Sintel¶
- class torchvision.datasets.Sintel(root: str, split: str = 'train', pass_name: str = 'clean', transforms: Optional[Callable] = None)[source]¶
Sintel Dataset for optical flow.
The dataset is expected to have the following structure:
root Sintel testing clean scene_1 scene_2 ... final scene_1 scene_2 ... training clean scene_1 scene_2 ... final scene_1 scene_2 ... flow scene_1 scene_2 ...
- Parameters:
root (string) – Root directory of the Sintel Dataset.
split (string, optional) – The dataset split, either “train” (default) or “test”
pass_name (string, optional) – The pass to use, either “clean” (default), “final”, or “both”. See link above for details on the different passes.
transforms (callable, optional) – A function/transform that takes in
img1, img2, flow, valid_flow_mask
and returns a transformed version.valid_flow_mask
is expected for consistency with other datasets which return a built-in valid mask, such asKittiFlow
.
- Special-members:
- __getitem__(index: int) Union[Tuple[Image, Image, Optional[ndarray], Optional[ndarray]], Tuple[Image, Image, Optional[ndarray]]] [source]¶
Return example at given index.
- Parameters:
index (int) – The index of the example to retrieve
- Returns:
A 3-tuple with
(img1, img2, flow)
. The flow is a numpy array of shape (2, H, W) and the images are PIL images.flow
is None ifsplit="test"
. If a valid flow mask is generated within thetransforms
parameter, a 4-tuple with(img1, img2, flow, valid_flow_mask)
is returned.- Return type: