FlyingChairs

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

FlyingChairs Dataset for optical flow.

You will also need to download the FlyingChairs_train_val.txt file from the dataset page.

The dataset is expected to have the following structure:

root
    FlyingChairs
        data
            00001_flow.flo
            00001_img1.ppm
            00001_img2.ppm
            ...
        FlyingChairs_train_val.txt

Parameters:

• root (str or pathlib.Path) – Root directory of the FlyingChairs Dataset.

• split (string, optional) – The dataset split, either “train” (default) or “val”

• 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="val". 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