FlyingChairs — Torchvision 0.22 documentation (original) (raw)

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

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 inimg1, 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]]][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 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