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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
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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