o پi@sdZddlZddlZddlZddlmZddlmZmZm Z m Z ddl m Z m Z mZmZmZddl Z ddlZedde jddd DZd Zd Zed ed Zee drce jje jjfZe jjZ n e 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@dSdTZAdUdVZBdWdXZCdYdZZDid[dd\dd]dd^e8d_e?d`e@daeAdbeBdceCddeDdee9dfe:dge9dhe:die9dje:dke9e:ee>ee;dldEdmee;dndodme;dp ZEid[e*d\e,d]e+d^e)d_e/d`e/dae/dbe-dce-dde.dee1dfe1dge0dhe0die2dje2dke3e3e#e$e'e(e%e&e6e4e5dp ZFGdqdrdrZGdsdtZHdudvZIdwdxZJdydzZKd{d|ZLdd~dZMGdddZNddeOde efddZPgdZQgdZRgdZSddddd d dddddddddZTdd d dddddddddddddZUdefddZVddeOfddZW l  dde eXeYfdeYde ede e eeffddZZGdddZ[  ddeOde ede e eOeeffddZ\gdZ]  dde eXeYfde ede e eOeeffddZ^GdddZ_ddeOde efddZ`dS)a AutoAugment, RandAugment, AugMix, and 3-Augment for PyTorch This code implements the searched ImageNet policies with various tweaks and improvements and does not include any of the search code. AA and RA Implementation adapted from: https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/autoaugment.py AugMix adapted from: https://github.com/google-research/augmix 3-Augment based on: https://github.com/facebookresearch/deit/blob/main/README_revenge.md Papers: AutoAugment: Learning Augmentation Policies from Data - https://arxiv.org/abs/1805.09501 Learning Data Augmentation Strategies for Object Detection - https://arxiv.org/abs/1906.11172 RandAugment: Practical automated data augmentation... - https://arxiv.org/abs/1909.13719 AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty - https://arxiv.org/abs/1912.02781 3-Augment: DeiT III: Revenge of the ViT - https://arxiv.org/abs/2204.07118 Hacked together by / Copyright 2019, Ross Wightman N)partial)DictListOptionalUnion)ImageImageOps ImageEnhance ImageChops ImageFiltercCg|]}t|qS)int).0xr r J/home/ubuntu/.local/lib/python3.10/site-packages/timm/data/auto_augment.py "r.)rrg$@)translate_constimg_mean ResamplingcCs(|dt}t|ttfrt|S|S)Nresample)pop_DEFAULT_INTERPOLATION isinstancelisttuplerandomchoice)kwargs interpolationr r r_interpolation5s  r%cCs*d|vr tdkr |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_xB&r4c Ks.t||j|jtjddd|ddffi|Sr+r-r1r r rshear_yGr5r6c Ks<||jd}t||j|jtjdd|dddffi|S)Nrr,r/r*r.rr0r2pctr#pixelsr r rtranslate_x_relL&r;c Ks<||jd}t||j|jtjddddd|ffi|Sr+r7r8r r rtranslate_y_relRr<r=c Ks.t||j|jtjdd|dddffi|Sr+r-r2r:r#r r rtranslate_x_absXr5r?c Ks.t||j|jtjddddd|ffi|Sr+r-r>r r rtranslate_y_abs]r5r@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.ts(zrotate..transformrr,rr(r)r) r*r)rotater/mathradiansroundcossinr.rr0) r2degreesr#wh post_trans rotn_centeranglerFr.r r rrMbs.   "rMcK t|SrD)r autocontrastr2__r r r auto_contrast r]cKrYrD)rinvertr[r r rr_r^r_cKrYrD)requalizer[r r rr`r^r`cKs t||SrD)rsolarize)r2threshr\r r rras rarcKstg}tdD]}||kr|td||q||q|jdvr8|jdkr3t|dkr3|||}||S|S)N)LRGBrf)rangeappendminmodelenpoint)r2addrbr\lutir r r solarize_adds     rpcKs|dkr|St||S)N)r posterize)r2 bits_to_keepr\r r rrrs rrcKt||SrD)r Contrastenhancer2r3r\r r rcontrastrxcKrtrD)r Colorrvrwr r rcolorryr{cKrtrD)r Brightnessrvrwr r r brightnessryr}cKrtrD)r Sharpnessrvrwr r r sharpnessryrcKs|tj|d}|S)Nradius)filterr GaussianBlurrwr r r gaussian_blursrcKs*d}d}|tjt|||d}|S)N皙?rAr)rr rr!uniform)r2r3r\ radius_min radius_maxr r rgaussian_blur_randsrcKs$tdtdd|}t||S)N?rC)rimaxr rzrv)r2r3_r r r desaturatesrcCstdkr | S|S)zWith 50% prob, negate the value?)r!)vr r r_randomly_negatesrcC|td}t|}|fS)Ng>@ _LEVEL_DENOMrlevel_hparamsr r r_rotate_level_to_arg rcCs|tddfS)Ng?r)rrr r r_enhance_level_to_argsrcCs$|td}tddt|}|fS)Ng?rr)rrrrr r r _enhance_increasing_level_to_args rrCrTcCs2|t}||||}|rt|t||}|fSrD)rrri)rrmin_valmax_valclampr r r_minmax_level_to_args rcCr)Ng333333?rrr r r_shear_level_to_argrrcCs&|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|tdfSNrrrr r r_posterize_level_to_argsrcCdt||dfS)Nrr)r)rrr r r"_posterize_increasing_level_to_argsrcCst|tddfSrrrr r r _posterize_original_level_to_argsrcCstdt|tdfS)Nrcrirrrr r r_solarize_level_to_argsrcCr)Nrcr)rrr r r!_solarize_increasing_level_to_argsrcCstdt|tdfS)Nrnrrr r r_solarize_add_level_to_arg!sr AutoContrastEqualizeInvertRotate PosterizePosterizeIncreasingPosterizeOriginalSolarizeSolarizeIncreasing SolarizeAddrzColorIncreasingruContrastIncreasingr|BrightnessIncreasingr~r)rrrrA) SharpnessIncreasingShearXShearY TranslateX TranslateY TranslateXRel TranslateYRel DesaturaterGaussianBlurRandc@s&eZdZd ddZddZdd ZdS) AugmentOpr NcCs|pt}||_t||_t||_||_||_||_ t d|vr%|dnt d|vr.|dnt d|_ |j dd|_|j dd|_dS)Nrr$)r&r magnitude_stdr magnitude_max)_HPARAMS_DEFAULTname NAME_TO_OPaug_fn LEVEL_TO_ARGlevel_fnprob magnitudecopyrdict_FILL_RANDOM_INTERPOLATIONr#rrr)selfrrrrr r r__init__gs    zAugmentOp.__init__cCs|jdkrt|jkr|S|j}|jdkr0|jtdkr$td|}n |jdkr0t||j}|jp4t}t dt ||}|j durI| ||j nt }|j|g|Ri|jS)NrrinfrC)rr!rrrrgaussrrrrirrr rr#)rr2r upper_bound level_argsr r r__call__|s   zAugmentOp.__call__cCsZ|jjd|jd|j}|d|jd|j7}|jdur'|d|j7}|d7}|S)Nz(name=z, p=z, m=z, mstd=z, mmax=)) __class____name__rrrrr)rfsr r r__repr__s  zAugmentOp.__repr__)rrNr __module__ __qualname__rrrr r r rres  rcddgddgddgddgd d gd d gd dgddgddgddgddgddgddgddgddgddgd d!gd"d#gd$d%gd&d'gd(d)gd*d+gd,d-gdd.gd/d0gg}fd1d2|D}|S)3Nr皙?r,rrrrz皙? r333333?rzrr,rrrqrrrrrrrrrrrrz皙?rrrrqrrrqrrrrrrrzrr,rrrrrrrrrrrrrrrrzrrrrr)rrrrrrrrqrzrrrrrrrrrrrrrrrrCrrrrrrrrzrrrzrrrrrrrrrrCrqrrrrrrrrrrrr)rrrrrrrrr,rzrrrrr(cg|] }fdd|DqS)cg|] }t|diqSrrrrGr r rrz5auto_augment_policy_v0...r rspr r rrz*auto_augment_policy_v0..r rpolicypcr r rauto_augment_policy_v08r*cr)3Nrrrrrrrrrrrrrrrrrrrrrrrrrrrrr r r r r rrrrrrrrrrrr)rrrrrrrcr)crr r!r"r r rrr#z6auto_augment_policy_v0r...r r$r r rrr&z+auto_augment_policy_v0r..r r'r r rauto_augment_policy_v0rs8r-cddgddgddgddgd d gd d gd dgddgddgddgd dgddgddgddgddgd dgddgd d!gd"d#gddgd d gddgddgddgddgg}fd$d%|D}|S)&N)rrrqrrrrrr(rrr(rr)rrr)rrrrr rrrrrrqrrrrrr)rrr(rrrrrrrrq)rrrrzrrrrrrrrrrCrrrrrrrqrrurrqrzrrrzrrqrrrr~rrrrrqrrr(rcr)crr r!r"r r rrr#z;auto_augment_policy_original...r r$r r rrr&z0auto_augment_policy_original..r r'r r rauto_augment_policy_originalr+rEcr.)&N)rrrqr/r0r1rr)rrr)rrrrr r2r3r4r5)rrr(rr6rr7r,r8r9r:r;r<r=rr>r?r@rArBrCrDrcr)crr r!r"r r rrr#z..r r$r r rrr&z1auto_augment_policy_originalr..r r'r r rauto_augment_policy_originalrr+rFcs&dgdgdgg}fdd|D}|S)N)rrr()rrr)rrrcr)crr r!r"r r rr"r#z5auto_augment_policy_3a...r r$r r rr"r&z*auto_augment_policy_3a..r r'r r rauto_augment_policy_3as rGv0cCsf|pt}|dkr t|S|dkrt|S|dkrt|S|dkr$t|S|dkr,t|SJd|)Noriginal originalrrHv0r3aFzUnknown AA policy )rrErFr*r-rG)rrr r rauto_augment_policy&srMc@s$eZdZddZddZddZdS) AutoAugmentcCs ||_dSrD)r()rr(r r rr7r^zAutoAugment.__init__cCs"t|j}|D]}||}q|SrD)r!r"r()rr2 sub_policyopr r rr:s  zAutoAugment.__call__cCsL|jjd}|jD]}|d7}|ddd|D7}|d7}q |d7}|S)Nz(policy=z [z, cSr r )str)rrPr r rrDrz(AutoAugment.__repr__..]r)rrr(join)rrpr r rr@s   zAutoAugment.__repr__Nrr r r rrN5s rN config_strrc Cs|d}|d}|dd}|D]'}td|}t|dkr q|dd\}}|dkr5|dt|qJd t||d }t|S) a 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'). While the remaining sections define other arguments * 'mstd' - float std deviation of magnitude noise applied hparams: Other hparams (kwargs) for the AutoAugmentation scheme Returns: A PyTorch compatible Transform Examples:: 'original-mstd0.5' results in AutoAugment with original policy, magnitude_std 0.5 -rr,N(\d.*)rmstdrFz"Unknown AutoAugment config sectionr )splitrerk setdefaultrrMrN) rUrconfig policy_namerIcskeyval aa_policyr r rauto_augment_transformJs     rb)rrrrrrrrzrur|r~rrrr)rrrrrrrrrrrrrrr)rrrrrr,)rrrrrrrrrrrrrrg?g?)rrrrrrrrrrrrrrr transformscCs4tt|\}}t|}|t|}||fSrD)rzipitemsnparraysum)rcprobsr r r_get_weighted_transformss rjrcCs0|dkrtS|dkr tS|dkrtS|rtStS)Nweights3awrL)_RAND_WEIGHTED_0_RAND_WEIGHTED_3A_RAND_3A_RAND_INCREASING_TRANSFORMS_RAND_TRANSFORMS)r increasingr r rrand_augment_choicess rsrrrcs&pt|pt}fdd|DS)Ncsg|] }t|dqS)rrrr!rrrrrr rrs  z$rand_augment_ops..)rrqrrrrcr rvrrand_augment_opss rxc@s&eZdZd ddZddZddZdS) RandAugmentrNcCs||_||_||_dSrD)ops num_layerschoice_weights)rrzr{r|r r rrs zRandAugment.__init__cCs6tjj|j|j|jdu|jd}|D]}||}q|S)N)replacerT)rfr!r"rzr{r|)rr2rzrPr r rrs zRandAugment.__call__cCs<|jjd|jd}|jD] }|d|7}q|d7}|S)Nz(n=, ops= r)rrr{rzrrrPr r rrs  zRandAugment.__repr__)rNrr r r rrys  rycCst}d}d}d}|d}|ddksJ|dd}|D]x}|d r3t|dd} |dur2| }qtd |} t| dkr@q| dd\} } | d kr_t| } | d krXtd } |d| q| dkrl|dt| q| dkrwt | rvd}q| dkrt| }q| dkrt| }q| dkrt| }qJdt |trt ||d}n |dur|rt nt }d} t |trt|\}} t||||d}t||| dS)a; Create a RandAugment transform Args: config_str (str): String defining configuration of random augmentation. Consists of multiple sections separated by dashes ('-'). The first section defines the specific variant of rand augment (currently only 'rand'). The remaining sections, not order specific determine * 'm' - integer magnitude of rand augment * 'n' - integer num layers (number of transform ops selected per image) * 'p' - float probability of applying each layer (default 0.5) * 'mstd' - float std deviation of magnitude noise applied, or uniform sampling if infinity (or > 100) * 'mmax' - set upper bound for magnitude to something other than default of _LEVEL_DENOM (10) * 'inc' - integer (bool), use augmentations that increase in severity with magnitude (default: 0) * 't' - str name of transform set to use hparams (dict): Other hparams (kwargs) for the RandAugmentation scheme Returns: A PyTorch compatible Transform Examples:: 'rand-m9-n3-mstd0.5' results in RandAugment with magnitude 9, num_layers 3, magnitude_std 0.5 'rand-mstd1-tweights' results in mag std 1.0, weighted transforms, default mag of 10 and num_layers 2 rFrrVrrandr,NtrWrXdrrmmaxrincTmnrTz"Unknown RandAugment config section)rrrw)r|)rrY startswithrQrZrkrr[rboolrrsrprqrrjrxry)rUrrcrr{rrrr\rIr`r^r_rXr|ra_opsr r rrand_augment_transformsX            r) rrrrrrrrrrrrrcs$pt|pt}fdd|DS)Ncsg|] }t|ddqS)rrtr!rurrr rrfs zaugmix_ops..)r_AUGMIX_TRANSFORMS)rrrcr rr augmix_ops_s  rc@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 rrFcCs"||_||_||_||_||_dSrD)rzalphawidthdepthblended)rrzrrrrr r rrts  zAugMixAugment.__init__cCs\||}d}g}|dddD]}||}|d|9}||qtj|dddtjdS)Nrrr,dtype)rhrfrgfloat32)rwsrcumprwsrTrr r r_calc_blended_weights{s  z#AugMixAugment._calc_blended_weightsc Csx|}|||}|D]-}|jdkr|jntjdd}tjj|j|dd}|} |D]} | | } q+t || |}q |S)Nrr,rTr}) rrrrfr!randintr"rzrblend) rr2mixing_weightsrimg_origrrTrrzimg_augrPr r r_apply_blendeds  zAugMixAugment._apply_blendedc Cs|jd|jdt|f}tj|tjd}|D]2}|jdkr#|jntjdd}tjj |j |dd}|} |D]} | | } q8||tj | tjd7}qtj |dd|dt |tj}t |||S) Nrr,rrTrgo@)out)r/rkgetbandsrfzerosrrr!rr"rzasarrayclipr fromarrayastypeuint8r) rr2rr img_shapemixedmwrrzrrPr r r _apply_basics zAugMixAugment._apply_basiccCs^ttj|jg|j}ttj|j|j}|jr&||||}|S| |||}|SrD) rfrr! dirichletrrbetarrr)rr2rrrr r rrszAugMixAugment.__call__cCsL|jjd|jd|jd|jd}|jD] }|d|7}q|d7}|S)Nz(alpha=z, width=z, depth=r~rr)rrrrrrzrr r rrs & zAugMixAugment.__repr__N)rrrF) rrr__doc__rrrrrrr r r rrns   rc Csd}d}d}d}d}|d}|ddksJ|dd }|D]T}td |} t| d kr.q| d d \} } | d krC|d t| q| dkrLt| }q| dkrUt| }q| dkr^t| }q| dkrgt| }q| dkrpt| }qJd|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 specific 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 rrrFrVraugmixr,NrWrrXrrrTrJrGrHzUnknown AugMix config sectionr)rr)rrrr) rYrZrkr[rrrrr) rUrrrrrrr\rIr^r_r`rzr r raugment_and_mix_transforms:          r)r)rCrT)rHNrD)T)rrNN)NN)rNN)arr!rNrZ functoolsrtypingrrrrPILrrr r r numpyrfr __version__rYr)rrrrhasattrrBILINEARBICUBICrrr%r*r4r6r;r=r?r@rMr]r_r`rarprrrxr{r}rrrrrrrrrrrrrrrrrrrrrr*r-rErFrGrMrNrQrbrqrprornrmrjrsrrrxryrrrrrr r r rs "                  2!"!! #     T  I