text: from torchvision.datasets.vision import VisionDataset
  from PIL import Image
  import os
  import os.path
  
  
  class CocoCaptions(VisionDataset):
      """`MS Coco Captions <http://mscoco.org/dataset/#captions-challenge2015>`_ Dataset.
  
      Args:
          root (string): Root directory where images are downloaded to.
          annFile (string): Path to json annotation file.
          transform (callable, optional): A function/transform that  takes in an PIL image
              and returns a transformed version. E.g, ``transforms.ToTensor``
          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.ToTensor())
  
              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']
  
      """
  
      def __init__(self, root, annFile, transform=None, target_transform=None, transforms=None):
          super(CocoCaptions, self).__init__(root, transforms, transform, target_transform)
          from pycocotools.coco import COCO
          self.coco = COCO(annFile)
          self.ids = list(sorted(self.coco.imgs.keys()))
  
      def __getitem__(self, index):
          """
          Args:
              index (int): Index
  
          Returns:
              tuple: Tuple (image, target). target is a list of captions for the image.
          """
          coco = self.coco
          img_id = self.ids[index]
          ann_ids = coco.getAnnIds(imgIds=img_id)
          anns = coco.loadAnns(ann_ids)
          target = [ann['caption'] for ann in anns]
  
          path = coco.loadImgs(img_id)[0]['file_name']
  
          img = Image.open(os.path.join(self.root, path)).convert('RGB')
  
          if self.transforms is not None:
              img, target = self.transforms(img, target)
  
          return img, target
  
      def __len__(self):
          return len(self.ids)
  
  
  class CocoDetection(VisionDataset):
      """`MS Coco Detection <http://mscoco.org/dataset/#detections-challenge2016>`_ Dataset.
  
      Args:
          root (string): Root directory where images are downloaded to.
          annFile (string): Path to json annotation file.
          transform (callable, optional): A function/transform that  takes in an PIL image
              and returns a transformed version. E.g, ``transforms.ToTensor``
          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.
      """
  
      def __init__(self, root, annFile, transform=None, target_transform=None, transforms=None):
          super(CocoDetection, self).__init__(root, transforms, transform, target_transform)
          from pycocotools.coco import COCO
          self.coco = COCO(annFile)
          self.ids = list(sorted(self.coco.imgs.keys()))
  
      def __getitem__(self, index):
          """
          Args:
              index (int): Index
  
          Returns:
              tuple: Tuple (image, target). target is the object returned by ``coco.loadAnns``.
          """
          coco = self.coco
          img_id = self.ids[index]
          ann_ids = coco.getAnnIds(imgIds=img_id)
          target = coco.loadAnns(ann_ids)
  
          path = coco.loadImgs(img_id)[0]['file_name']
  
          img = Image.open(os.path.join(self.root, path)).convert('RGB')
          if self.transforms is not None:
              img, target = self.transforms(img, target)
  
          return img, target, path
  
      def __len__(self):
          return len(self.ids)
  