QЦiT|SSKrSSKJr SSKJrJrJrJrJ r J r SSK J r SSK Jr "SS\5r"S S \5rg) N)Path)AnyCallableListOptionalTupleUnion)Image) VisionDatasetc ^\rSrSrSrSS\\\4S\S\\ S\\ S\\ S S4 U4S jjjr S \ S \ R4S jr S \ S \\4S jrS\ S \\\44SjrS \ 4SjrSrU=r$) CocoDetection a`MS Coco Detection `_ Dataset. It requires the `COCO API to be installed `_. Args: root (str or ``pathlib.Path``): Root directory where images are downloaded to. annFile (string): Path to json annotation file. transform (callable, optional): A function/transform that takes in a PIL image and returns a transformed version. E.g, ``transforms.PILToTensor`` target_transform (callable, optional): A function/transform that takes in the target and transforms it. transforms (callable, optional): A function/transform that takes input sample and its target as entry and returns a transformed version. NrootannFile transformtarget_transform transformsreturnc>[TU]XX45 SSKJn U"U5Ul[ [ URRR555Ul g)Nr)COCO) super__init__pycocotools.cocorcocolistsortedimgskeysids)selfrrrrrr __class__s X/var/www/html/ai-image-ml/venv/lib/python3.13/site-packages/torchvision/datasets/coco.pyrCocoDetection.__init__sD 9G)M tyy~~22456idcURRU5SSn[R"[R R URU55RS5$)Nr file_nameRGB) rloadImgsr openospathjoinrconvert)r!r&r-s r# _load_imageCocoDetection._load_image(sMyy!!"%a(5zz"'',,tyy$78@@GGr%cjURRURRU55$N)rloadAnns getAnnIds)r!r&s r# _load_targetCocoDetection._load_target,s&yy!!$))"5"5b"9::r%indexc[U[5(d[S[U5S35eURUnUR U5nUR U5nURbURX45up4X44$)Nz#Index must be of type integer, got z instead.) isinstanceint ValueErrortyper r0r6r)r!r8r&imagetargets r# __getitem__CocoDetection.__getitem__/sw%%%B4;-yYZ Z XXe_  $""2& ?? & OOE:ME}r%c,[UR5$r3)lenr )r!s r#__len__CocoDetection.__len__=s488}r%)rr )NNN)__name__ __module__ __qualname____firstlineno____doc__r strrrrrr;r r0rrr6rr@rD__static_attributes__ __classcell__r"s@r#rr s &)-/3)- 7CI 7 7H% 7 #8, 7 X& 7  7 7HcHekkH;s;tCy;  sCx r%rc>^\rSrSrSrS\S\\4U4SjjrSr U=r $) CocoCaptionsAa`MS Coco Captions `_ Dataset. It requires the `COCO API to be installed `_. Args: root (str or ``pathlib.Path``): Root directory where images are downloaded to. annFile (string): Path to json annotation file. transform (callable, optional): A function/transform that takes in a PIL image and returns a transformed version. E.g, ``transforms.PILToTensor`` target_transform (callable, optional): A function/transform that takes in the target and transforms it. transforms (callable, optional): A function/transform that takes input sample and its target as entry and returns a transformed version. Example: .. code:: python import torchvision.datasets as dset import torchvision.transforms as transforms cap = dset.CocoCaptions(root = 'dir where images are', annFile = 'json annotation file', transform=transforms.PILToTensor()) print('Number of samples: ', len(cap)) img, target = cap[3] # load 4th sample print("Image Size: ", img.size()) print(target) Output: :: Number of samples: 82783 Image Size: (3L, 427L, 640L) [u'A plane emitting smoke stream flying over a mountain.', u'A plane darts across a bright blue sky behind a mountain covered in snow', u'A plane leaves a contrail above the snowy mountain top.', u'A mountain that has a plane flying overheard in the distance.', u'A mountain view with a plume of smoke in the background'] r&rcR>[TU]U5Vs/sHo"SPM sn$s snf)Ncaption)rr6)r!r&annr"s r#r6CocoCaptions._load_targetls)*/'*>r*BC*B3I*BCCCs$) rFrGrHrIrJr;rrKr6rLrMrNs@r#rPrPAs)(TDsDtCyDDr%rP)os.pathr,pathlibrtypingrrrrrr PILr visionr rrPrVr%r#r\s2>>!4M4n,D=,Dr%