mindspore.dataset.vision.MixUp
- class mindspore.dataset.vision.MixUp(batch_size, alpha, is_single=True)[source]
Randomly mix up a batch of numpy.ndarray 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 integer.
TypeError – If alpha is not of type float.
TypeError – If is_single is not of type boolean.
ValueError – If batch_size is not positive.
ValueError – If alpha is not positive.
- Supported Platforms:
CPU
Examples
>>> import numpy as np >>> import mindspore.dataset as ds >>> import mindspore.dataset.vision as vision >>> import mindspore.dataset.transforms as transforms >>> >>> # Use the transform in dataset pipeline mode >>> data = np.random.randint(0, 255, size=(64, 64, 3)).astype(np.uint8) >>> numpy_slices_dataset = ds.NumpySlicesDataset(data, ["image"]) >>> numpy_slices_dataset = numpy_slices_dataset.map( ... operations=lambda img: (data, np.random.randint(0, 5, (3, 1))), ... input_columns=["image"], ... output_columns=["image", "label"]) >>> # ont hot decode the label >>> numpy_slices_dataset = numpy_slices_dataset.map(operations=transforms.OneHot(10), input_columns="label") >>> # batch the samples >>> numpy_slices_dataset = numpy_slices_dataset.batch(batch_size=4) >>> # finally mix up the images and labels >>> numpy_slices_dataset = numpy_slices_dataset.map( ... operations=vision.MixUp(batch_size=1, alpha=0.2), ... input_columns=["image", "label"]) >>> for item in numpy_slices_dataset.create_dict_iterator(num_epochs=1, output_numpy=True): ... print(item["image"].shape, item["image"].dtype) ... print(item["label"].shape, item["label"].dtype) ... break (4, 64, 64, 3) float64 (4, 3, 10) float64 >>> >>> # Use the transform in eager mode >>> data = np.random.randint(0, 255, size=(100, 100, 3)).astype(np.uint8) >>> label = np.array([[0, 1]]) >>> output = vision.MixUp(batch_size=2, alpha=0.2, is_single=False)(data, label) >>> print(output[0].shape, output[0].dtype) (2, 100, 100, 3) float64 >>> print(output[1].shape, output[1].dtype) (2, 2) float64
- Tutorial Examples: