RandomZoomOut¶

class torchaug.transforms.RandomZoomOut(fill=0, side_range=(1.0, 4.0), p=0.5)[source]¶

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 TATensor (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 (Union[int, float, Sequence[int], Sequence[float], None, Dict[Union[Type, str], Union[int, float, Sequence[int], Sequence[float], None]]], optional) – Pixel fill value used when the padding_mode is constant. 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={ta_tensors.Image: 127, ta_tensors.Mask: 0} where Image will be filled with 127 and Mask will be filled with 0. Default: 0
side_range (Sequence[float], optional) – tuple of two floats defines minimum and maximum factors to scale the input size. Default: (1.0, 4.0)
p (float, optional) – probability that the zoom operation will be performed. Default: 0.5