Method randomize

monai/transforms/croppad/array.py:602–611

(self, img_size: Sequence[int])

        self._slices: tuple[slice, ...]

    def randomize(self, img_size: Sequence[int]) -> None:
        self._size = fall_back_tuple(self.roi_size, img_size)
        if self.random_size:
            max_size = img_size if self.max_roi_size is None else fall_back_tuple(self.max_roi_size, img_size)
            if any(i > j for i, j in zip(self._size, max_size)):
                raise ValueError(f"min ROI size: {self._size} is larger than max ROI size: {max_size}.")
            self._size = tuple(self.R.randint(low=self._size[i], high=max_size[i] + 1) for i in range(len(img_size)))
        if self.random_center:
            valid_size = get_valid_patch_size(img_size, self._size)
            self._slices = get_random_patch(img_size, valid_size, self.R)

    def __call__(self, img: torch.Tensor, randomize: bool = True, lazy: bool | None = None) -> torch.Tensor:  # type: ignore
        """