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import os.path
from pathlib import Path
from typing import Any, Callable, List, Optional, Tuple, Union
from PIL import Image
from .vision import VisionDataset
class CocoDetection(VisionDataset):
"""`MS Coco Detection <https://cocodataset.org/#detection-2016>`_ Dataset.
It requires `pycocotools <https://github.com/ppwwyyxx/cocoapi>`_ 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.
"""
def __init__(
self,
root: Union[str, Path],
annFile: str,
transform: Optional[Callable] = None,
target_transform: Optional[Callable] = None,
transforms: Optional[Callable] = None,
) -> None:
super().__init__(root, transforms, transform, target_transform)
from pycocotools.coco import COCO
self.coco = COCO(annFile)
self.ids = list(sorted(self.coco.imgs.keys()))
def _load_image(self, id: int) -> Image.Image:
path = self.coco.loadImgs(id)[0]["file_name"]
return Image.open(os.path.join(self.root, path)).convert("RGB")
def _load_target(self, id: int) -> List[Any]:
return self.coco.loadAnns(self.coco.getAnnIds(id))
def __getitem__(self, index: int) -> Tuple[Any, Any]:
if not isinstance(index, int):
raise ValueError(f"Index must be of type integer, got {type(index)} instead.")
id = self.ids[index]
image = self._load_image(id)
target = self._load_target(id)
if self.transforms is not None:
image, target = self.transforms(image, target)
return image, target
def __len__(self) -> int:
return len(self.ids)
class CocoCaptions(CocoDetection):
"""`MS Coco Captions <https://cocodataset.org/#captions-2015>`_ Dataset.
It requires `pycocotools <https://github.com/ppwwyyxx/cocoapi>`_ 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']
"""
def _load_target(self, id: int) -> List[str]:
return [ann["caption"] for ann in super()._load_target(id)]
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