NgiJ7dZddlZddlmZmZmZddlZddlmZddl m Z m Z m Z ddl mZmZmZddlmZmZmZmZmZmZmZmZmZmZddlmZd d e e d d fd eeeeeffdedee dfdee dfde!de!f dZ"d dddddddd dddde e ddddd d d fd eeeeeffdeee e fdeee e fdeede de d ee ee dffd!ee d"e!d#e d$e d%eededee dfdee dfd&e d'ed(ed)ede!de!d*e!f,d+Z#d dddd e e d d f d eeeeeffd,ee d-eed.eededee dfdee dfde!de!fd/Z$d d d ddddddddddd e e dddddddd d d d fd0eeeeefeeeeffd1e!d2e!deedeee e fdeee e fde de d ee ee dffd!ee d#e d$e d%eededee dfdee dfd&e d'ed(ed)ed,ee d-eed.eed3e!de!de!d*e!f6d4Z%dS)5z Transforms Factory Factory methods for building image transforms for use with TIMM (PyTorch Image Models) Hacked together by / Copyright 2019, Ross Wightman N)OptionalTupleUnion) transforms)IMAGENET_DEFAULT_MEANIMAGENET_DEFAULT_STDDEFAULT_CROP_PCT)rand_augment_transformaugment_and_mix_transformauto_augment_transform) str_to_interp_modestr_to_pil_interp!RandomResizedCropAndInterpolationResizeKeepRatioCenterCropOrPadRandomCropOrPad TrimBorderToNumpy MaybeToTensorMaybePILToTensor) RandomErasingbilinearFTimg_size interpolationmean.stduse_prefetcher normalizec|dkrd}tj|t|tj|g}|r|t gz }n`|s|t gz }nK|t tjtj |tj |gz }tj |S)a No-augmentation image transforms for training. Args: img_size: Target image size. interpolation: Image interpolation mode. mean: Image normalization mean. std: Image normalization standard deviation. use_prefetcher: Prefetcher enabled. Do not convert image to tensor or normalize. normalize: Normalization tensor output w/ provided mean/std (if prefetcher not used). Returns: randomrrrr) rResizer CenterCroprrr NormalizetorchtensorCompose)rrrrrrtfls X/var/www/html/ai-engine/env/lib/python3.11/site-packages/timm/data/transforms_factory.pytransforms_noaug_trainr,s*  " (2D]2S2STTTh'' C    {     ""## OO  \$''L%%       c " ""?g?r!constscaleratiotrain_crop_modehflipvflip color_jittercolor_jitter_probforce_color_jittergrayscale_probgaussian_blur_prob auto_augmentre_probre_modere_count re_num_splitsseparatec `|pd}|dvsJ|dvrat|pd}t|pd}t|| d|dd||d krt|d nt|d g}n6t|pd }t|pd }t |||| g}|dkr|t j|gz }|dkr|t j|gz }g}d}| r t| tsJ|pd| v }t|ttfrt|}n|}tt|dztd| D}| r| dkrt| |d<| dr|t!| |gz }nC| drd|d<|t#| |gz }n|t%| |gz }||st|ttfrt'|dvsJnt)|fdz}|(|t jt j|g|gz }n|t j|gz }| r|t j| gz }| r.|t jt jd g| gz }g}|r|t3gz }n~|s|t5gz }ni|t7t jt;j| t;j|!gz }|dkr|t?||||d"#gz }|r;t j |t j |t j |fSt j ||z|zS)$a ImageNet-oriented image transforms for training. Args: img_size: Target image size. train_crop_mode: Training random crop mode ('rrc', 'rkrc', 'rkrr'). scale: Random resize scale range (crop area, < 1.0 => zoom in). ratio: Random aspect ratio range (crop ratio for RRC, ratio adjustment factor for RKR). hflip: Horizontal flip probability. vflip: Vertical flip probability. color_jitter: Random color jitter component factors (brightness, contrast, saturation, hue). Scalar is applied as (scalar,) * 3 (no hue). color_jitter_prob: Apply color jitter with this probability if not None (for SimlCLR-like aug). force_color_jitter: Force color jitter where it is normally disabled (ie with RandAugment on). grayscale_prob: Probability of converting image to grayscale (for SimCLR-like aug). gaussian_blur_prob: Probability of applying gaussian blur (for SimCLR-like aug). auto_augment: Auto augment configuration string (see auto_augment.py). interpolation: Image interpolation mode. mean: Image normalization mean. std: Image normalization standard deviation. re_prob: Random erasing probability. re_mode: Random erasing fill mode. re_count: Number of random erasing regions. re_num_splits: Control split of random erasing across batch size. use_prefetcher: Prefetcher enabled. Do not convert image to tensor or normalize. normalize: Normalize tensor output w/ provided mean/std (if prefetcher not used). separate: Output transforms in 3-stage tuple. Returns: If separate==True, the transforms are returned as a tuple of 3 separate transforms for use in a mixing dataset that passes * all data through the first (primary) transform, called the 'clean' data * a portion of the data through the secondary transform * normalizes and converts the branches above with the third, final transform rrc>rCrkrcrkrr)rDrE)g??)g?grq?r.T)rrandom_scale_probrandom_scale_rangerandom_scale_arearandom_aspect_probrandom_aspect_rangerDreflect) padding_mode)g{Gz?rF)g?gUUUUUU?)r2r3rr/)pF3ag?c Ng|]"}tdtd|z#S)minround).0xs r+ z-transforms_imagenet_train..s,CCCCU37^^44CCCr-)translate_constimg_meanr!rrandaugmixg333333? translate_pctN)r]) kernel_sizer#cpu)mode max_count num_splitsdevice)!tuplerrrrrRandomHorizontalFlipRandomVerticalFlip isinstancestrlistrSdictintr startswithr r r lenfloat RandomApply ColorJitterRandomGrayscale GaussianBlurrrrr&r'r(rr))rr2r3r4r5r6r7r8r9r:r;r<rrrr=r>r?r@rrrA primary_tfl secondary_tfldisable_color_jitter img_size_min aa_params final_tfls r+transforms_imagenet_trainr{@st&.O 5 5 5 5 5***e*{++e*{++ +"%#("&#&$)   &(( H9 = = = = H9 = = =  e*{++e1122 -+      rzz 7%@@@AA  rzz 5>>>?? M O,,,,,,%7$N$,:NO h . . $x==LL#L t 344CCdCCCDD     J]h66):=)I)IIo &  " "6 * * O 4\9MMN NMM  $ $X . . O),Io & 7 iPPQ QMM 4\9MMN NM(< lT5M 2 2 6|$$.....",//1A5L  ( &". =(( MM j4lCD DMH*4~FFFGG    "+;;;$%     Igii[ &(())  OO  \$''L%%      R<<  &,  IK!+.. 0B=0Q0QS]SefoSpSppp!+ "= "IJJJr-crop_pct crop_modecrop_border_pixelsc ptt|ttfr1t |dksJtfd|D} nt j|z } | | f} g} |r| t|gz } |dkr;| tj | t|tj |gz } n|dkr3d|D} | t| |dt|| gz } nk| d | d kr.| tj | d t|gz } n| t| gz } | tj |gz } |r| tgz } n`|s| tgz } nK| t!tjt%j|t%j| gz } tj| S) a ImageNet-oriented image transform for evaluation and inference. Args: img_size: Target image size. crop_pct: Crop percentage. Defaults to 0.875 when None. crop_mode: Crop mode. One of ['squash', 'border', 'center']. Defaults to 'center' when None. crop_border_pixels: Trim a border of specified # pixels around edge of original image. interpolation: Image interpolation mode. mean: Image normalization mean. std: Image normalization standard deviation. use_prefetcher: Prefetcher enabled. Do not convert image to tensor or normalize. normalize: Normalize tensor output w/ provided mean/std (if prefetcher not used). Returns: Composed transform pipeline c>g|]}tj|z S)mathfloor)rUrVr|s r+rWz,transforms_imagenet_eval..s'GGGDJq8|44GGGr-squashr"borderc2g|]}td|zSrQ)rT)rUvs r+rWz,transforms_imagenet_eval..*s"---1cAg---r-rF)rlongest)fillrr1r#)r rirfrkrorrrrr$r r%rrrrrr&r'r(r)) rr|r}r~rrrrr scale_sizer*rs ` r+transforms_imagenet_evalrsE6++H(UDM**.8}}!!!!GGGGhGGGHH Z8 344  *- C0 -..//H  j8J=8Y8Y Z Z Z  !( + +   h  .---- JmS Q Q Q H4 0 0 0   a=JqM ) ) !*Q-?QR_?`?`aaa CC OJ//0 0C %h//00    {     ""## OO  \$''L%%       c " ""r- input_size is_trainingno_augtf_preprocessingc t|ttfr |dd}n|}|r#|r!|r Jdddlm}|||| }n|r$|r"|r Jdt || ||||}nn|rHt |fid |d |d |d |d |d|d| d| d| d| d| d|d|d|d|d|d|d|d|d|}n$|r Jdt|| ||||||| }|S)a; Args: input_size: Target input size (channels, height, width) tuple or size scalar. is_training: Return training (random) transforms. no_aug: Disable augmentation for training (useful for debug). train_crop_mode: Training random crop mode ('rrc', 'rkrc', 'rkrr'). scale: Random resize scale range (crop area, < 1.0 => zoom in). ratio: Random aspect ratio range (crop ratio for RRC, ratio adjustment factor for RKR). hflip: Horizontal flip probability. vflip: Vertical flip probability. color_jitter: Random color jitter component factors (brightness, contrast, saturation, hue). Scalar is applied as (scalar,) * 3 (no hue). color_jitter_prob: Apply color jitter with this probability if not None (for SimlCLR-like aug). grayscale_prob: Probability of converting image to grayscale (for SimCLR-like aug). gaussian_blur_prob: Probability of applying gaussian blur (for SimCLR-like aug). auto_augment: Auto augment configuration string (see auto_augment.py). interpolation: Image interpolation mode. mean: Image normalization mean. std: Image normalization standard deviation. re_prob: Random erasing probability. re_mode: Random erasing fill mode. re_count: Number of random erasing regions. re_num_splits: Control split of random erasing across batch size. crop_pct: Inference crop percentage (output size / resize size). crop_mode: Inference crop mode. One of ['squash', 'border', 'center']. Defaults to 'center' when None. crop_border_pixels: Inference crop border of specified # pixels around edge of original image. tf_preprocessing: Use TF 1.0 inference preprocessing for testing model ports use_prefetcher: Pre-fetcher enabled. Do not convert image to tensor or normalize. normalize: Normalization tensor output w/ provided mean/std (if prefetcher not used). separate: Output transforms in 3-stage tuple. Returns: Composed transforms or tuple thereof Nz6Separate transforms not supported for TF preprocessingr)TfPreprocessTransform)rsizerz-Cannot perform split augmentation with no_aug)rrrrrr4r2r3r5r6r7r8r:r;r<rrrr=r>r?r@rrrAz>Separate transforms not supported for validation preprocessing)rrrr|r}r~rr)rirfrktimm.data.tf_preprocessingrr,r{r)rrrr4r2r3r5r6r7r8r:r;r<rrrr=r>r?r@r|r}r~rrrrArr transforms r+create_transformrNs6@*udm,,bcc?7N7UUUUUUDDDDDD))#'   . 6.  P P!P P P P.+-# II$ 1 /ee  e  e *\#4"3 .~$6#5*\,mTC   !""#$,m%& .~'($))*"+II0 a a!a a a a0+!##5-#   I r-)&__doc__rtypingrrrr' torchvisionrtimm.data.constantsrrr timm.data.auto_augmentr r r timm.data.transformsr rrrrrrrrrtimm.data.random_erasingrrmrjrpboolr,r{rrrr-r+rsm  )))))))))) """"""]]]]]]]]]]llllllllllllllllllllllllllllllllll22222214'"7!5$ *#*#U38_,-*#*#E3J*#5#:  *#  *#  *#*#*#*#\14/3/3)-8;-1#( "$&&*%"7!5$-tKtKU38_,-tKeUl+,tKeUl+,tK"# tK  tK  tKE5#445tK$E?tK!tKtK"tKsmtKtKE3JtK5#: tK !tK"#tK$%tK&'tK()tK*+tK,-tKtKtKtKp14$(#',0'"7!5$T#T#U38_,-T#5/T#C=T#%SM T#  T# E3J T#5#: T#T#T#T#T#T#pIL!)-/3/38;-1 "$&&*'"7!5$(#',0!&$7~~#uS#Xc3m0DDE~~~"# ~ eUl+, ~ eUl+, ~~~E5#445~$E?~~"~sm~~E3J~ 5#: !~"#~$%~&'~()~*5/+~,C=-~.%SM/~01~23~45~67~~~~~~r-