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# HD1K

class torchvision.datasets.HD1K(root: str, split: str = 'train', transforms: Optional[Callable] = None)

  HD1K dataset for optical flow.

  The dataset is expected to have the following structure:

      root
          hd1k
              hd1k_challenge
                  image_2
              hd1k_flow_gt
                  flow_occ
              hd1k_input
                  image_2

  Parameters:

    - root (string) – Root directory of the HD1K Dataset.

    - split (string, optional) – The dataset split, either “train” (default) or “test”

    - transforms (callable, optional) – A function/transform that takes in `img1, img2, flow, valid_flow_mask` and returns a transformed version.

  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 4-tuple with `(img1, img2, flow, valid_flow_mask)` where `valid_flow_mask` is a numpy boolean mask of shape (H, W) indicating which flow values are valid. The flow is a numpy array of shape (2, H, W) and the images are PIL images. `flow` and `valid_flow_mask` are None if `split="test"`.

    Return type:

      tuple

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