jN@SSKrSSKrSSKrSSKrSSKJrJrJrJr SSK r SSK J r SSK J s J r SSK Jr SrSSKJr SSKr/SQr"SS 5r"S S 5r"S S \ R05r"SS5r\"\S5(a\R8R:S\R8R<S\R8R>S\R8R@S\R8RBS\R8RDS0r#OJ\R:S\R<S\R>S\R@S\RBS\RDS0r#\#RI5VVs0sHupX_M snnr%\(ap\R:S\R<S\R>S\R@S\RBS\RDS0r&\&RI5VVs0sHupX_M snnr'O0r(0r&Sr)Sr*Sr+\*"S5\*"S54r,S2Sjr-"SS5r.S3S\ R^S\\0\\04S\\0\\0\0\044S \1S!\ R^4 S"jjr2"S#S$\ RfRh5r5S3S\ R^S%\0S&\0S'\0S(\0S\\0\\0\0\044S \1S!\ R^4S)jjr6"S*S+\ RfRh5r7"S,S-5r8"S.S/5r9"S0S1\ RfRh5r:g!\a SrGNf=fs snnfs snnf)4N)ListSequenceTupleUnion)InterpolationModeTF)Image)ToNumpyToTensorstr_to_interp_modestr_to_pil_interpinterp_mode_to_str!RandomResizedCropAndInterpolationCenterCropOrPadcenter_crop_or_pad crop_or_padRandomCropOrPad RandomPadResizeKeepRatio TrimBorder MaybeToTensorMaybePILToTensorc\rSrSrSrSrg)r c[R"U[RS9nURS:a[R"USS9n[R "US5nU$)Ndtype)axis)nparrayuint8ndim expand_dimsrollaxis)selfpil_imgnp_imgs ڢC:\Users\johnn\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.13_qbz5n2kfra8p0\LocalCache\local-packages\Python313\site-packages\timm/data/transforms.py__call__ToNumpy.__call__sF'2 ;;?^^F4FVQ' N)__name__ __module__ __qualname____firstlineno__r+__static_attributes__r.r-r*r r sr-r c>\rSrSrSr\R 4SjrSrSr g)r #z$ToTensor with no rescaling of valuescXlgNr)r'rs r*__init__ToTensor.__init__%s r-c\[R"U5RURS9$)Nr)F pil_to_tensortor)r'r(s r*r+ToTensor.__call__(s#w'***<[TU]5 gr7superr8r' __class__s r*r8MaybeToTensor.__init__0 r-cp[U[R5(aU$[R"U5$)zu Args: pic (PIL Image or numpy.ndarray): Image to be converted to tensor. Returns: Tensor: Converted image. ) isinstancer@Tensorr; to_tensorr'pics r*r+MaybeToTensor.__call__3s) c5<< ( (J{{3r-c4URRS3$Nz()rIr/r's r*__repr__MaybeToTensor.__repr__?..))*"--r-r.rDN) r/r0r1r2r?r8r@rNr+strrWr3 __classcell__rIs@r*rr,s.  u||  .#..r-rcD^\rSrSrSrSU4SjjrSrS\4SjrSr U=r $) rCzSConvert a PIL Image to a tensor of the same type - this does not scale values. rDc">[TU]5 gr7rFrHs r*r8MaybePILToTensor.__init__GrKr-cp[U[R5(aU$[R"U5$)z Note: A deep copy of the underlying array is performed. Args: pic (PIL Image): Image to be converted to tensor. Returns: Tensor: Converted image. )rMr@rNr;r<rPs r*r+MaybePILToTensor.__call__Js) c5<< ( (Js##r-c4URRS3$rTrUrVs r*rWMaybePILToTensor.__repr__XrYr-r.rZ) r/r0r1r2r?r8r+r[rWr3r\r]s@r*rrCs# $.#..r-r Resamplingnearestbilinearbicubicboxhamminglanczosc[U$r7)_str_to_pil_interpolationmode_strs r*r r s $X ..r-c<[(a [U$[U$r7)has_interpolation_mode_str_to_torch_interpolationrnros r*r r s*844(22r-c<[(a [U$[U$r7)rr_torch_interpolation_to_str_pil_interpolation_to_str)modes r*r r s*400(..r-c[U[R5(a[U5[U54$[U[5(a[ U5S:Xa USUS4$[ U5S:wa [ U5eU$)Nrr )rMnumbersNumberintrlen ValueError)size error_msgs r* _setup_sizersk$''4y#d)##$!!c$i1nAwQ 4yA~## Kr-cD\rSrSrSrS Sjr\S5rSrSr Sr g) ra1Crop the given PIL Image to random size and aspect ratio with random interpolation. A crop of random size (default: of 0.08 to 1.0) of the original size and a random aspect ratio (default: of 3/4 to 4/3) of the original aspect ratio is made. This crop is finally resized to given size. This is popularly used to train the Inception networks. Args: size: expected output size of each edge scale: range of size of the origin size cropped ratio: range of aspect ratio of the origin aspect ratio cropped interpolation: Default: PIL.Image.BILINEAR c$[U[[45(a[U5UlOX4UlUSUS:d USUS:a[R "S5 US:Xa [ UlO[U5UlX l X0l g)Nrryz"range should be of kind (min, max)random) rMlisttuplerwarningswarn_RANDOM_INTERPOLATION interpolationr scaleratio)r'rrrrs r*r8*RandomResizedCropAndInterpolation.__init__s| dT5M * *d DI DI !HuQx U1Xa%8 MM> ? H $!6D !3M!BD   r-c j[R"U5up4X4-n[S5GH n[R"U6U-n[ R "US5[ R "US54n[ R"[R"U65n [[[ R"Xy-555n [[[ R"Xy- 555n X::dMX::dM[R"SXK- 5n [R"SX:- 5n XX4s $ X4- nU[U5:a#Un [[U [U5- 55n O6U[U5:a#Un [[U [U5-55n OUn Un XK- S-n X:- S-n XX4$)aQGet parameters for ``crop`` for a random sized crop. Args: img (PIL Image): Image to be cropped. scale (tuple): range of size of the origin size cropped ratio (tuple): range of aspect ratio of the origin aspect ratio cropped Returns: tuple: params (i, j, h, w) to be passed to ``crop`` for a random sized crop. rryr )r;get_image_sizerangeruniformmathlogexpr|roundsqrtrandintminmax)imgrrimg_wimg_hareaattempt target_area log_ratio aspect_ratiotarget_wtarget_hijin_ratios r* get_params,RandomResizedCropAndInterpolation.get_paramssu'', }RyG ..%047K%(+TXXeAh-?@I88FNNI$>?L5;+E!FGHH5;+E!FGHH X%6NN1e&67NN1e&67X//!= c%j H5CJ!678H E "H5CJ!678HHH  ! #  ! #X''r-c >URXRUR5up#pE[UR[ [ 45(a![R"UR5nO URn[R"XX4XPRU5$)zz Args: img (PIL Image): Image to be cropped and resized. Returns: PIL Image: Randomly cropped and resized image. ) rrrrMrrrrchoicer; resized_cropr)r'rrrhwrs r*r+*RandomResizedCropAndInterpolation.__call__so__S**djjA a d((5$- 8 8"MM$*<*<=M ..M~~caAyy-HHr-c4[UR[[45(a6SR URVs/sHn[ U5PM sn5nO[ UR5nUR RSRUR5-nUSR[SUR555- nUSR[SUR555- nUSRU5- nU$s snf)N (size={0}z , scale={0}c3:# UHn[US5v M g7fNr).0ss r* =RandomResizedCropAndInterpolation.__repr__.. 3TAE!QKKz , ratio={0}c3:# UHn[US5v M g7frr)rrs r*rr rrz, interpolation={0})) rMrrrjoinr rIr/formatrrrr'xinterpolate_str format_strings r*rW*RandomResizedCropAndInterpolation.__repr__s d((5$- 8 8!hhtGYGY'ZGY!(:1(=GY'Z[O01C1CDO//+2D2DTYY2OO --e3T3T.TUU --e3T3T.TUU /66GG ([sD)rrrrN))g{Gz??)g?gUUUUUU?rh r/r0r1r2r?r8 staticmethodrr+rWr3r.r-r*rrs7 "$$ *((((T I r-rr output_sizefill padding_moderDc[U5nUupE[R"U5upgnXX:dXG:avXX:aXX- S-OSXG:aXG- S-OSXX:a XX- S-S-OSXG:a XG- S-S-OS/n [R"X X#S9n[R"U5upgnXX:XaXG:XaU$[ [ Xt- S- 55n [ [ X- S- 55n [R "X XU5$)a?Center crops and/or pads the given image. If the image is torch Tensor, it is expected to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions. If image size is smaller than output size along any edge, image is padded with 0 and then center cropped. Args: img (PIL Image or Tensor): Image to be cropped. output_size (sequence or int): (height, width) of the crop box. If int or sequence with single int, it is used for both directions. fill (int, Tuple[int]): Padding color Returns: PIL Image or Tensor: Cropped image. r rryrrg@)rr;get_dimensionspadr|rcrop) rrrr crop_height crop_width_ image_height image_width padding_ltrbcrop_top crop_lefts r*rrs *k*K)K#$#3#3C#8 A[;#=/9/GZ %! +Q1<1K[ 'A -QR3=3KZ % )a /QR5@5O[ '! + 1UV  eeCDL'('7'7'<$  $)DJ5,4;<=HE;3s:;[TU]5 [U5UlX lX0lgr7rGr8rrrrr'rrrrIs r*r8CenterCropOrPad.__init__F' %  (r-cT[XRURURS9$)m Args: img (PIL Image or Tensor): Image to be cropped. Returns: PIL Image or Tensor: Cropped image. r)rrrr)r'rs r*forwardCenterCropOrPad.forwardQs!"#yytyytO`O`aar-rDcNURRSURS3$Nz(size=)rIr/rrVs r*rWCenterCropOrPad.__repr__[$..))*& 1==r-rrrrconstant)r/r0r1r2r?rr|rrr[r8rrWr3r\r]s@r*rr:so 67 * )T#Y' )U3S=112 ) ) )b>#>>r-rtopleftheightwidthc [R"U5upxn X$-n X-n US:dUS:d X:dX:aw[U*[SU 5-S5[U*[SU 5-S5[U [X5- S5[U [X5- S5/n [R"X XVS9n[US5n[US5n[R "XX#U5$)zZCrops and/or pads image to meet target size, with control over fill and padding_mode. rr)r;rrrrr) rrrrrrrrrrrightbottomrs r*rr_s$%#3#3C#8 A[ LE \F ax37e1V5J Au %q ) s1f~%q ) K.. 2 \// 3  eeCDL c1+C tQ[TU]5 [U5UlX lX0lgr7rrs r*r8RandomCropOrPad.__init__rr-c N[R"U5up#nX1S- nXAS- n[[R"[ R "S[U55U55n[[R"[ R "S[U55U55nXx4$)Nrry)r;rr|rcopysignrrabs) rrrrr delta_height delta_widthrrs r*rRandomCropOrPad.get_paramss'('7'7'<$#1g- !G+ $--q#l2C DlST4==3{3C!DkRSyr-c URXR5up#[UUUURSURSURURS9$)rrry)rrrrrr)rrrrr)r'rrrs r*rRandomCropOrPad.forwardsTOOC3  99Q<))A,**  r-rDcNURRSURS3$rrrVs r*rWRandomCropOrPad.__repr__rr-rr)r/r0r1r2r?rr|rrr[r8rrrrWr3r\r]s@r*rr{s 67 * )T#Y' )U3S=112 ) ) ) &>#>>r-rc4\rSrSrSSjr\S5rSrSrg)ricXlX lgr7) input_sizer)r'rrs r*r8RandomPad.__init__s $ r-c[R"U5up#[USU- S5n[USU- S5n[R"SU5n[R"SU5nXF- nXW- n XgX4$)Nryr)r;rrrr) rrrrrrpad_leftpad_top pad_right pad_bottoms r*rRandomPad.get_paramssy((- *Q-%/3 :a=6115 >>![1..L1* !+ )77r-c~URXR5n[R"XUR5nU$r7)rrr;rr)r'rpaddings r*r+RandomPad.__call__s-//#7eeC$)), r-)rrN)r) r/r0r1r2r8rrr+r3r.r-r*rrs 88r-rcZ\rSrSrSrS Sjr\S Sj5rSrSr Sr g) riz!Resize and Keep Aspect Ratio c  [U[[45(a[U5UlOX4UlUS:Xa [UlO[ U5Ul[U5UlX@l XPl X`l Xpl Xl g)z Args: size: longest: interpolation: random_scale_prob: random_scale_range: random_scale_area: random_aspect_prob: random_aspect_range: rN)rMrrrrrr floatlongestrandom_scale_probrandom_scale_rangerandom_scale_arearandom_aspect_probrandom_aspect_range) r'rrrrrrrrs r*r8ResizeKeepRatio.__init__so. dT5M * *d DI DI H $!6D !3M!BD W~ !2"4!2"4#6 r-c[R"U5SS=upn UupX- n X- n[X5U-[X5SU- --nUS:a[[R"5U:aB[R "USUS5nU(aS[ R"U5- nUU4nOSnUS:a[R"5U:a[ R"US5[ R"US54n[ R"[R "U65n[ R"U5nUSU- USU-4n[U U5VVs/sHunn[UU-U- 5PM nnnU$s snnf)zGet parameters ryNrr)rr) r;rrrrrrrrrzipr)r target_sizerrrrrrrrimg_sizerrratio_hratio_wr ratio_factor log_aspect aspect_factorrfrs r*rResizeKeepRatio.get_paramssr#$"2"23"7";; x(""G%/#g2G2PW<2XX q V]]_7H%H!>>*U>UVW>XY\=]]^` ` 01H1H0MNN 2 44Q7__ac c ([sD6)rrrrrrrrN)rhr()g333333?g?Fr(g?g(\?)r()rgHzG?Fr(r)rr.r-r*rrsT $ +#! +$7L !*#! +%%N(r-rc6^\rSrSrS\4U4SjjrSrSrU=r$)ri7 border_sizec.>[TU]5 Xlgr7)rGr8r+)r'r+rIs r*r8TrimBorder.__init__9s &r-c[R"U5up#UR=pE[XC5n[XS5n[ SUSUR-- 5n[ SUSUR-- 5n[R "XXVU5$)Nrr )r;rr+rrr)r'rrrrrrrs r*rTrimBorder.forward@s$%%%#k4|QA 0 0001Aq1t////0vvce44r-)r+) r/r0r1r2r|r8rr3r\r]s@r*rr7s''55r-r)z3Please provide only two dimensions (h, w) for size.r);rrzrrtypingrrrrr@torchvision.transforms transforms!torchvision.transforms.functional functionalr;rrr ImportErrorPILrnumpyr!__all__r r rrhasattrrfNEARESTBILINEARBICUBICBOXHAMMINGLANCZOSrvitemsrnrurs_pil_interpolation_to_torchr r r rrrrNr|r[rnnModulerrrrrr)abs00r*rFsj  // +--#C! ==.J''....8 5,   ) !!:   ) e   )   ) !  y   y 5 y y !/H.M.M.OP.OdaQT.OP!!9""J!!9u!!9!!9 #5P4U4U4W"X4WDA144W"X"$"$/3/,J79KI9VW ggZ23& 'E \\'E3S >*'ECsC}--.'E 'E  \\ 'ET">ehhoo">V23&1 \\1 11 1  1 CsC}--. 11 \\18,>ehhoo,>^,ttn55U#"#JQ#YsL,L"LLL