FlyingChairs¶
- class torchvision.datasets.FlyingChairs(root: str, 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 (string) – 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 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="val"
. If a valid flow mask is generated within thetransforms
parameter, a 4-tuple with(img1, img2, flow, valid_flow_mask)
is returned.- Return type: