mindspore.dataset.vision.RandAugment
- class mindspore.dataset.vision.RandAugment(num_ops=2, magnitude=9, num_magnitude_bins=31, interpolation=Inter.NEAREST, fill_value=0)[source]
Apply RandAugment data augmentation method on the input image.
Refer to RandAugment: Learning Augmentation Strategies from Data .
Only support 3-channel RGB image.
- Parameters
num_ops (int, optional) – Number of augmentation transformations to apply sequentially. Default:
2
.magnitude (int, optional) – Magnitude for all the transformations, must be smaller than num_magnitude_bins. Default:
9
.num_magnitude_bins (int, optional) – The number of different magnitude values, must be no less than 2. Default:
31
.interpolation (Inter, optional) – Image interpolation method defined by
Inter
. Default:Inter.NEAREST
.fill_value (Union[int, tuple[int, int, int]], optional) – Pixel fill value for the area outside the transformed image, must be in range of [0, 255]. Default:
0
. If int is provided, pad all RGB channels with this value. If tuple[int, int, int] is provided, pad R, G, B channels respectively.
- Raises
TypeError – If num_ops is not of type int.
ValueError – If num_ops is negative.
TypeError – If magnitude is not of type int.
ValueError – If magnitude is not positive.
TypeError – If num_magnitude_bins is not of type int.
ValueError – If num_magnitude_bins is less than 2.
TypeError – If fill_value is not of type int or tuple[int, int, int].
ValueError – If fill_value is not in range of [0, 255].
RuntimeError – If shape of the input image is not <H, W, C>.
- Supported Platforms:
CPU
Examples
>>> import mindspore.dataset as ds >>> import mindspore.dataset.vision as vision >>> >>> image_folder_dataset = ds.ImageFolderDataset("/path/to/image_folder_dataset_directory") >>> transforms_list = [vision.Decode(), vision.RandAugment()] >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, input_columns=["image"])
- Tutorial Examples: