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# Sintel

class torchvision.datasets.Sintel(root: str, split: str = 'train', pass_name: str = 'clean', transforms: Optional[Callable] = None)

  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 as `KittiFlow`.

  Special-members:

  __getitem__(index: int) → Union[Tuple[Image, Image, Optional[ndarray], Optional[ndarray]], Tuple[Image, Image, Optional[ndarray]]]

    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 if `split="test"`. If a valid flow mask is generated within the `transforms` parameter, a 4-tuple with `(img1, img2, flow, valid_flow_mask)` is returned.

    Return type:

      tuple

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