mindspore.dataset.vision.CutMixBatch

class mindspore.dataset.vision.CutMixBatch(image_batch_format, alpha=1.0, prob=1.0)[source]

Apply CutMix transformation on input batch of images and labels. Note that you need to make labels into one-hot format and batched before calling this operation.

Parameters

image_batch_format (ImageBatchFormat) – The method of padding. Can be any of [ImageBatchFormat.NHWC, ImageBatchFormat.NCHW].
alpha (float, optional) – Hyperparameter of beta distribution, must be larger than 0. Default: 1.0.
prob (float, optional) – The probability by which CutMix is applied to each image, which must be in range: [0.0, 1.0]. Default: 1.0.

Raises

TypeError – If image_batch_format is not of type mindspore.dataset.vision.ImageBatchFormat .
TypeError – If alpha is not of type float.
TypeError – If prob is not of type float.
ValueError – If alpha is less than or equal 0.
ValueError – If prob is not in range [0.0, 1.0].
RuntimeError – If given tensor shape is not <H, W, C>.

Supported Platforms:: CPU

Examples

>>> import mindspore.dataset as ds
>>> import mindspore.dataset.vision as vision
>>> import mindspore.dataset.transforms as transforms
>>> from mindspore.dataset.vision import ImageBatchFormat
>>>
>>> image_folder_dataset = ds.ImageFolderDataset("/path/to/image_folder_dataset_directory")
>>> onehot_op = transforms.OneHot(num_classes=10)
>>> image_folder_dataset= image_folder_dataset.map(operations=onehot_op,
...                                                input_columns=["label"])
>>> cutmix_batch_op = vision.CutMixBatch(ImageBatchFormat.NHWC, 1.0, 0.5)
>>> image_folder_dataset = image_folder_dataset.batch(5)
>>> image_folder_dataset = image_folder_dataset.map(operations=cutmix_batch_op,
...                                                 input_columns=["image", "label"])

Tutorial Examples:

Illustration of vision transforms