Cityscapes

class torchvision.datasets.Cityscapes(root: str, split: str = 'train', mode: str = 'fine', target_type: Union[List[str], str] = 'instance', transform: Optional[Callable] = None, target_transform: Optional[Callable] = None, transforms: Optional[Callable] = None)

Cityscapes Dataset.

Parameters

• root (string) – 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

dataset = Cityscapes('./data/cityscapes', split='train', mode='fine',
                     target_type='semantic')

img, smnt = dataset[0]

Get multiple targets

dataset = Cityscapes('./data/cityscapes', split='train', mode='fine',
                     target_type=['instance', 'color', 'polygon'])

img, (inst, col, poly) = dataset[0]

Validate on the “coarse” set

dataset = Cityscapes('./data/cityscapes', split='val', mode='coarse',
                     target_type='semantic')

img, smnt = dataset[0]

Special-members

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

Parameters

index (int) – Index

Returns

(image, target) where target is a tuple of all target types if target_type is a list with more than one item. Otherwise target is a json object if target_type=”polygon”, else the image segmentation.

Return type

tuple