RandomResize

class torchaug.transforms.RandomResize(min_size, max_size, interpolation=InterpolationMode.BILINEAR, antialias=True)[source]

Randomly resize the input.

This transformation can be used together with RandomCrop as data augmentations to train models on image segmentation task.

Output spatial size is randomly sampled from the interval [min_size, max_size]:

size = uniform_sample(min_size, max_size)
output_width = size
output_height = size

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:

  • min_size (int) – Minimum output size for random sampling

  • max_size (int) – Maximum output size for random sampling

  • interpolation (Union[InterpolationMode, int], optional) – Desired interpolation enum defined by torchvision.transforms.InterpolationMode. Only InterpolationMode.NEAREST, InterpolationMode.NEAREST_EXACT, InterpolationMode.BILINEAR and InterpolationMode.BICUBIC are supported. Default: InterpolationMode.BILINEAR

  • antialias (bool, optional) – Whether to apply antialiasing. Default: True