o #jy$@sddlZddlZddlZddlmZddlZddlmZddl m Z gZ dZ e dZ dZe dZd Zd d d d dZGddde ZGdddeZdS)N)Image)_check_exists_and_download)Datasetz$https://dataset.bj.bcebos.com/cifar/zcifar-10-python.tar.gzZ c58f30108f718f92721af3b95e74349azcifar-100-python.tar.gzZ eb9058c3a382ffc7106e4002c42a8d85Z data_batchZ test_batchtraintest)Ztrain10Ztest10Ztrain100Ztest100c@sDeZdZdZ     dddZddZd d Zd d Zd dZdS)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 NrTcCs|dvs Jd|||_|durtj}|dvr&td|||_|||_|jdurF|s;Jdt ||j |j d||_||_ | t|_dS)N)rrz2mode.lower() should be 'train' or 'test', but got )pilZcv2z4Expected backend are one of ['pil', 'cv2'], but got z>data_file is not set and downloading automatically is disabledZcifar)lowermodepaddleZvisionZget_image_backend ValueErrorbackend_init_url_md5_flag data_filerdata_urldata_md5 transform _load_dataZget_default_dtypedtypeselfrr rdownloadr r]/var/www/html/Deteccion_Ine/venv/lib/python3.10/site-packages/paddle/vision/datasets/cifar.py__init__js0   zCifar10.__init__cC t|_t|_t|jd|_dS)N10) CIFAR10_URLr CIFAR10_MD5r MODE_FLAG_MAPr flagrrrrrzCifar10._init_url_md5_flagc sg_tjjddK}fdd|D}t|}|D]2}tj||dd}|d}|d|d d}|dus;Jt ||D] \}}j ||fq@qWddS1sYwYdS) Nr)r c3s"|] }j|jvr|jVqdSN)r name).0Z each_itemr!rr sz%Cifar10._load_data..bytes)encodingsdataslabelss fine_labels) datatarfileopenrsortedpickleload extractfilegetzipappend) rfnamesr%batchr*labelssamplelabelrr!rrs   "zCifar10._load_datacCs|j|\}}t|gd}|gd}|jdkr#t|d}|jdur-||}|jdkr<|t |dfS||j t |dfS)N) r;)rrZuint8Zint64) r*npZreshapeZ transposer rZ fromarrayZastyperarrayr)ridximager9rrr __getitem__s    zCifar10.__getitem__cCs t|jSr$)lenr*r!rrr__len__s zCifar10.__len__NrNTN) __name__ __module__ __qualname____doc__rrrrBrDrrrrr)sB ( rcs4eZdZdZ     d fdd ZddZZS) Cifar100a* Implementation of `Cifar-100 `_ dataset, which has 100 categories. Args: data_file (str, optional): path to data file, can be set None if :attr:`download` is True. Default: None, default data path: ~/.cache/paddle/dataset/cifar mode (str, optional): Either train or test mode. Default 'train'. transform (Callable, optional): transform to perform on image, None for no transform. Default: None. download (bool, optional): download dataset automatically if :attr:`data_file` is None. Default True. backend (str, optional): Specifies which type of image to be returned: PIL.Image or numpy.ndarray. Should be one of {'pil', 'cv2'}. If this option is not set, will get backend from :ref:`paddle.vision.get_image_backend `, default backend is 'pil'. Default: None. Returns: :ref:`api_paddle_io_Dataset`. An instance of Cifar100 dataset. Examples: .. code-block:: python >>> # doctest: +TIMEOUT(60) >>> import itertools >>> import paddle.vision.transforms as T >>> from paddle.vision.datasets import Cifar100 >>> cifar100 = Cifar100() >>> print(len(cifar100)) 50000 >>> for i in range(5): # only show first 5 images ... img, label = cifar100[i] ... # do something with img and label ... print(type(img), img.size, label) ... # (32, 32) 19 >>> transform = T.Compose( ... [ ... T.Resize(64), ... T.ToTensor(), ... T.Normalize( ... mean=[0.5, 0.5, 0.5], ... std=[0.5, 0.5, 0.5], ... to_rgb=True, ... ), ... ] ... ) >>> cifar100_test = Cifar100( ... mode="test", ... transform=transform, # apply transform to every image ... backend="cv2", # use OpenCV as image transform backend ... ) >>> print(len(cifar100_test)) 10000 >>> for img, label in itertools.islice(iter(cifar100_test), 5): # only show first 5 images ... # do something with img and label ... print(type(img), img.shape, label) ... # [3, 64, 64] 49 NrTcst|||||dSr$)superrr __class__rrrszCifar100.__init__cCr)N100) CIFAR100_URLr CIFAR100_MD5rrr r r!rrrrr"zCifar100._init_url_md5_flagrE)rFrGrHrIrr __classcell__rrrLrrJsC rJ)r.r+numpyr>ZPILrr Zpaddle.dataset.commonrZ paddle.ior__all__Z URL_PREFIXrrrOrPrrrJrrrrs*