mindspore.dataset.vision.c_transforms.CutMixBatch

class mindspore.dataset.vision.c_transforms.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 operator.

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, range: [0, 1] (default = 1.0).

Raises

  • TypeError – If image_batch_format is not of type 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, 1].

  • RuntimeError – If given tensor shape is not <H, W, C>.

Supported Platforms:

CPU

Examples

>>> from mindspore.dataset.vision import ImageBatchFormat
>>> onehot_op = c_transforms.OneHot(num_classes=10)
>>> image_folder_dataset= image_folder_dataset.map(operations=onehot_op,
...                                                input_columns=["label"])
>>> cutmix_batch_op = c_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"])