mindspore.dataset.vision.AutoAugment
- class mindspore.dataset.vision.AutoAugment(policy=AutoAugmentPolicy.IMAGENET, interpolation=Inter.NEAREST, fill_value=0)[source]
Apply AutoAugment data augmentation method based on AutoAugment: Learning Augmentation Strategies from Data . This operation works only with 3-channel RGB images.
- Parameters
policy (AutoAugmentPolicy, optional) –
AutoAugment policies learned on different datasets. Default:
AutoAugmentPolicy.IMAGENET
. It can beAutoAugmentPolicy.IMAGENET
,AutoAugmentPolicy.CIFAR10
,AutoAugmentPolicy.SVHN
. Randomly apply 2 operations from a candidate set. See auto augmentation details in AutoAugmentPolicy.AutoAugmentPolicy.IMAGENET
, means to apply AutoAugment learned on ImageNet dataset.AutoAugmentPolicy.CIFAR10
, means to apply AutoAugment learned on Cifar10 dataset.AutoAugmentPolicy.SVHN
, means to apply AutoAugment learned on SVHN dataset.
interpolation (Inter, optional) –
Image interpolation mode for Resize operation. Default:
Inter.NEAREST
. It can beInter.NEAREST
,Inter.BILINEAR
,Inter.BICUBIC
,Inter.AREA
.Inter.NEAREST
: means interpolation method is nearest-neighbor interpolation.Inter.BILINEA
: means interpolation method is bilinear interpolation.Inter.BICUBIC
: means the interpolation method is bicubic interpolation.Inter.AREA
: means the interpolation method is pixel area interpolation.
fill_value (Union[int, tuple[int]], optional) – Pixel fill value for the area outside the transformed image. It can be an int or a 3-tuple. If it is a 3-tuple, it is used to fill R, G, B channels respectively. If it is an integer, it is used for all RGB channels. The fill_value values must be in range [0, 255]. Default:
0
.
- Raises
TypeError – If policy is not of type
mindspore.dataset.vision.AutoAugmentPolicy
.TypeError – If interpolation is not of type
mindspore.dataset.vision.Inter
.TypeError – If fill_value is not an integer or a tuple of length 3.
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 >>> from mindspore.dataset.vision import AutoAugmentPolicy, Inter >>> >>> transforms_list = [vision.Decode(), vision.AutoAugment(policy=AutoAugmentPolicy.IMAGENET, ... interpolation=Inter.NEAREST, ... fill_value=0)] >>> >>> image_folder_dataset = ds.ImageFolderDataset("/path/to/image_folder_dataset_directory") >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, ... input_columns=["image"])
- Tutorial Examples: