RandomZoomOut

class torchvision.transforms.v2.RandomZoomOut(fill: Union[int, float, Sequence[int], Sequence[float], None, Dict[Union[Type, str], Optional[Union[int, float, Sequence[int], Sequence[float]]]]] = 0, side_range: Sequence[float] = (1.0, 4.0), p: float = 0.5)

“Zoom out” transformation from “SSD: Single Shot MultiBox Detector”.

This transformation randomly pads images, videos, bounding boxes and masks creating a zoom out effect. Output spatial size is randomly sampled from original size up to a maximum size configured with side_range parameter:

r = uniform_sample(side_range[0], side_range[1])
output_width = input_width * r
output_height = input_height * r

If the input is a torch.Tensor or a TVTensor (e.g. Image, Video, BoundingBoxes etc.) it can have arbitrary number of leading batch dimensions. For example, the image can have [..., C, H, W] shape. A bounding box can have [..., 4] shape.

Parameters:

• fill (number or tuple or dict, optional) – Pixel fill value used when the padding_mode is constant. Default is 0. If a tuple of length 3, it is used to fill R, G, B channels respectively. Fill value can be also a dictionary mapping data type to the fill value, e.g. fill={tv_tensors.Image: 127, tv_tensors.Mask: 0} where Image will be filled with 127 and Mask will be filled with 0.

• side_range (sequence of python:floats, optional) – tuple of two floats defines minimum and maximum factors to scale the input size.

• p (float, optional) – probability that the zoom operation will be performed.

Examples using RandomZoomOut:

Transforms v2: End-to-end object detection/segmentation example

Transforms v2: End-to-end object detection/segmentation example

make_params(flat_inputs: List[Any]) → Dict[str, Any]

Method to override for custom transforms.

See How to write your own v2 transforms

transform(inpt: Any, params: Dict[str, Any]) → Any

Method to override for custom transforms.

See How to write your own v2 transforms