6ee3f1b0d2a6ebadc9e2f664354594342ab0805d,kornia/augmentation/augmentation.py,RandomErasing,__init__,#RandomErasing#Any#Any#Any#Any#Any#Any#,285

Before Change

            value: float = 0., return_transform: bool = False, same_on_batch: bool = False, p: float = 0.5
    ) -> None:
        super(RandomErasing, self).__init__(p=p, return_transform=return_transform, same_on_batch=same_on_batch)
        self.scale = cast(torch.Tensor, scale) if isinstance(scale, torch.Tensor) else torch.tensor(scale)
        self.ratio = cast(torch.Tensor, ratio) if isinstance(ratio, torch.Tensor) else torch.tensor(ratio)
        self.value: float = value

    def __repr__(self) -> str:

After Change

        self.size = size
        self.resample = Resample.get(resample)
        self.align_corners = align_corners
        self.flags: Dict[str, torch.Tensor] = dict(
            interpolation=torch.tensor(self.resample.value),
            align_corners=torch.tensor(align_corners)
        )

    def __repr__(self) -> str:
        repr = f"size={self.size}"
        return self.__class__.__name__ + f"({repr}, {super().__repr__()})"

Instances