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# CocoCaptions

class torchvision.datasets.CocoCaptions(root: str, annFile: str, transform: Optional[Callable] = None, target_transform: Optional[Callable] = None, transforms: Optional[Callable] = None)

  MS Coco Captions Dataset.

  It requires the COCO API to be installed.

  Parameters:

    - 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.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

      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']

  Special-members:

  __getitem__(index: int) → Tuple[Any, Any]
    Parameters:

      index (int) – Index

    Returns:

      Sample and meta data, optionally transformed by the respective transforms.

    Return type:

      (Any)

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