Method __call__

monai/transforms/croppad/array.py:549–555

(self, img: torch.Tensor, lazy: bool | None = None)

        self.roi_scale = roi_scale

    def __call__(self, img: torch.Tensor, lazy: bool | None = None) -> torch.Tensor:  # type: ignore[override]
        img_size = img.peek_pending_shape() if isinstance(img, MetaTensor) else img.shape[1:]
        ndim = len(img_size)
        roi_size = [ceil(r * s) for r, s in zip(ensure_tuple_rep(self.roi_scale, ndim), img_size)]
        lazy_ = self.lazy if lazy is None else lazy
        cropper = CenterSpatialCrop(roi_size=roi_size, lazy=lazy_)
        return super().__call__(img=img, slices=cropper.compute_slices(img_size), lazy=lazy_)


class RandSpatialCrop(Randomizable, Crop):