YhddlZddlmZddlmZmZmZmZddl m Z ddl m Z Gdde Z Gd d e ZdS) N)Path)AnyCallableOptionalUnion)Image) VisionDatasetc eZdZdZ ddeeefdedeedeedeeddf fd Z d e de j fd Z d e de efd Zd e deeeffdZde fdZxZS) CocoDetectionah`MS Coco Detection `_ Dataset. It requires `pycocotools `_ to be installed, which could be installed via ``pip install pycocotools`` or ``conda install conda-forge::pycocotools``. Args: root (str or ``pathlib.Path``): Root directory where images are downloaded to. annFile (string): Path to json annotation file. transform (callable, optional): A function/transform that takes in a PIL image and returns a transformed version. E.g, ``transforms.PILToTensor`` target_transform (callable, optional): A function/transform that takes in the target and transforms it. transforms (callable, optional): A function/transform that takes input sample and its target as entry and returns a transformed version. NrootannFile transformtarget_transform transformsreturnct||||ddlm}|||_t t |jj|_ dS)Nr)COCO) super__init__pycocotools.cocorcocolistsortedimgskeysids)selfr rrrrr __class__s k/var/www/tools.fuzzalab.pt/emblema-extractor/venv/lib/python3.11/site-packages/torchvision/datasets/coco.pyrzCocoDetection.__init__sp z96FGGG))))))DMM ty~22445566idc|j|dd}tjtj|j|dS)Nr file_nameRGB) rloadImgsropenospathjoinr convert)rr"r)s r _load_imagezCocoDetection._load_image)sQy!!"%%a(5z"',,ty$7788@@GGGr!cf|j|j|SN)rloadAnns getAnnIds)rr"s r _load_targetzCocoDetection._load_target-s(y!!$)"5"5b"9"9:::r!indexc"t|ts tdt|d|j|}||}||}|j|||\}}||fS)Nz#Index must be of type integer, got z instead.) isinstanceint ValueErrortyperr,r1r)rr2r"imagetargets r __getitem__zCocoDetection.__getitem__0s%%% [Y4;;YYYZZ Z Xe_  $$""2&& ? & OOE6::ME6f}r!c*t|jSr.)lenr)rs r __len__zCocoDetection.__len__>s48}}r!)NNN)__name__ __module__ __qualname____doc__rstrrrrrr5rr,rrr1tupler:r= __classcell__rs@r r r s>()-/3)- 7 7CI 7 7H% 7 #8, 7 X& 7  7 7 7 7 7 7HcHekHHHH;s;tCy;;;;  sCx    r!r c8eZdZdZdedeeffd ZxZS) CocoCaptionsa[`MS Coco Captions `_ Dataset. It requires `pycocotools `_ to be installed, which could be installed via ``pip install pycocotools`` or ``conda install conda-forge::pycocotools``. Args: root (str or ``pathlib.Path``): Root directory where images are downloaded to. annFile (string): Path to json annotation file. transform (callable, optional): A function/transform that takes in a PIL image and returns a transformed version. E.g, ``transforms.PILToTensor`` target_transform (callable, optional): A function/transform that takes in the target and transforms it. transforms (callable, optional): A function/transform that takes input sample and its target as entry and returns a transformed version. Example: .. code:: python import torchvision.datasets as dset import torchvision.transforms as transforms cap = dset.CocoCaptions(root = 'dir where images are', annFile = 'json annotation file', transform=transforms.PILToTensor()) print('Number of samples: ', len(cap)) img, target = cap[3] # load 4th sample print("Image Size: ", img.size()) print(target) Output: :: Number of samples: 82783 Image Size: (3L, 427L, 640L) [u'A plane emitting smoke stream flying over a mountain.', u'A plane darts across a bright blue sky behind a mountain covered in snow', u'A plane leaves a contrail above the snowy mountain top.', u'A mountain that has a plane flying overheard in the distance.', u'A mountain view with a plume of smoke in the background'] r"rcZdt|DS)Ncg|] }|d S)caption).0anns r z-CocoCaptions._load_target..osCCC3ICCCr!)rr1)rr"rs r r1zCocoCaptions._load_targetns)CC%''*>*>r*B*BCCCCr!) r>r?r@rAr5rrBr1rDrEs@r rGrGBsf))VDsDtCyDDDDDDDDDDr!rG)os.pathr(pathlibrtypingrrrrPILrvisionr r rGrKr!r rTs111111111111!!!!!!55555M555p-D-D-D-D-D=-D-D-D-D-Dr!