RandomZoomOut

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

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

Warning

The RandomZoomOut transform is in Beta stage, and while we do not expect major breaking changes, some APIs may still change according to user feedback. Please submit any feedback you may have in this issue: https://github.com/pytorch/vision/issues/6753, and you can also check out https://github.com/pytorch/vision/issues/7319 to learn more about the APIs that we suspect might involve future changes.

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 Datapoint (e.g. Image, Video, BoundingBox 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={datapoints.Image: 127, datapoints.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 of the input being flipped. Default value is 0.5

Examples using RandomZoomOut:

Transforms v2: End-to-end object detection example

Transforms v2: End-to-end object detection example