ёi'*SSKJr SSKrSSKrSSKJrJrJr SSKr SSK Jr SSK J r SSKrSSKJr SSKJr \(aSSK Jr SSKJr SSKJr S S KJrJr \S r/rS r\S -rSr\S-rSr SSSSS.r!"SS\\"S5r#"SS\#5r$g)) annotationsN) TYPE_CHECKINGAnyLiteral)Image)_check_exists_and_download)Dataset) _DTypeLiteral) _Transform) _ImageBackend_ImageDataTypetraintestz$https://dataset.bj.bcebos.com/cifar/zcifar-10-python.tar.gz c58f30108f718f92721af3b95e74349azcifar-100-python.tar.gz eb9058c3a382ffc7106e4002c42a8d85 data_batch test_batchrr)train10test10train100test100c\rSrSr%SrS\S'S\S'S\S'S \S 'S \S 'SSSjjrSrSrSSjr Sr Sr g )Cifar106am 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 dtypeNcUR5S;d SU35eUR5UlUc[RR 5nUS;a[ SU35eXPlUR5 XlURc5U(dS5e[XRURSU5UlX0l UR5 [R"5Ulg)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$)selfr!rr#downloadrs \/var/www/html/banglarbhumi/venv/lib/python3.13/site-packages/paddle/vision/datasets/cifar.py__init__Cifar10.__init__}szz|   G@v F G JJL ?mm557G . (FwiP   !" >> ! P 88==$--(DN# --/ cf[Ul[Ul[UR S-Ulg)N10) CIFAR10_URLr/ CIFAR10_MD5r0 MODE_FLAG_MAPrflagr3s r5r.Cifar10._init_url_md5_flags%# # !$))d"23 r8c ^/Tl[R"TRSS9nU4SjU5n[ U5nUHn[ R "URU5SS9nUSnURSURSS55nUce[XV5H!upxTRRXx45 M# M SSS5 g!,(df  g=f) Nr)rc3t># UH-nTRUR;dMURv M/ g7fN)r>name).0 each_itemr3s r5 %Cifar10._load_data..s)019TYY)..5P s88bytes)encodingsdataslabelss fine_labels) datatarfileopenr!sortedpickleload extractfilegetzipappend) r3fnamesrEbatchrLlabelssamplelabels ` r5r1Cifar10._load_datas \\$..s 3q01E5ME AMM$$7'JW~9eii.MN)))%(%6MFII$$f_5&7 4 3 3s B!C C cURUup#[R"U/SQ5nUR/SQ5nURS:Xa%[ R "URS55nURbURU5nURS:Xa'U[R"U5RS54$URUR5[R"U5RS54$)N) r_)r rr&uint8int64) rLnpreshape transposerr fromarrayastyper#arrayr$)r3idximager[s r5 __getitem__Cifar10.__getitem__syy~  5+. * <<5 OOELL$9:E >> %NN5)E <<5 "((5/0099 9||DJJ'%)?)?)HHHr8c,[UR5$rD)lenrLr?s r5__len__Cifar10.__len__s499~r8) rrLr!r0r/r$r>rr#NrNTN r!r rrr#r"r4boolrz_ImageBackend | NonereturnNone)riintrtz'tuple[_ImageDataType, npt.NDArray[Any]]) __name__ __module__ __qualname____firstlineno____doc____annotations__r6r.r1rkro__static_attributes__r8r5rr6s>@  ** !%$15(, &0&0&0/ &0  &0 & &0 &0P4 6$ Ir8r)rznpt.NDArray[Any]cZ^\rSrSrSrSSU4SjjjrSrSrU=r$)Cifar100a} 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 c(>[TU]XX4U5 grD)superr6)r3r!rr#r4r __class__s r5r6Cifar100.__init__s )wGr8cf[Ul[Ul[UR S-Ulg)N100) CIFAR100_URLr/ CIFAR100_MD5r0r=rr>r?s r5r.Cifar100._init_url_md5_flags%$ $ !$))e"34 r8)r0r/r>rqrr) rwrxryrzr{r6r.r} __classcell__)rs@r5rrsp?F!%$15(, HHH/ H  H & H HH55r8r)% __future__rrPrMtypingrrrnumpyrc numpy.typingnptPILrr*paddle.dataset.commonr paddle.ior paddle._typing.dtype_liker #paddle.vision.transforms.transformsr rjr rr__all__ URL_PREFIXr;r<rrr=tuplerrr~r8r5rs# .. <7>5?+L  3 33 0 55 1   Vge@ABVrO5wO5r8