NgފdZddlZddlZddlZddlmZddlmZmZm Z m Z ddl m Z m Z mZmZmZddl Z ddlZede jdddDZd Zd Zed e Zee d r%e jje jjfZe jjZ ne je jfZe jZ dZ!dZ"dZ#dZ$dZ%dZ&dZ'dZ(dZ)dZ*dZ+dZ,dZ-dtdZ.dZ/dZ0dZ1d Z2d!Z3d"Z4d#Z5d$Z6d%Z7d&Z8d'Z9d(Z:dud,Z;d-Zd0Z?d1Z@d2ZAd3ZBd4ZCd5ZDid6dd7dd8dd9e8d:e?d;e@deCd?eDd@e9dAe:dBe9dCe:dDe9dEe:dFe9e:ee>ee;dGd*Hee;dIdJHe;dK ZEid6e*d7e,d8e+d9e)d:e/d;e/de-d?e.d@e1dAe1dBe0dCe0dDe2dEe2dFe3e3e#e$e'e(e%e&e6e4e5dK ZFGdLdMZGdNZHdOZIdPZJdQZKdRZLdvdTZMGdUdVZNdwdWeOdXe efdYZPgdZZQgd[ZRgd\ZSd]d]d]d^ddd_d_d_d_d_d_d_d_d`ZTd^ddd_d_dadadadbdbdbdbdbdbdbdcZUddefdeZVdxdfeOfdgZW dydie eXeYfdjeYdXe edde e eeffdkZZGdldmZ[ dzdWeOdXe edde e eOeeffdnZ\gdoZ] d{die eXeYfdXe edde e eOeeffdpZ^GdqdrZ_dwdWeOdXe efdsZ`dS)|a AutoAugment, RandAugment, AugMix, and 3-Augment for PyTorch This code implements the searched ImageNet policies with various tweaks and improvements and does not include any of the search code. AA and RA Implementation adapted from: https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/autoaugment.py AugMix adapted from: https://github.com/google-research/augmix 3-Augment based on: https://github.com/facebookresearch/deit/blob/main/README_revenge.md Papers: AutoAugment: Learning Augmentation Policies from Data - https://arxiv.org/abs/1805.09501 Learning Data Augmentation Strategies for Object Detection - https://arxiv.org/abs/1906.11172 RandAugment: Practical automated data augmentation... - https://arxiv.org/abs/1909.13719 AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty - https://arxiv.org/abs/1912.02781 3-Augment: DeiT III: Revenge of the ViT - https://arxiv.org/abs/2204.07118 Hacked together by / Copyright 2019, Ross Wightman N)partial)DictListOptionalUnion)ImageImageOps ImageEnhance ImageChops ImageFilterc,g|]}t|S)int).0xs R/var/www/html/ai-engine/env/lib/python3.11/site-packages/timm/data/auto_augment.py r"sAAAQ#a&&AAA.)rrg$@)translate_constimg_mean Resamplingc|dt}t|ttfrt j|S|S)Nresample)pop_DEFAULT_INTERPOLATION isinstancelisttuplerandomchoice)kwargs interpolations r_interpolationr'5sBJJz+ABBM-$//,}]+++ rcrd|vr tdkr|dt||d<dS)N fillcolorrr)_PIL_VERrr')r%s r_check_args_tfr-<sAfF!2!2 ;'//F:rc jt||j|jtjd|ddddffi|SNrr- transformsizerAFFINEimgfactorr%s rshear_xr8Bs?6 3=5.transformtsA!' 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H H H2 6 6 6 62 6 6 6 H H Hrrrpolicypcs` rauto_augment_policy_v0rLs 12 01 ./ 34 34 01 67 /0 01 /0 12 01  89 01 12 67 12 ./ ./ 34 78 45  56 34 ./3F6 I H H H H H HB Ircddgddgddgddgd d gd d gd dgddgddgddgddgddgddgddgddgddgd d!gd"d#gd$d%gd&d'gd(d)gd*d+gd,d-gdd.gd/d0gg}fd1|D}|S)2Nr r r rrrrrrrrrrrrr r!r"r#r$r%r&r'rrr(r)r*r+r,r-r.r/r0r1r2r3r4r5r6r7r8r9r:r;)rr rr<r=r>r?c,g|]}fd|DS)c(g|]}t|diSrBrCrDs rrz6auto_augment_policy_v0r...rErrrFs rrz+auto_augment_policy_v0r..rHrrrIs` rauto_augment_policy_v0rrQs 12 01 ./ 34 34 01 67 /0 01 /0 12 01 (*BC 01 12 67 12 ./ ./ 34 78 45 (*?@ 34 ./3F6 I H H H H H HB Ircddgddgddgddgd d gd d gd dgddgddgddgd dgddgddgddgddgd dgddgd d!gd"d#gddgd d gddgddgddgddgg}fd$|D}|S)%N)rrr~rrrrrr+rrr+rr)rrr)rrr(rr,rrrrr r~rrrrrr)rr r+r!rrrr*rrr~)rrr(rrrrrrrrrrrKrrrrrrr~r3rrr~rrrrr r~rr rrrrrrr~rrr+r$c,g|]}fd|DS)c(g|]}t|diSrBrCrDs rrz;auto_augment_policy_original...rErrrFs rrz0auto_augment_policy_original..rHrrrIs` rauto_augment_policy_originalrks '(:; 78 34 &(EF 34 12 34 &(<= 12 <= ./ 12 34 12 01 ./ 01  23 12 01 34 78 12 01 343F6 I H H H H H HB Ircddgddgddgddgd d gd d gd dgddgddgddgd dgddgddgddgddgd dgddgd d!gd"d#gddgd d gddgddgddgddgg}fd$|D}|S)%N)rrr~rSrTrUrr)rrr)rrr(rr,rVrWrXrY)rr r+r!rZr*r[rNr\r]r^r_r`rar3rbrcrdrerfrgrhr$c,g|]}fd|DS)c(g|]}t|diSrBrCrDs rrz..rErrrFs rrz1auto_augment_policy_originalr..rHrrrIs` rauto_augment_policy_originalrros )*<= 78 34 (*IJ 34 12 34 (*>? 12 >? ./ 12 34 12 01 ./ 01  23 12 01 34 78 12 01 343F6 I H H H H H HB Irc4dgdgdgg}fd|D}|S)N)rrr+)rrr)rrrc,g|]}fd|DS)c(g|]}t|diSrBrCrDs rrz5auto_augment_policy_3a..."rErrrFs rrz*auto_augment_policy_3a.."rHrrrIs` rauto_augment_policy_3arss=  ! &'F I H H H H H HB Irv0c|pt}|dkrt|S|dkrt|S|dkrt|S|dkrt |S|dkrt |SJd|)Noriginal originalrrtv0r3aFzUnknown AA policy )rrkrorLrQrs)rrs rauto_augment_policyrz&s))G z+G444 {,W555 t||%g... u}}&w/// t||%g...--t-----rc eZdZdZdZdZdS) AutoAugmentc||_dSrM)rJ)rrJs rrzAutoAugment.__init__7s  rcXtj|j}|D] }||}|SrM)r#r$rJ)rr6 sub_policyops rrzAutoAugment.__call__:s6]4;//   B"S''CC rc|jjdz}|jD].}|dz }|dd|Dz }|dz }/|dz }|S)Nz(policy=z [z, c,g|]}t|Sr)str)rrs rrz(AutoAugment.__repr__..Ds111SWW111r]r)rrrJjoin)rrps rrzAutoAugment.__repr__@si ^ $z 1  A 'MB $))11q11122 2B #IBB c  rNrrrrr|r|5sA rr| config_strrcl|d}|d}|dd}|D]k}tjd|}t|dkr+|dd\}}|dkr$|dt |bJd t || }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 -rr0N(\d.*)rmstdrFz"Unknown AutoAugment config sectionrB)splitrerw setdefaultrrzr|) rrconfig policy_namerRcskeyval aa_policys rauto_augment_transformrJs$  c " "F)K ABBZF  ? ? Xi # # r77Q;; bqb6S &==   c ; ; ; ; >> > > >#KAAAI y ! !!r)rrrrrrrrrrrrrrr)rrrrrrrrrrrrrrr)rrrr(rr0)rrrrrrrrrrrrrrg?g?)rrrrrrrrrrrrrrr transformsctt|\}}tj|}|tj|z }||fSrM)r!zipitemsnparraysum)rprobss r_get_weighted_transformsrsOS*"2"2"4"4566J HUOOE BF5MM !E u rrcp|dkrtS|dkrtS|dkrtS|rtntS)Nweights3awry)_RAND_WEIGHTED_0_RAND_WEIGHTED_3A_RAND_3A_RAND_INCREASING_TRANSFORMS_RAND_TRANSFORMS)r increasings rrand_augment_choicesrsB y u}}   t||*4 J & &:JJrrrrcLpt|pt}fd|DS)Nc6g|]}t|S)rrrrC)rrrrrs rrz$rand_augment_ops..sG W W WCG  49g ? ? ? W W Wr)rrrrrrs``` rrand_augment_opsrs\ ))G//J W W W W W WKU W W WWrc"eZdZddZdZdZdS) RandAugmentrNc0||_||_||_dSrM)ops num_layerschoice_weights)rrrrs rrzRandAugment.__init__s$,rctj|j|j|jdu|j}|D] }||}|S)N)replacer)rr#r$rrr)rr6rrs rrzRandAugment.__call__s^i H O'4/!      B"S''CC rcd|jjd|jdz}|jD] }|d|z } |dz }|S)Nz(n=, ops= r)rrrrrrrs rrzRandAugment.__repr__sP ^ $'DT_'D'D'D D(  B +++ BB c  r)rNrrrrrrsF----   rrct}d}d}d}|d}|ddksJ|dd}|D]:}|d rt|dd} || }4t jd |} t | dkr]| dd\} } | d kr;t | } | d krt d } |d| | dkr$|dt| | dkrt| rd}| dkrt| }| dkrt| }| dkrt | }2Jdt|trt||}n||rtnt}d} t|trt|\}} t!||||}t#||| S)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 rFrrrrandr0NtrrdrrmmaxrincTmnrz"Unknown RandAugment config section)rr)r)rr startswithrrrwrrrboolr rrrrrrr)rrrrrrrrrRrrrrrra_opss rrand_augment_transformrs36IJJ D   c " "F !9     ABBZF  C C <<   Cae**C! )Q''B2ww{{"1"vHCf}}Szz#:: <T00DT N*d##J%=j%I%I" N  gZd e e eF vz. I I IIr) rrrrrrrrrrrrrcHpt|pt}fd|DS)Nc6g|]}t|dS)rrrC)rrrrs rrzaugmix_ops..csF             r)r_AUGMIX_TRANSFORMS)rrrs`` r augmix_opsr\sN ))G11J          rc8eZdZdZd dZdZdZd Zd Zd Z d S) 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 rrFcL||_||_||_||_||_dSrM)ralphawidthdepthblended)rrrrrrs rrzAugMixAugment.__init__qs)    rc||z}d}g}|dddD]$}||z }|d|z z}||%tj|dddtjS)Nrrr0dtype)rtrrfloat32)rwsrcumprwsr]rs r_calc_blended_weightsz#AugMixAugment._calc_blended_weightsxs{ !VDDbD  AHE QY D JJu    xDDbD 4444rcf|}|||}|D]}|jdkr|jntjdd}tj|j|d}|} |D] } | | } tj || |}|S)Nrr0rTr) rrrrr#randintr$rrblend) rr6mixing_weightsrimg_origrr]rrimg_augrs r_apply_blendedzAugMixAugment._apply_blendeds 88::  ' ' : : / /A"&*q..DJJbi6G6G16M6ME)""48UD"AACG & &"W+++c7A..CC rc|jd|jdt|f}tj|tj}|D]}|jdkr|jntjdd}tj |j |d}|} |D] } | | } ||tj | tjzz }tj |dd|tj|tj}tj|||S) Nrr0rrTrgo@)out)r3rwgetbandsrzerosrrr#rr$rasarrayclipr fromarrayastypeuint8r) rr6rr img_shapemixedmwrrrrs r _apply_basiczAugMixAugment._apply_basics'HQK!c#,,...A.AA "*555  @ @B"&*q..DJJbi6G6G16M6ME)""48UD"AACG & &"W++ R"*WBJ???? ?EE q$E**** RX 6 677{3q)))rchtjtj|jg|jz}tjtj|j|j}|jr||||}n| |||}|SrM) rrr# dirichletrrbetarrr)rr6rrrs rrzAugMixAugment.__call__sBI$7$7 tz8Q$R$RSS Jry~~dj$*== > > < >''^Q??EE%%c>1==E rc|jjd|jd|jd|jdz}|jD] }|d|z } |dz }|S)Nz(alpha=z, width=z, depth=rrr)rrrrrrrs rrzAugMixAugment.__repr__sf ^ $'k'k'kTZ'k'kY]Yc'k'k'k k(  B +++ BB c  rN)rrrF) rrr__doc__rrrrrrrrrrrks} 555 ***"rrcd}d}d}d}d}|d}|ddksJ|dd }|D]}tjd |} t| d kr+| d d \} } | d kr$|d t | b| dkrt | }x| dkrt | }| dkrt | }| dkrt | }| dkrt | }Jd|d t dt||} t| ||||S)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 rrrFrraugmixr0Nrrrrrr]rSrPrQzUnknown AugMix config sectionr)rr)rrrr) rrrwrrrrrr) rrrrrrrrrRrrrrs raugment_and_mix_transformrs&I E E EG   c " "F !9 ABBZF :: Xi # # r77Q;; bqb6S &==   c ; ; ; ; CZZCII CZZHHEE CZZHHEE CZZ#JJEE CZZ3iiGG 99 9 9 9 e 555 y' : : :C EeW U U UUr)r)rKrT)rtNrM)T)rrNN)NN)rNN)arr#rWr functoolsrtypingrrrrPILrr r r r numpyrr" __version__rr,rrrrhasattrrBILINEARBICUBICrrr'r-r8r:r@rBrErGrVrfrhrjrlr|rrrrrrrrrrrrrrrrrrrrrrrrrrLrQrkrorsrzr|rrrrrrrrrrrrrrrrrrrrrrsY , ............FFFFFFFFFFFFFF  5AA#/"7"7"<"&&&   """***     111 666333888777 333 ... /// ,,,;;;000888 === 888 D d "  ( =9&;- "7%: '!"<#$&%&<!!--00'.SIIIG0#sKKK,9 @M f f    9 (< Uu(* J!"#$%!!$$!*7 >////////dBDBB . . . .* " "s "Xd^ " " " "F((   *&KKsKKKK(*"&26 W We$ W W$ WU4:./ W W W W8#'7;NJNJNJ$NJU3d?34NJNJNJNJb$(*"&7;  e$ $ U3d?34    FFFFFFFFR1V1V#1V1V1V1V1V1V1Vr