mindspore.dataset.vision.py_transforms.MixUp

class mindspore.dataset.vision.py_transforms.MixUp(batch_size, alpha, is_single=True)[source]

Randomly mix up a batch of images together with its labels.

Each image will be multiplied by a random weight \(lambda\) generated from the Beta distribution and then added to another image multiplied by \(1 - lambda\). The same transformation will be applied to their labels with the same value of \(lambda\). Make sure that the labels are one-hot encoded in advance.

Parameters

batch_size (int) – The number of images in a batch.
alpha (float) – The alpha and beta parameter for the Beta distribution.
is_single (bool, optional) – If True, it will randomly mix up [img0, …, img(n-1), img(n)] with [img1, …, img(n), img0] in each batch. Otherwise, it will randomly mix up images with the output of the previous batch. Default: True.

Raises

TypeError – If batch_size is not of type int.
TypeError – If alpha is not of type float.
TypeError – If is_single is not of type bool.
ValueError – If batch_size is not positive.
ValueError – If alpha is not positive.

Supported Platforms:: CPU

Examples

>>> # Setup multi-batch mixup transformation
>>> transform = [py_vision.MixUp(batch_size=16, alpha=0.2, is_single=False)]
>>> # Apply the transform to the dataset through dataset.map()
>>> image_folder_dataset = image_folder_dataset.map(input_columns="image",
...                                                 operations=transform)