|j'@ddlmZddlZddlZddlmZmZmZddlZ ddl mZ ddl m Z ddlZddlmZddlmZer"ddl mZ ddlmZddlmZd d lmZmZed ZgZd Zed zZdZedzZdZ dddddZ!Gddee"dZ#Gdde#Z$dS)) annotationsN) TYPE_CHECKINGAnyLiteral)Image)_check_exists_and_download)Dataset) _DTypeLiteral) _Transform) _ImageBackend_ImageDataTypetraintestz$https://dataset.bj.bcebos.com/cifar/zcifar-10-python.tar.gz c58f30108f718f92721af3b95e74349azcifar-100-python.tar.gz eb9058c3a382ffc7106e4002c42a8d85 data_batch test_batchrr)train10test10train100test100cteZdZUdZded<ded<ded<ded <d ed < dddZdZdZddZdZ d S)Cifar10a9 Implementation of `Cifar-10 `_ dataset, which has 10 categories. Args: data_file (str|None, 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|None, 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|None, 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) # type: ignore ... # [3, 64, 64] 3 _DatasetModemoder backend str | None data_file_Transform[Any, Any] | None transformr dtypeNrTdownloadbool_ImageBackend | NonereturnNonec|dvs Jd|||_|tj}|dvrt d|||_|||_|j.|s Jdt||j |j d||_||_ | tj|_dS)Nrz2mode.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)lowerrpaddlevisionget_image_backend ValueErrorr_init_url_md5_flagr rdata_urldata_md5r" _load_dataget_default_dtyper#)selfr rr"r$rs l/lsinfo/ai/hellotax_ai/data_center/backend/venv/lib/python3.11/site-packages/paddle/vision/datasets/cifar.py__init__zCifar10.__init__}s(zz||     G F F   JJLL ?m5577G . ( (PwPP   !!!" > !  P  884=$-(DN# -// cjt|_t|_t|jdz|_dS)N10) CIFAR10_URLr3 CIFAR10_MD5r4 MODE_FLAG_MAPrflagr7s r8r2zCifar10._init_url_md5_flags'# # !$)d"23 r:c g_tjjd5}fd|D}t |}|D]}t j||d}|d}|d|dd}|Jt||D]!\}}j ||f" ddddS#1swxYwYdS) Nr)rc3BK|]}j|jv|jVdSN)r@name).0 each_itemr7s r8 z%Cifar10._load_data..s<#,TY).5P5P 5P5P5P5Pr:bytes)encodingsdataslabelss fine_labels) datatarfileopenr sortedpickleload extractfilegetzipappend) r7fnamesrFbatchrLlabelssamplelabels ` r8r5zCifar10._load_datas` \$.s 3 3 3 6q01E5MME 6 6 AMM$$7$7'JJJW~9eii.M.MNN)))%(v%6%666MFEI$$fe_55556  6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6sB4C&&C*-C*idxint'tuple[_ImageDataType, npt.NDArray[Any]]c|j|\}}tj|gd}|gd}|jdkr't j|d}|j||}|jdkr)|tj |dfS||j tj |dfS)N) ra)r rr*uint8int64) rLnpreshape transposerr fromarrayastyper"arrayr#)r7r\imager[s r8 __getitem__zCifar10.__getitem__sy~ u 5+++.. ** <5 OELL$9$9::E > %NN5))E <5 "(5//00999 9||DJ''%)?)?)H)HHHr:c*t|jSrE)lenrLrAs r8__len__zCifar10.__len__s49~~r:NrNTN r rrrr"r!r$r%rr&r'r()r\r]r'r^) __name__ __module__ __qualname____doc____annotations__r9r2r5rlror:r8rr6s>>@****!%$15(, &0&0&0&0&0P444 666$ I I I Ir:r)rznpt.NDArray[Any]c6eZdZdZ ddfd ZdZxZS)Cifar100aI Implementation of `Cifar-100 `_ dataset, which has 100 categories. Args: data_file (str|None, 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|None, 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|None, 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) # type: ignore ... # [3, 64, 64] 49 NrTr rrrr"r!r$r%rr&r'r(cRt|||||dSrE)superr9)r7r rr"r$r __class__s r8r9zCifar100.__init__s+ D)XwGGGGGr:cjt|_t|_t|jdz|_dS)N100) CIFAR100_URLr3 CIFAR100_MD5r4r?rr@rAs r8r2zCifar100._init_url_md5_flags'$ $ !$)e"34 r:rprq)rrrsrtrur9r2 __classcell__)r|s@r8ryryst??F!%$15(, HHHHHHH5555555r:ry)% __future__rrPrMtypingrrrnumpyre numpy.typingnptPILrr.paddle.dataset.commonr paddle.ior paddle._typing.dtype_liker #paddle.vision.transforms.transformsr rkr rr__all__ URL_PREFIXr=r>rrr?tuplerryrwr:r8rs#""""" .......... <<<<<<,777777>>>>>>55555555?+L  3 33 0 55 1   VVVVVge@ABVVVrO5O5O5O5O5wO5O5O5O5O5r: