AutoAugment¶
-
class
torchvision.transforms.
AutoAugment
(policy: torchvision.transforms.autoaugment.AutoAugmentPolicy = <AutoAugmentPolicy.IMAGENET: 'imagenet'>, interpolation: torchvision.transforms.functional.InterpolationMode = <InterpolationMode.NEAREST: 'nearest'>, fill: Optional[List[float]] = None)[source]¶ AutoAugment data augmentation method based on “AutoAugment: Learning Augmentation Strategies from Data”. If the image is torch Tensor, it should be of type torch.uint8, and it is expected to have […, 1 or 3, H, W] shape, where … means an arbitrary number of leading dimensions. If img is PIL Image, it is expected to be in mode “L” or “RGB”.
- Parameters
policy (AutoAugmentPolicy) – Desired policy enum defined by
torchvision.transforms.autoaugment.AutoAugmentPolicy
. Default isAutoAugmentPolicy.IMAGENET
.interpolation (InterpolationMode) – Desired interpolation enum defined by
torchvision.transforms.InterpolationMode
. Default isInterpolationMode.NEAREST
. If input is Tensor, onlyInterpolationMode.NEAREST
,InterpolationMode.BILINEAR
are supported.fill (sequence or number, optional) – Pixel fill value for the area outside the transformed image. If given a number, the value is used for all bands respectively.
Examples using
AutoAugment
:-
forward
(img: torch.Tensor) → torch.Tensor[source]¶ img (PIL Image or Tensor): Image to be transformed.
- Returns
AutoAugmented image.
- Return type
PIL Image or Tensor
-
static
get_params
(transform_num: int) → Tuple[int, torch.Tensor, torch.Tensor][source]¶ Get parameters for autoaugment transformation
- Returns
params required by the autoaugment transformation