a  dފ@s(dZddlZddlZddlZddlmZddlmZmZm Z m Z ddl m Z m Z mZmZmZddl Z ddlZedde jddd DZd Zd Zed ed Zee dre jje jjfZe jjZ ne je jfZe jZ ddZ!ddZ"ddZ#ddZ$ddZ%ddZ&ddZ'ddZ(dd Z)d!d"Z*d#d$Z+d%d&Z,d'd(Z-dd*d+Z.d,d-Z/d.d/Z0d0d1Z1d2d3Z2d4d5Z3d6d7Z4d8d9Z5d:d;Z6dd?Z8d@dAZ9dBdCZ:ddGdHZ;dIdJZdOdPZ?dQdRZ@dSdTZAdUdVZBdWdXZCdYdZZDddde8e?e@eAeBeCeDe9e:e9e:e9e:e9e:ee>ee;d[dEd\ee;d]d^d\e;d_ZEe*e,e+e)e/e/e/e-e-e.e1e1e0e0e2e2e3e3e#e$e'e(e%e&e6e4e5d_ZFGd`dadaZGdbdcZHdddeZIdfdgZJdhdiZKdjdkZLddmdnZMGdodpdpZNdeOe edqdrdsZPgdtZQgduZRgdvZSdwdwdwdxd d dydydydydydydydydzZTdxd d dydyd{d{d{d|d|d|d|d|d|d|d}ZUed~ddZVdeOdddZWde eXeYfeYe ee e eefdddZZGdddZ[deOe ee e eOeefdddZ\gdZ]de eXeYfe ee e eOeefdddZ^GdddZ_deOe edqddZ`dS)a AutoAugment, RandAugment, AugMix, and 3-Augment for PyTorch This code implements the searched ImageNet policies with various tweaks and improvements and does not include any of the search code. AA and RA Implementation adapted from: https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/autoaugment.py AugMix adapted from: https://github.com/google-research/augmix 3-Augment based on: https://github.com/facebookresearch/deit/blob/main/README_revenge.md Papers: AutoAugment: Learning Augmentation Policies from Data - https://arxiv.org/abs/1805.09501 Learning Data Augmentation Strategies for Object Detection - https://arxiv.org/abs/1906.11172 RandAugment: Practical automated data augmentation... - https://arxiv.org/abs/1909.13719 AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty - https://arxiv.org/abs/1912.02781 3-Augment: DeiT III: Revenge of the ViT - https://arxiv.org/abs/2204.07118 Hacked together by / Copyright 2019, Ross Wightman N)partial)DictListOptionalUnion)ImageImageOps ImageEnhance ImageChops ImageFiltercCsg|] }t|qS)int).0xr r _/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/timm/data/auto_augment.py "r.)rrg$@)translate_constimg_mean ResamplingcCs(|dt}t|ttfr$t|S|S)Nresample)pop_DEFAULT_INTERPOLATION isinstancelisttuplerandomchoice)kwargs interpolationr r r_interpolation5s  r$cCs*d|vrtdkr|dt||d<dS)N fillcolorrr)_PIL_VERrr$)r"r r r_check_args_tf<s r)c Ks.t||j|jtjd|ddddffi|SNrr) transformsizerAFFINEimgfactorr"r r rshear_xBsr3c Ks.t||j|jtjddd|ddffi|Sr*r,r0r r rshear_yGsr4c Ks<||jd}t||j|jtjdd|dddffi|S)Nrr+r.r)r-rr/r1Zpctr"pixelsr r rtranslate_x_relLsr8c Ks<||jd}t||j|jtjddddd|ffi|Sr*r5r6r r rtranslate_y_relRsr9c Ks.t||j|jtjdd|dddffi|Sr*r,r1r7r"r r rtranslate_x_absXsr;c Ks.t||j|jtjddddd|ffi|Sr*r,r:r r rtranslate_y_abs]sr<c Ks(t|tdkr"|j|fi|Stdkr|j\}}d}|d|df}t| }tt|dtt|ddtt| dtt|ddg}dd} | |d  |d |d  |d |\|d <|d <|d |d 7<|d |d 7<|j |jt j |fi|S|j||d dS)N)r'rr&)rr@c Ss8|\}}}}}}||||||||||fSNr ) rymatrixabcdefr r rr-tszrotate..transformrr+rr'r)r) r)r(rotater.mathradiansroundcossinr-rr/) r1degreesr"whZ post_transZ rotn_centerZanglerBr-r r rrIbs.    "rIcKs t|Sr@)rZ autocontrastr1__r r r auto_contrastsrTcKs t|Sr@)rinvertrRr r rrUsrUcKs t|Sr@)requalizerRr r rrVsrVcKs t||Sr@)rsolarize)r1threshrSr r rrWsrWrcKstg}tdD],}||kr.|td||q ||q |jdvrp|jdkrft|dkrf|||}||S|S)N)LRGBr\)rangeappendminmodelenZpoint)r1addrXrSZlutir r r solarize_adds     rdcKs|dkr |St||S)N)r posterize)r1Z bits_to_keeprSr r rrfsrfcKst||Sr@)r Contrastenhancer1r2rSr r rcontrastsrjcKst||Sr@)r Colorrhrir r rcolorsrlcKst||Sr@)r Brightnessrhrir r r brightnesssrncKst||Sr@)r Sharpnessrhrir r r sharpnesssrpcKs|tj|d}|S)NZradius)filterr GaussianBlurrir r r gaussian_blursrtcKs*d}d}|tjt|||d}|S)N皙?r=rq)rrr rsr uniform)r1r2rSZ radius_minZ radius_maxr r rgaussian_blur_randsrwcKs$tdtdd|}t||S)N?r?)r_maxr rkrh)r1r2_r r r desaturatesr{cCstdkr| S|S)zWith 50% prob, negate the value?)r )vr r r_randomly_negatesr~cCs|td}t|}|fS)Ng>@ _LEVEL_DENOMr~level_hparamsr r r_rotate_level_to_args rcCs|tddfS)Ng?ru)rrr r r_enhance_level_to_argsrcCs$|td}tddt|}|fS)Ng?rurx)rryr~rr r r _enhance_increasing_level_to_args rr?rxTcCs2|t}||||}|r,t|t||}|fSr@)rryr_)rrmin_valmax_valclampr r r_minmax_level_to_args rcCs|td}t|}|fS)Ng333333?rrr r r_shear_level_to_args rcCs&|d}|tt|}t|}|fS)Nr)rfloatr~)rhparamsrr r r_translate_abs_level_to_argsrcCs&|dd}|t|}t|}|fS)N translate_pctg?)getrr~)rrrr r r_translate_rel_level_to_args  rcCst|tdfSNr rrr r r_posterize_level_to_argsrcCsdt||dfS)Nrr)r)rrr r r"_posterize_increasing_level_to_argsrcCst|tddfSrrrr r r _posterize_original_level_to_argsrcCstdt|tdfS)NrYr_r rrr r r_solarize_level_to_argsrcCsdt||dfS)NrYr)rrr r r!_solarize_increasing_level_to_argsrcCstdt|tdfS)Nrnrrr r r_solarize_add_level_to_arg!srr|)rrrur=) AutoContrastEqualizeInvertRotate PosterizePosterizeIncreasingPosterizeOriginalSolarizeSolarizeIncreasing SolarizeAddrkColorIncreasingrgContrastIncreasingrmBrightnessIncreasingroSharpnessIncreasingShearXShearYZ TranslateXZ TranslateY TranslateXRel TranslateYRel DesaturatersGaussianBlurRandc@s&eZdZd ddZddZdd ZdS) AugmentOpr| NcCs|pt}||_t||_t||_||_||_||_ t d|vrJ|dnt d|vr\|dnt d|_ |j dd|_|j dd|_dS)Nrr#)r%r magnitude_stdr magnitude_max)_HPARAMS_DEFAULTname NAME_TO_OPaug_fn LEVEL_TO_ARGlevel_fnprob magnitudecopyrdict_FILL_RANDOM_INTERPOLATIONr"rrr)selfrrrrr r r__init__gs    zAugmentOp.__init__cCs|jdkrt|jkr|S|j}|jdkr`|jtdkrHtd|}n|jdkr`t||j}|jpht}t dt ||}|j dur| ||j nt }|j|g|Ri|jS)Nrxrinfr?)rr rrrrvgaussrrryr_rrrrr")rr1rZ upper_boundZ level_argsr r r__call__|s   zAugmentOp.__call__cCsZ|jjd|jd|j}|d|jd|j7}|jdurN|d|j7}|d7}|S)Nz(name=z, p=z, m=z, mstd=z, mmax=)) __class____name__rrrrr)rfsr r r__repr__s  zAugmentOp.__repr__)r|rNr __module__ __qualname__rrrr r r rres rcsddgddgddgddgd d gd d gd dgddgddgddgddgddgddgddgddgddgd d!gd"d#gd$d%gd&d'gd(d)gd*d+gd,d-gdd.gd/d0gg}fd1d2|D}|S)3Nr皙?r+rrrrk皙? r333333?rkrr+rrrerrrrrrrrrrrrk皙?rrrrerrrerrrrrrrkrr+rrxrrrrrrrrrxrrrrrkrrrrr)rrrrrrrrerkrrrrrrrrrrxrrrrrr?rrrrrrrrkrrrkrxrrrrrrrrr?rerrxrrrrrrrrrr)rrrrrrrrr+rkrrrrr'csg|]}fdd|DqS)csg|]}t|diqSrrrrCrr rrrz5auto_augment_policy_v0...r rsprr rrrz*auto_augment_policy_v0..r rpolicyZpcr rrauto_augment_policy_v0s8r csddgddgddgddgd d gd d gd dgddgddgddgddgddgddgddgddgddgd d!gd"d#gd$d%gd&d'gd(d)gd*d+gd,d-gdd.gd/d0gg}fd1d2|D}|S)3Nrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrr)rrrrrrrcsg|]}fdd|DqS)csg|]}t|diqSrrrrr rrrz6auto_augment_policy_v0r...r rrr rrrz+auto_augment_policy_v0r..r r r rrauto_augment_policy_v0rs8r csddgddgddgddgd d gd d gd dgddgddgddgd dgddgddgddgddgd dgddgd d!gd"d#gddgd d gddgddgddgddgg}fd$d%|D}|S)&N)rrrerrrrrr'rrr'rr)rrr)rrrrrrrrrrrerrrrrr)rrr'rrrrrrrre)rrrrkrrrrrrrrrr?rrrrrrxrerrgrxrerkrxrrkrrerrrrorrrrrerrr'rcsg|]}fdd|DqS)csg|]}t|diqSrrrrr rrrz;auto_augment_policy_original...r rrr rrrz0auto_augment_policy_original..r r r rrauto_augment_policy_originals8r$csddgddgddgddgd d gd d gd dgddgddgddgd dgddgddgddgddgd dgddgd d!gd"d#gddgd d gddgddgddgddgg}fd$d%|D}|S)&N)rrrerrrrr)rrr)rrrrrrrrr)rrr'rrrrr rrrrrrrrrrr r!r"r#rcsg|]}fdd|DqS)csg|]}t|diqSrrrrr rrrz..r rrr rrrz1auto_augment_policy_originalr..r r r rrauto_augment_policy_originalrs8r%cs&dgdgdgg}fdd|D}|S)N)rrxr')rrxr)rrxrcsg|]}fdd|DqS)csg|]}t|diqSrrrrr rr"rz5auto_augment_policy_3a...r rrr rr"rz*auto_augment_policy_3a..r r r rrauto_augment_policy_3as r&v0cCsn|pt}|dkrt|S|dkr(t|S|dkr8t|S|dkrHt|S|dkrXt|SdsjJd|dS)NoriginalZ originalrr'Zv0r3aFzUnknown AA policy )rr$r%r r r&)rrr r rauto_augment_policy&sr*c@s$eZdZddZddZddZdS) AutoAugmentcCs ||_dSr@)r )rr r r rr7szAutoAugment.__init__cCs"t|j}|D] }||}q|Sr@)r r!r )rr1Z sub_policyopr r rr:s  zAutoAugment.__call__cCsL|jjd}|jD],}|d7}|ddd|D7}|d7}q|d7}|S)Nz(policy=z [z, cSsg|] }t|qSr )str)rr,r r rrDrz(AutoAugment.__repr__..]r)rrr join)rrpr r rr@s   zAutoAugment.__repr__Nrr r r rr+5sr+) config_strrc Cs|d}|d}|dd}|D]T}td|}t|dkr@q"|dd\}}|dkrj|dt|q"d s"Jd q"t||d }t|S) az Create a AutoAugment transform Args: config_str: String defining configuration of auto augmentation. Consists of multiple sections separated by dashes ('-'). The first section defines the AutoAugment policy (one of 'v0', 'v0r', 'original', 'originalr'). The remaining sections: 'mstd' - float std deviation of magnitude noise applied Ex 'original-mstd0.5' results in AutoAugment with original policy, magnitude_std 0.5 hparams: Other hparams (kwargs) for the AutoAugmentation scheme Returns: A PyTorch compatible Transform -rr+N(\d.*)rmstdrFz"Unknown AutoAugment config sectionr)splitrera setdefaultrr*r+) r1rconfigZ policy_namerEcskeyvalZ aa_policyr r rauto_augment_transformJs     r<)rrrrrrrrkrgrmrorrrr)rrrrrrrrrrrrrrr)rrrsrrr+)rrrsrrrrrrrrrrrg?g?)rrrrrrrrrrrrrrr) transformscCs4tt|\}}t|}|t|}||fSr@)rzipitemsnparraysum)r=Zprobsr r r_get_weighted_transformss rC)rcCs0|dkr tS|dkrtS|dkr$tS|r,tStS)NweightsZ3awr))_RAND_WEIGHTED_0_RAND_WEIGHTED_3A_RAND_3A_RAND_INCREASING_TRANSFORMS_RAND_TRANSFORMS)r increasingr r rrand_augment_choicessrKrrrrr=cs&pt|pt}fdd|DS)Ncsg|]}t|dqS)rrrrrrrrrr rrsz$rand_augment_ops..)rrIrLr rOrrand_augment_opss rPc@s&eZdZd ddZddZddZdS) RandAugmentrNcCs||_||_||_dSr@)ops num_layerschoice_weights)rrRrSrTr r rrszRandAugment.__init__cCs6tjj|j|j|jdu|jd}|D] }||}q$|S)N)replacer0)r@r r!rRrSrT)rr1rRr,r r rrs zRandAugment.__call__cCs<|jjd|jd}|jD]}|d|7}q|d7}|S)Nz(n=, ops= r)rrrSrRrrr,r r rrs  zRandAugment.__repr__)rNrr r r rrQs  rQ)r1rr=cCst}d}d}d}|d}|ddks*J|dd}|D]}|d rft|dd} |durd| }q:td |} t| dkrq:| dd\} } | d krt| } | d krtd } |d| q:| dkr|dt| q:| dkrt | rd}q:| dkrt| }q:| dkrt| }q:| dkr*t| }q:ds:Jdq:t |trRt ||d}n|durj|rft nt }d} t |trt|\}} t||||d}t||| dS)a+ Create a RandAugment transform Args: config_str (str): String defining configuration of random augmentation. Consists of multiple sections separated by dashes ('-'). The first section defines the specific variant of rand augment (currently only 'rand'). The remaining sections, not order sepecific determine 'm' - integer magnitude of rand augment 'n' - integer num layers (number of transform ops selected per image) 'p' - float probability of applying each layer (default 0.5) 'mstd' - float std deviation of magnitude noise applied, or uniform sampling if infinity (or > 100) 'mmax' - set upper bound for magnitude to something other than default of _LEVEL_DENOM (10) 'inc' - integer (bool), use augmentations that increase in severity with magnitude (default: 0) 't' - str name of transform set to use Ex 'rand-m9-n3-mstd0.5' results in RandAugment with magnitude 9, num_layers 3, magnitude_std 0.5 'rand-mstd1-tweights' results in mag std 1.0, weighted transforms, default mag of 10 and num_layers 2 hparams (dict): Other hparams (kwargs) for the RandAugmentation scheme Returns: A PyTorch compatible Transform rFr|r2rZrandr+Ntr3r4drrZmmaxrincTmnr0z"Unknown RandAugment config section)rJrL)rT)rr5 startswithr-r6rarr7r boolrrKrHrIrrCrPrQ)r1rr=rrSrJrr8rEr;r9r:r4rTZra_opsr r rrand_augment_transformsT               r`) rrrrrrrrrrrrrrrr=cs$pt|pt}fdd|DS)Ncsg|]}t|ddqS)rxrMrrNrrr rrcszaugmix_ops..)r_AUGMIX_TRANSFORMSrar rbr augmix_ops\s  rdc@sBeZdZdZdddZdd Zd d Zd d ZddZddZ dS) AugMixAugmenta AugMix Transform Adapted and improved from impl here: https://github.com/google-research/augmix/blob/master/imagenet.py From paper: 'AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty - https://arxiv.org/abs/1912.02781 rxrFcCs"||_||_||_||_||_dSr@)rRalphawidthdepthblended)rrRrgrhrirjr r rrqs zAugMixAugment.__init__cCs\||}d}g}|dddD]"}||}|d|9}||qtj|dddtjdS)Nrxrfr+Zdtype)r^r@rAfloat32)rwsr\ZcumpZrwsrPrgr r r_calc_blended_weightsxs  z#AugMixAugment._calc_blended_weightsc Csx|}|||}|D]Z}|jdkr,|jn tjdd}tjj|j|dd}|} |D] } | | } qVt || |}q|S)Nrr+rTrU) rrnrir@r randintr!rRrblend) rr1mixing_weightsr\Zimg_origrmrPrirRimg_augr,r r r_apply_blendeds  zAugMixAugment._apply_blendedc Cs|jd|jdt|f}tj|tjd}|D]d}|jdkrF|jn tjdd}tjj |j |dd}|} |D] } | | } qp||tj | tjd7}q2tj |dd|dt |tj}t |||S) Nrr+rkrTrogo@)out)r.raZgetbandsr@zerosrlrir rpr!rRZasarrayZcliprZ fromarrayZastypeZuint8rq) rr1rrr\Z img_shapemixedmwrirRrsr,r r r _apply_basics zAugMixAugment._apply_basiccCs\ttj|jg|j}ttj|j|j}|jrJ||||}n| |||}|Sr@) r@rlr Z dirichletrgrhbetarjrtry)rr1rrr\rwr r rrs zAugMixAugment.__call__cCsL|jjd|jd|jd|jd}|jD]}|d|7}q,|d7}|S)Nz(alpha=z, width=z, depth=rVrWr)rrrgrhrirRrXr r rrs & zAugMixAugment.__repr__N)rxrrfF) rrr__doc__rrnrtryrrr r r rreks   rec Csd}d}d}d}d}|d}|ddks.J|dd }|D]}td |} t| d kr\q>| d d \} } | d kr|d t| q>| dkrt| }q>| dkrt| }q>| dkrt| }q>| dkrt| }q>| dkrt| }q>ds>Jdq>|d tdt||d} t| ||||dS)a  Create AugMix PyTorch transform Args: config_str (str): String defining configuration of random augmentation. Consists of multiple sections separated by dashes ('-'). The first section defines the specific variant of rand augment (currently only 'rand'). The remaining sections, not order sepecific determine 'm' - integer magnitude (severity) of augmentation mix (default: 3) 'w' - integer width of augmentation chain (default: 3) 'd' - integer depth of augmentation chain (-1 is random [1, 3], default: -1) 'b' - integer (bool), blend each branch of chain into end result without a final blend, less CPU (default: 0) 'mstd' - float std deviation of magnitude noise applied (default: 0) Ex 'augmix-m5-w4-d2' results in AugMix with severity 5, chain width 4, chain depth 2 hparams: Other hparams (kwargs) for the Augmentation transforms Returns: A PyTorch compatible Transform rrfrxFr2rZaugmixr+Nr3rr4rr\rPrFrCrDzUnknown AugMix config sectionr)rr)rgrhrirj) r5r6rar7rr r_rdre) r1rrrhrirgrjr8rEr9r:r;rRr r raugment_and_mix_transforms:          r|)r)r?rxT)r'N)N)T)rr|NN)NN)rNN)N)ar{r rJr6 functoolsrtypingrrrrZPILrrr r r numpyr@r __version__r5r(rrrrhasattrrZBILINEARZBICUBICrrr$r)r3r4r8r9r;r<rIrTrUrVrWrdrfrjrlrnrprtrwr{r~rrrrrrrrrrrrrrrrr r r$r%r&r*r+r-r<rIrHrGrFrErCrKr rrPrQr`rcrdrer|r r r rst "       !2!"!! #    Q  I