ΑiYlSrSSKrSSKrSSKJr SSKJrJr SSKJ r J r SSK J r J r SSKJr /rS rS rS rS rS rSrSrSrSrSr\R6"\S5r\R6"\S5rSSjr\ "SSSSS9\SSS4Sj5r\ "SSSSS9\SSS4Sj5r \ "SSSSS9\SS4Sj5r!Sr"g) a This module will download dataset from http://www.robots.ox.ac.uk/~vgg/data/flowers/102/index.html and parse train/test dataset into paddle reader creators. This set contains images of flowers belonging to 102 different categories. The images were acquired by searching the web and taking pictures. There are a minimum of 40 images for each category. The database was used in: Nilsback, M-E. and Zisserman, A. Automated flower classification over a large number of classes.Proceedings of the Indian Conference on Computer Vision, Graphics and Image Processing (2008) http://www.robots.ox.ac.uk/~vgg/publications/papers/nilsback08.{pdf,ps.gz}. N) cpu_count)load_image_bytessimple_transform) map_readers xmap_readers) deprecated try_import)downloadz8http://paddlemodels.bj.bcebos.com/flowers/102flowers.tgzz9http://paddlemodels.bj.bcebos.com/flowers/imagelabels.matz3http://paddlemodels.bj.bcebos.com/flowers/setid.mat 52808999861908f626f3c1f4e79d11fa e0620be6f572b9609742df49c70aed4d a5357ecc9cb78c4bef273ce3793fc85ctstidtrnidvalidcUup#[U5n[USSU/SQS9nUR5RS5U4$)z: map image bytes data to type needed by model input layer )g\(Y@gR1]@gQ^@)meanfloat32)rrflattenastype)is_trainsampleimglabels V/var/www/html/banglarbhumi/venv/lib/python3.13/site-packages/paddle/dataset/flowers.pydefault_mapperr:sIJC 3 C  S#x&> C ;;=   *E 11TFc ~^^^^UUUU4SjnU(a[XH[S[55U5$[XH5$)a) 1. read images from tar file and merge images into batch files in 102flowers.tgz_batch/ 2. get a reader to read sample from batch file :param data_file: downloaded data file :type data_file: string :param label_file: downloaded label file :type label_file: string :param setid_file: downloaded setid file containing information about how to split dataset :type setid_file: string :param dataset_name: data set name (tstid|trnid|valid) :type dataset_name: string :param mapper: a function to map image bytes data to type needed by model input layer :type mapper: callable :param buffered_size: the size of buffer used to process images :type buffered_size: int :param cycle: whether to cycle through the dataset :type cycle: bool :return: data reader :rtype: callable c3># [S5nURT5SSnURT5T Sn0nUHnSUSS3nXS- X5'M [R"T 5nUR 5nSnUHTn U R U;dMUR U 5R5n X9R n U [U 5S- 4v MV g7f)Nzscipy.iolabelsrz jpg/image_05z.jpgr ) r loadmattarfileopen getmembersname extractfilereadint)scior#indexes img2labelirtfmemsfile_idmemimager data_file dataset_name label_file setid_files rreaderreader_creator..readerms*%j)(3A6,,z*<8; AqfD)C#E]IN\\) $}}Cxx9$s+002!((+SZ!^++ s BCAC)rminrr) r6r8r9r7mapper buffered_sizeuse_xmapcycler:s ```` rreader_creatorrBJs6F,,(FC9;,?OO6**rz2.0.0zpaddle.vision.datasets.Flowersz>Please use new dataset API which supports paddle.io.DataLoader)since update_tolevelreasonc [[[S[5[[S[ 5[[ S[5[UUUUS9$)a Create flowers training set reader. It returns a reader, each sample in the reader is image pixels in [0, 1] and label in [1, 102] translated from original color image by steps: 1. resize to 256*256 2. random crop to 224*224 3. flatten :param mapper: a function to map sample. :type mapper: callable :param buffered_size: the size of buffer used to process images :type buffered_size: int :param cycle: whether to cycle through the dataset :type cycle: bool :return: train data reader :rtype: callable flowersrA) rBr DATA_URLDATA_MD5 LABEL_URL LABEL_MD5 SETID_URL SETID_MD5 TRAIN_FLAGr>r?r@rAs rtrainrRsD0 9h/Iy1Iy1  rc [[[S[5[[S[ 5[[ S[5[UUUUS9$)a Create flowers test set reader. It returns a reader, each sample in the reader is image pixels in [0, 1] and label in [1, 102] translated from original color image by steps: 1. resize to 256*256 2. random crop to 224*224 3. flatten :param mapper: a function to map sample. :type mapper: callable :param buffered_size: the size of buffer used to process images :type buffered_size: int :param cycle: whether to cycle through the dataset :type cycle: bool :return: test data reader :rtype: callable rHrI) rBr rJrKrLrMrNrO TEST_FLAGrQs rtestrUsD0 9h/Iy1Iy1  rc [[[S[5[[S[ 5[[ S[5[UUU5$)a Create flowers validation set reader. It returns a reader, each sample in the reader is image pixels in [0, 1] and label in [1, 102] translated from original color image by steps: 1. resize to 256*256 2. random crop to 224*224 3. flatten :param mapper: a function to map sample. :type mapper: callable :param buffered_size: the size of buffer used to process images :type buffered_size: int :return: test data reader :rtype: callable rH) rBr rJrKrLrMrNrO VALID_FLAG)r>r?r@s rrrsA, 9h/Iy1Iy1 rc[[S[5 [[S[5 [[ S[ 5 g)NrH)r rJrKrLrMrNrOrrfetchrZs& Xy(+ Y 9- Y 9-r)r TF)#__doc__ functoolsr&multiprocessingrpaddle.dataset.imagerr paddle.readerrr paddle.utilsrr commonr __all__rJrLrNrKrMrOrPrTrWrpartial train_mapper test_mapperrBrRrUrrZrYrrrfs$$%C3/  E G A - . .     2  6 6   :+z . K  TD  < . K  4$e  < . K  D4  6.r