\;j%ddlZddlZddlZddlmZddlZddlmZddl m Z gZ dZ e dzZ dZe dzZd Zd d d d dZGdde ZGddeZy)N)Image)_check_exists_and_download)Datasetz$https://dataset.bj.bcebos.com/cifar/zcifar-10-python.tar.gz c58f30108f718f92721af3b95e74349azcifar-100-python.tar.gz eb9058c3a382ffc7106e4002c42a8d85 data_batch test_batchtraintest)train10test10train100test100c:eZdZdZ ddZdZdZdZdZy) Cifar10a Implementation of `Cifar-10 `_ dataset, which has 10 categories. Args: data_file (str, optional): Path to data file, can be set None if :attr:`download` is True. Default None, default data path: ~/.cache/paddle/dataset/cifar mode (str, optional): Either train or test mode. Default 'train'. transform (Callable, optional): transform to perform on image, None for no transform. Default: None. download (bool, optional): download dataset automatically if :attr:`data_file` is None. Default True. backend (str, optional): Specifies which type of image to be returned: PIL.Image or numpy.ndarray. Should be one of {'pil', 'cv2'}. If this option is not set, will get backend from :ref:`paddle.vision.get_image_backend `, default backend is 'pil'. Default: None. Returns: :ref:`api_paddle_io_Dataset`. An instance of Cifar10 dataset. Examples: .. code-block:: python >>> # doctest: +TIMEOUT(60) >>> import itertools >>> import paddle.vision.transforms as T >>> from paddle.vision.datasets import Cifar10 >>> cifar10 = Cifar10() >>> print(len(cifar10)) 50000 >>> for i in range(5): # only show first 5 images ... img, label = cifar10[i] ... # do something with img and label ... print(type(img), img.size, label) ... # (32, 32) 6 >>> transform = T.Compose( ... [ ... T.Resize(64), ... T.ToTensor(), ... T.Normalize( ... mean=[0.5, 0.5, 0.5], ... std=[0.5, 0.5, 0.5], ... to_rgb=True, ... ), ... ] ... ) >>> cifar10_test = Cifar10( ... mode="test", ... transform=transform, # apply transform to every image ... backend="cv2", # use OpenCV as image transform backend ... ) >>> print(len(cifar10_test)) 10000 >>> for img, label in itertools.islice(iter(cifar10_test), 5): # only show first 5 images ... # do something with img and label ... print(type(img), img.shape, label) ... # [3, 64, 64] 3 Nc|jdvs Jd||j|_|tjj }|dvrt d|||_|j||_|j1|sJdt||j|jd||_||_ |jtj|_y)N)r r z2mode.lower() should be 'train' or 'test', but got )pilcv2z4Expected backend are one of ['pil', 'cv2'], but got z>data_file is not set and downloading automatically is disabledcifar)lowermodepaddlevisionget_image_backend ValueErrorbackend_init_url_md5_flag data_filerdata_urldata_md5 transform _load_dataget_default_dtypedtype)selfrrr!downloadrs eG:\00. PROJECTS\API\Inventory\templateJSON\kerjaOCR\Lib\site-packages\paddle/vision/datasets/cifar.py__init__zCifar10.__init__jszz|   G@v F G JJL ?mm557G . (FwiP   !" >> ! PO P74==$--(DN# --/ cft|_t|_t|j dz|_y)N10) CIFAR10_URLr CIFAR10_MD5r MODE_FLAG_MAPrflagr%s r'rzCifar10._init_url_md5_flags%# # !$))d"23 r)c g_tjjd5}fd|D}t |}|D]}t j |j|d}|d}|jd|jdd}|Jt||D]"\}}jj||f$ dddy#1swYyxYw) Nr)rc3hK|])}j|jvs|j+ywN)r/name).0 each_itemr%s r' z%Cifar10._load_data..s(019TYY)..5P s22bytes)encodingsdataslabelss fine_labels) datatarfileopenrsortedpickleload extractfilegetzipappend) r%fnamesr5batchr;labelssamplelabels ` r'r"zCifar10._load_datas \\$..s 3q01E5ME AMM$$7'JW~9eii.MN)))%(v%6MFEII$$fe_5&7 4 3 3s B CCc|j|\}}tj|gd}|jgd}|jdk(r$t j |jd}|j|j|}|jdk(r&|tj|jdfS|j|jtj|jdfS)N) rM)rruint8int64) r;npreshape transposerr fromarrayastyper!arrayr$)r%idximagerJs r' __getitem__zCifar10.__getitem__syy~ u 5+. * <<5 OOELL$9:E >> %NN5)E <<5 "((5/0099 9||DJJ'%)?)?)HHHr)c,t|jSr4)lenr;r0s r'__len__zCifar10.__len__s499~r)Nr NTN) __name__ __module__ __qualname____doc__r(rr"rZr]r)r'rr)s4>D  &0P4 6$ Ir)rc4eZdZdZ dfd ZdZxZS)Cifar100a* Implementation of `Cifar-100 `_ dataset, which has 100 categories. Args: data_file (str, optional): path to data file, can be set None if :attr:`download` is True. Default: None, default data path: ~/.cache/paddle/dataset/cifar mode (str, optional): Either train or test mode. Default 'train'. transform (Callable, optional): transform to perform on image, None for no transform. Default: None. download (bool, optional): download dataset automatically if :attr:`data_file` is None. Default True. backend (str, optional): Specifies which type of image to be returned: PIL.Image or numpy.ndarray. Should be one of {'pil', 'cv2'}. If this option is not set, will get backend from :ref:`paddle.vision.get_image_backend `, default backend is 'pil'. Default: None. Returns: :ref:`api_paddle_io_Dataset`. An instance of Cifar100 dataset. Examples: .. code-block:: python >>> # doctest: +TIMEOUT(60) >>> import itertools >>> import paddle.vision.transforms as T >>> from paddle.vision.datasets import Cifar100 >>> cifar100 = Cifar100() >>> print(len(cifar100)) 50000 >>> for i in range(5): # only show first 5 images ... img, label = cifar100[i] ... # do something with img and label ... print(type(img), img.size, label) ... # (32, 32) 19 >>> transform = T.Compose( ... [ ... T.Resize(64), ... T.ToTensor(), ... T.Normalize( ... mean=[0.5, 0.5, 0.5], ... std=[0.5, 0.5, 0.5], ... to_rgb=True, ... ), ... ] ... ) >>> cifar100_test = Cifar100( ... mode="test", ... transform=transform, # apply transform to every image ... backend="cv2", # use OpenCV as image transform backend ... ) >>> print(len(cifar100_test)) 10000 >>> for img, label in itertools.islice(iter(cifar100_test), 5): # only show first 5 images ... # do something with img and label ... print(type(img), img.shape, label) ... # [3, 64, 64] 49 c,t||||||yr4)superr()r%rrr!r&r __class__s r'r(zCifar100.__init__s D)XwGr)cft|_t|_t|j dz|_y)N100) CIFAR100_URLr CIFAR100_MD5r r.rr/r0s r'rzCifar100._init_url_md5_flags%$ $ !$))e"34 r)r^)r_r`rarbr(r __classcell__)rhs@r'reres%?F  H5r)re)r?r<numpyrRPILrrpaddle.dataset.commonr paddle.ior__all__ URL_PREFIXr,r-rkrlr.rrercr)r'rtsx <  3 33 0 55 1   PgPfO5wO5r)