o Qe4%@sddlmZddlZddlZddlZddlmZddlm Z ddl Z ddl m Z ddlmZgZdZedZd Zed Zd Zd d dddZGddde ZGdddeZdS))print_functionN)Image)cPickle)Dataset)_check_exists_and_downloadz$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)Cifar10az 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 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|||_|||_ |j durF|s;Jdt ||j |j d||_ ||_ |t|_dS)N)rrz4mode.lower() should be 'train' or 'test', but got {})pilZcv2z6Expected backend are one of ['pil', 'cv2'], but got {}z>data_file is not set and downloading automatically is disabledZcifar)lowerformatmodepaddleZvisionZget_image_backend ValueErrorbackend_init_url_md5_flag data_filerdata_urldata_md5 transform _load_dataZget_default_dtypedtypeselfrr rdownloadrrLD:\Projects\ConvertPro\env\Lib\site-packages\paddle/vision/datasets/cifar.py__init__ls4   zCifar10.__init__cC t|_t|_t|jd|_dS)N10) CIFAR10_URLr CIFAR10_MD5r MODE_FLAG_MAPr flagrrrrrzCifar10._init_url_md5_flagc sg_tjjddX}fdd|D}tt|}|D]=}tj||dd}|t d}| t d| t d d}|dusFJt j ||D] \}}j||fqMqWddS1sfwYdS) Nr)r c3s"|] }j|jvr|jVqdSN)r#name).0Z each_itemr$rr sz%Cifar10._load_data..bytes)encodingdatalabelsZ fine_labels)r-tarfileopenrsortedlistpickleload extractfilesixbgetmoveszipappend) rfnamesr(batchr-r.samplelabelrr$rrs$   "zCifar10._load_datacCs|j|\}}t|gd}|gd}|jdkr#t|d}|jdur-||}|jdkr<|t |dfS||j t |dfS)N) rB)rr Zuint8Zint64) r-npZreshapeZ transposerrZ fromarrayZastyperarrayr)ridximager@rrr __getitem__s    zCifar10.__getitem__cCs t|jSr')lenr-r$rrr__len__s zCifar10.__len__NrNTN) __name__ __module__ __qualname____doc__rrrrIrKrrrrr +sB * r cs4eZdZdZ     d fdd ZddZZS) Cifar100a Implementation of `Cifar-100 `_ dataset, which has 100 categories. Args: data_file (str, optional): path to data file, can be set None if :attr:`download` is True. Default: None, default data path: ~/.cache/paddle/dataset/cifar mode (str, optional): Either train or test mode. Default 'train'. transform (Callable, optional): transform to perform on image, None for no transform. Default: None. download (bool, optional): download dataset automatically if :attr:`data_file` is None. Default True. backend (str, optional): Specifies which type of image to be returned: PIL.Image or numpy.ndarray. Should be one of {'pil', 'cv2'}. If this option is not set, will get backend from :ref:`paddle.vision.get_image_backend `, default backend is 'pil'. Default: None. Returns: :ref:`api_paddle_io_Dataset`. An instance of Cifar100 dataset. Examples: .. code-block:: python import itertools import paddle.vision.transforms as T from paddle.vision.datasets import Cifar100 cifar100 = Cifar100() print(len(cifar100)) # 50000 for i in range(5): # only show first 5 images img, label = cifar100[i] # do something with img and label print(type(img), img.size, label) # (32, 32) 19 transform = T.Compose( [ T.Resize(64), T.ToTensor(), T.Normalize( mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], to_rgb=True, ), ] ) cifar100_test = Cifar100( mode="test", transform=transform, # apply transform to every image backend="cv2", # use OpenCV as image transform backend ) print(len(cifar100_test)) # 10000 for img, label in itertools.islice(iter(cifar100_test), 5): # only show first 5 images # do something with img and label print(type(img), img.shape, label) # [3, 64, 64] 49 NrTcstt||||||dSr')superrQrr __class__rrrs  zCifar100.__init__cCr)N100) CIFAR100_URLr CIFAR100_MD5rr"r r#r$rrrrr%zCifar100._init_url_md5_flagrL)rMrNrOrPrr __classcell__rrrSrrQsB rQ) __future__rr/numpyrEr6ZPILrZ six.movesrr3rZ paddle.iorZpaddle.dataset.commonr__all__Z URL_PREFIXr r!rVrWr"r rQrrrrs.