Name of network pretrained: If pretrained weights should be used >>> _ = _get_tv_model_features("alexnet", pretrained=True) >>> _ = _get_tv_model_features("squeezenet1_1", pretrained=True) >>> _ = _get_tv_model_features("vgg16", pretrained=True) zSTorchvision is not installed. 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GA KK " "3q6+B1+E F Gq" GA KK " "3q6+B1+E F Gr2 GA KK " "3q6+B1+E F Gr2 GA KK " "3q6+B1+E F G* ,&+# ,r"rCc|j|}|}|j|}|}|j|}|}|j|}|}|j |}|}Gddt }||||||S)rEc@eZdZUeed<eed<eed<eed<eed<y)"Vgg16.forward.._VGGOutputsrelu1_2relu2_2relu3_3relu4_3relu5_3NrOrTr"r! _VGGOutputsrsOOOOOr"rrm) r;rCrn h_relu1_2 h_relu2_2 h_relu3_3 h_relu4_3 h_relu5_3rs r!rXz Vgg16.forwards} KKN KKN KKN KKN KKN  * 9iIyQQr"rYrZr^s@r!rurus7,d,,PT,0RRJRr"ruin_tenskeep_dimc,|jddg|S)z2Spatial averaging over height and width of images.r()keepdimmean)rrs r!_spatial_averagers <<A< 11r"out_hw.c>tj|dd|S)z+Upsample input with bilinear interpolation.bilinearF)sizemode align_corners)rUpsample)rrs r! _upsamplers I2;;F5 I' RRr"in_featepscntj|tj|dzddz}||z S)zNormalize input tensor.r(T)dimr)r3sqrtsum)rr norm_factors r!_normalize_tensorrs1**S599WaZQ#MMNK [  r"rCrc|jd|kDr@|jd|kDr.tjjj |||fdStjjj |||fddS)zlhttps://github.com/toshas/torch-fidelity/blob/master/torch_fidelity/sample_similarity_lpips.py#L127C22-L132.area)rrF)rr)shaper3r functional interpolate)rCrs r!_resize_tensorrspwwr{TaggbkD0xx""..q4,V.LL 88   * *1tTl[` * aar"cJeZdZUdZeed<eed<dfd ZdedefdZxZS) ScalingLayerzScaling layer.shiftscalerct||jdtjgddddddfd|jdtjgddddddfdy)Nr)gQgI +gMbȿF) persistentr)gZd;O?gy&1?g?)r/r0register_bufferr3r)r;rBs r!r0zScalingLayer.__init__sp  Well3K<STVZ\`M`&anst Well3H&I$PQSWY]J]&^kpqr"inpc:||jz |jz SrE)rr)r;rs r!rXzScalingLayer.forwardsdjj DJJ..r")rN) rPrQrRr[rrSr0rXr]r^s@r!rrs) M Mr /6/f/r"rc FeZdZdZd dedededdffd Zdedefd ZxZ S) NetLinLayerz,A single linear layer which does a 1x1 conv.chn_inchn_out use_dropoutrNc t||rtjgng}|tj||ddddgz }tj ||_y)NrrF)stridepaddingbias)r/r0rDropoutConv2dr4r )r;rrrlayersrBs r!r0zNetLinLayer.__init__sV #."**,B IIfgqAE J  ]]F+ r"rCc$|j|Sr)r )r;rCs r!rXzNetLinLayer.forwardszz!}r")rF) rPrQrRr[intr\r0rrXr]r^s@r!rrs;6,s,S,4,TX,Fr"rceZdZ ddededdedededed eed ed eed dffd Z dde de deded e e e e e e fff dZ xZS)_LPIPSNrralexvggsqueezespatial pnet_rand pnet_tuner model_path eval_moderesizerc .t |||_||_||_||_| |_t|_|jdvrt} gd|_ n=|jdk(rt} gd|_ n|jdk(rt} gd|_ t|j|_ |j |j|_t!|jd| |_t!|jd | |_t!|jd | |_t!|jd | |_t!|jd | |_|j"|j$|j&|j(|j*g|_|jdk(rit!|jd| |_t!|jd| |_|xj,|j.|j0gz c_t3j4|j,|_|r|_t6j8j;t6j8j=t?j@|jdd|d}|jCtEjF|dd|r|jI|js|jKD] } d| _& yy)aInitializes a perceptual loss torch.nn.Module. Args: pretrained: This flag controls the linear layers should be pretrained version or random net: Indicate backbone to use, choose between ['alex','vgg','squeeze'] spatial: If input should be spatial averaged pnet_rand: If backbone should be random or use imagenet pre-trained weights pnet_tune: If backprop should be enabled for both backbone and linear layers use_dropout: If dropout layers should be added model_path: Model path to load pretained models from eval_mode: If network should be in evaluation mode resize: If input should be resized to this size )rr)@rr)rrrr)rrrrrrr)rr%r)rrr(rrwr)Nz..z lpips_models/z.pthcpu) map_locationF)strict)'r/r0 pnet_typerrrrr scaling_layerruchnsr`r$lenLrrlin0lin1lin2lin3lin4linslin5lin6rr8ospathabspathjoininspectgetfileload_state_dictr3loadevalr:r%) r;rrrrrrrrrnet_typerArBs r!r0z_LPIPS.__init__ sH4 ""  )^ >>- -H0DI ^^v %H0DI ^^y (!H:DITYY4>>'9X ! +F  ! +F  ! +F  ! +F  ! +F YY 499diiK >>Y &#DIIaLkJDI#DIIaLkJDI II$))TYY/ /IMM$)), !WW__GGLL!?WZV[[_G`a   JU!KTY Z  IIK~~* ,&+# ,r"in0in1 retperlayer normalizec ^|rd|zdz }d|zdz }|j||j|}}|j.t||j}t||j}|jj ||jj |}}iii} } } t |j D]6} t|| t|| c| | <| | <| | | | z dz| | <8g} t |j D]} |jrI| jt|j| | | t|jddX| jt|j| | | dt| }|r|| fS|S)Nr(r)r)rT)r)rrrrrXr2rrrr7rrtuplerrr)r;rrrr in0_input in1_inputouts0outs1feats0feats1diffskkresvals r!rXz_LPIPS.forwardUs c'A+Cc'A+C $11#68J8J38O9  ;; "&yt{{CI&yt{{CIxx'' 2DHH4D4DY4Ou "B- 7B%6uRy%ACTUZ[]U^C_ "F2Jr fRj0Q6E"I 7- VB|| 9]TYYr]59%=eCIIVWVXMFZ[\ +MDIIbM%),DtTU  V #h :  r") TrFFFTNTN)FF)rPrQrRr\r rr6rr0rrrrrXr]r^s@r!rr s 17 $( $I,I,- .I, I,  I,  I,I,SMI,I, I, I,XV[   & 59 NR  vuVT&\122 3 r"rc,eZdZdZdeddffd ZxZS) _NoTrainLpipsz8Wrapper to make sure LPIPS never leaves evaluation mode.rrc"t|dS)z.Force network to always be in evaluation mode.F)r/train)r;rrBs r!rz_NoTrainLpips.train{sw}U##r")rPrQrRr[r\rr]r^s@r!rrxsB$$$?$$r"rimgrc|r(|jdkxr&|jdk\n|jdk\}|jdk(xr|jddk(xr|S)z1Check that input is a valid image to the network.g?grrwrr)maxminndimr)rr value_checks r! _valid_imgr sV;D#'')s"7swwyC'7#'')WY/K 88q= >SYYq\Q. >;>r"img1img2c Jt||r t||sstd|jd|jd|j|j gd|j|j gd|rddgnddgd |||| j S) NzeExpected both input arguments to be normalized tensors with shape [N, 3, H, W]. Got input with shape z and z and values in range z+ when all values are expected to be in the rrrz range.)r)r  ValueErrorrr rr)r rrrs r! _lpips_updaters tY 'JtY,G %%)ZZLdjj\BTXXZ() DHHJ/G.HI&09q!fAw%Gw P  tTY / 7 7 99r"scores reduction)rrnonec|dk(r|jS|dk(r|jS|dk(s||Std|)NrrrzInvalid reduction type: )rrr)rrs r!_lpips_computersOF{{}Ezz|Fi/ / {; <>> from torch import rand >>> from torchmetrics.functional.image.lpips import learned_perceptual_image_patch_similarity >>> img1 = (rand(10, 3, 100, 100) * 2) - 1 >>> img2 = (rand(10, 3, 100, 100) * 2) - 1 >>> learned_perceptual_image_patch_similarity(img1, img2, net_type='squeeze') tensor(0.1005) >>> from torch import rand, Generator >>> from torchmetrics.functional.image.lpips import learned_perceptual_image_patch_similarity >>> gen = Generator().manual_seed(42) >>> img1 = (rand(2, 3, 100, 100, generator=gen) * 2) - 1 >>> img2 = (rand(2, 3, 100, 100, generator=gen) * 2) - 1 >>> learned_perceptual_image_patch_similarity(img1, img2, net_type='squeeze', reduction='none') tensor([0.1024, 0.0938]) )r)devicedtype)rtorrrr)r rrrrrlosss r!rrsDT H % ( ( 4:: ( NC tS) 4D $ **r")F)T))rr)g:0yE>)rr)rrF))rrtypingrrrrr3rrtyping_extensionsr torchmetrics.utilities.importsr r__doctest_skip__r6r\modules containerr4rModuler$r`rurrrrfloatrrrrrrr rrrrTr"r!r$s,2 44 %A-  CE]^"**BVBVBaBa0%&%&P/Lehhoo/Ld/REHHOO/Rd2f222 SvSuS#XSfS !v!E!V! bfbCbb /299 / "))"lRYYl^$F$?F?t?? ::f:299::RX:=6=hw?T7U.V=dj=39:@ ,+ ,+ ,+./,+ 567 ,+  ,+  ,+r"