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Cityscapesaw `Cityscapes `_ Dataset.
Args:
root (str or ``pathlib.Path``): Root directory of dataset where directory ``leftImg8bit``
and ``gtFine`` or ``gtCoarse`` are located.
split (string, optional): The image split to use, ``train``, ``test`` or ``val`` if mode="fine"
otherwise ``train``, ``train_extra`` or ``val``
mode (string, optional): The quality mode to use, ``fine`` or ``coarse``
target_type (string or list, optional): Type of target to use, ``instance``, ``semantic``, ``polygon``
or ``color``. Can also be a list to output a tuple with all specified target types.
transform (callable, optional): A function/transform that takes in a PIL image
and returns a transformed version. E.g, ``transforms.RandomCrop``
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.
Examples:
Get semantic segmentation target
.. code-block:: python
dataset = Cityscapes('./data/cityscapes', split='train', mode='fine',
target_type='semantic')
img, smnt = dataset[0]
Get multiple targets
.. code-block:: python
dataset = Cityscapes('./data/cityscapes', split='train', mode='fine',
target_type=['instance', 'color', 'polygon'])
img, (inst, col, poly) = dataset[0]
Validate on the "coarse" set
.. code-block:: python
dataset = Cityscapes('./data/cityscapes', split='val', mode='coarse',
target_type='semantic')
img, smnt = dataset[0]
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Args:
index (int): Index
Returns:
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