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Implementation of `VOC2012 `_ dataset.
Args:
data_file (str, optional): Path to data file, can be set None if
:attr:`download` is True. Default: None, default data path: ~/.cache/paddle/dataset/voc2012.
mode (str, optional): Either train or test mode. Default 'train'.
transform (Callable, optional): Transform to perform on image, None for no transform. Default: None.
download (bool, optional): Download dataset automatically if :attr:`data_file` is None. Default: True.
backend (str, optional): Specifies which type of image to be returned:
PIL.Image or numpy.ndarray. Should be one of {'pil', 'cv2'}.
If this option is not set, will get backend from :ref:`paddle.vision.get_image_backend `,
default backend is 'pil'. Default: None.
Returns:
:ref:`api_paddle_io_Dataset`. An instance of VOC2012 dataset.
Examples:
.. code-block:: python
>>> # doctest: +TIMEOUT(75)
>>> import itertools
>>> import paddle.vision.transforms as T
>>> from paddle.vision.datasets import VOC2012
>>> voc2012 = VOC2012()
>>> print(len(voc2012))
2913
>>> for i in range(5): # only show first 5 images
... img, label = voc2012[i]
... # do something with img and label
... print(type(img), img.size)
... # (500, 281)
... print(type(label), label.size)
... # (500, 281)
>>> transform = T.Compose(
... [
... T.ToTensor(),
... T.Normalize(
... mean=[0.5, 0.5, 0.5],
... std=[0.5, 0.5, 0.5],
... to_rgb=True,
... ),
... ]
... )
>>> voc2012_test = VOC2012(
... mode="test",
... transform=transform, # apply transform to every image
... backend="cv2", # use OpenCV as image transform backend
... )
>>> print(len(voc2012_test))
1464
>>> for img, label in itertools.islice(iter(voc2012_test), 5): # only show first 5 images
... # do something with img and label
... print(type(img), img.shape)
... # [3, 281, 500]
... print(type(label), label.shape)
... # (281, 500)
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