AutoAugment¶
- class torchvision.transforms.AutoAugment(policy: AutoAugmentPolicy = AutoAugmentPolicy.IMAGENET, interpolation: InterpolationMode = InterpolationMode.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
:Illustration of transforms