Ng#dZ ddlmcmZddlZdZdZejj  dd Z d Z d Z d Z d ZedfdZedfdZddedfdZGddZdS)z Tensorflow Preprocessing Adapter Allows use of Tensorflow preprocessing pipeline in PyTorch Transform Copyright of original Tensorflow code below. Hacked together by / Copyright 2020 Ross Wightman N 皙??gHzG?g??dc tj|d||g5tj|}tj||||||d}|\} } } tj| \} } } tj| \}}} tj| | ||g}tj||d}|cdddS#1swxYwYdS)aGenerates cropped_image using one of the bboxes randomly distorted. See `tf.image.sample_distorted_bounding_box` for more documentation. Args: image_bytes: `Tensor` of binary image data. bbox: `Tensor` of bounding boxes arranged `[1, num_boxes, coords]` where each coordinate is [0, 1) and the coordinates are arranged as `[ymin, xmin, ymax, xmax]`. If num_boxes is 0 then use the whole image. min_object_covered: An optional `float`. Defaults to `0.1`. The cropped area of the image must contain at least this fraction of any bounding box supplied. aspect_ratio_range: An optional list of `float`s. The cropped area of the image must have an aspect ratio = width / height within this range. area_range: An optional list of `float`s. The cropped area of the image must contain a fraction of the supplied image within in this range. max_attempts: An optional `int`. Number of attempts at generating a cropped region of the image of the specified constraints. After `max_attempts` failures, return the entire image. scope: Optional `str` for name scope. Returns: cropped image `Tensor` distorted_bounding_box_cropT)bounding_boxesmin_object_coveredaspect_ratio_range area_range max_attemptsuse_image_if_no_bounding_boxeschannelsN)tf name_scopeimageextract_jpeg_shapesample_distorted_bounding_boxunstackstackdecode_and_crop_jpeg) image_bytesbboxrrrrscopeshaper bbox_begin bbox_size_offset_yoffset_x target_height target_width crop_windowrs V/var/www/html/ai-engine/env/lib/python3.11/site-packages/timm/data/tf_preprocessing.pyr r !s?> u;k4=P Q Q++K88(*(N(N 11!%+/)O)1)1%$A Iq!# : 6 6(A)+I)>)>& |Qh(M<PQQ --k;QR-SS%sB7CC!$C!ctj||}tj|tj}tjtj||S)z0At least `x` of `a` and `b` `Tensors` are equal.)requalcastint32 greater_equal reduce_sum)abxmatchs r*_at_least_x_are_equalr5UsA HQNNE GE28 $ $E BM%00! 4 44c Ftjgdtjgd}t|ddddd tj}t |tjd }tj|fd fd S) z!Make a random crop of image_size.)r8r r )r9)dtyper!r)rgUUUUUU?)g{Gz?r N)rrrrr rc$tS)N)_decode_and_center_crop)r image_sizesr*z)_decode_and_random_crop..ls' Z@@r6cXtjggdSNr)rrresize)rr? resize_methodsr*r@z)_decode_and_random_crop..ms&*j)A=QQRSTr6) rconstantfloat32r rrr5r!cond)rr?rDroriginal_shapebadrs``` @r*_decode_and_random_croprJ\s ;+++2:YYY O O OD ' ,   EX00==N  C CC G @@@@@TTTTTT V VE Lr6c tj|}|d}|d}tj||tzz tjtj||tjztj}||z dzdz}||z dzdz}tj||||g} tj || d} tj | g||g|d} | S)z=Crops to center of image with padding then scales image_size.rr9rr) rrrr- CROP_PADDINGminimumrFr.rrrC) rr?rDr! image_height image_widthpadded_center_crop_size offset_height offset_widthr)rs r*r>r>rs H ' ' 4 4E8L(K g  \1 2 L+66 C C D  #%<<AaGM #::a?AEL(M<35LNOOK H ) )+{Q ) O OE HOOUGj*%=} M Ma PE Lr6cDtj|}|S)zRandom horizontal image flip.)rrrandom_flip_left_right)rs r*_fliprVs H + +E 2 2E Lr6bicubiccL|dkrtjjjntjjj}t |||}t |}tj|||dg}tj||r tj n tj }|SaAPreprocesses the given image for evaluation. Args: image_bytes: `Tensor` representing an image binary of arbitrary size. use_bfloat16: `bool` for whether to use bfloat16. image_size: image size. interpolation: image interpolation method Returns: A preprocessed image `Tensor`. rWr)r;) rr ResizeMethodBICUBICBILINEARrJrVreshapeconvert_image_dtypebfloat16rFr use_bfloat16r? interpolationrDrs r*preprocess_for_trainrcs6Ci5O5OBH)11UWU]UjUsM #K] K KE %LLE Juz:q9 : :E H ( ( L@R[[bj ) B BE Lr6c.|dkrtjjjntjjj}t |||}tj|||dg}tj||r tjn tj }|SrY) rrrZr[r\r>r]r^r_rFr`s r*preprocess_for_evalres6Ci5O5OBH)11UWU]UjUsM #K] K KE Juz:q9 : :E H ( ( L@R[[bj ) B BE Lr6FcN|rt||||St||||S)aPreprocesses the given image. Args: image_bytes: `Tensor` representing an image binary of arbitrary size. is_training: `bool` for whether the preprocessing is for training. use_bfloat16: `bool` for whether to use bfloat16. image_size: image size. interpolation: image interpolation method Returns: A preprocessed image `Tensor` with value range of [0, 255]. )rcre)r is_trainingrar?rbs r*preprocess_imagerhs5"Y#Kz=YYY"; j-XXXr6c"eZdZddZdZdZdS) TfPreprocessTransformFrrWc||_t|tr|dn||_||_d|_||_d|_dSrB) rg isinstancetuplesizerb _image_bytes_build_tf_graph process_imagesess)selfrgrnrbs r*__init__zTfPreprocessTransform.__init__sW&)$66@DGGD * !1133 r6ctjd5tjgtj|_t |j|jd|j|j}dddn #1swxYwY|S)Nz/cpu:0)r!r;F) rdevice placeholderstringrorhrgrnrb)rsimgs r*rpz%TfPreprocessTransform._build_tf_graphs Yx  [ [ "i!!!D #!4#3UDItGY[[C  [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ sA A..A25A2c|jtj|_|j|j|j|i}|ddtj }|j dkrtj |d}tj |d}|S)N) feed_dictrr)axisrL)rrrSessionrunrqroroundclipastypenpuint8ndim expand_dimsrollaxis)rsrrys r*__call__zTfPreprocessTransform.__call__s 9  DIimmD.4;Lk:Zm[[iikkq#&&--bh77 8a<<.2...Ck#q!! r6N)FrrW)__name__ __module__ __qualname__rtrprr6r*rjrjsFr6rj)rrrr N)__doc__tensorflow.compat.v1compatv1rnumpyr IMAGE_SIZErMdisable_eager_executionr r5rJr>rVrcrerhrjrr6r*rsP.*!!!!!!!!!    $$&&&473?+6-0&* 1111h555,* @JYb*?IXa*"'"' *#, YYYY.r6