Class Zoom

monai/transforms/spatial/array.py:1044–1178

Zooms an ND image using :py:class:`torch.nn.functional.interpolate`. For details, please see https://pytorch.org/docs/stable/generated/torch.nn.functional.interpolate.html. Different from :py:class:`monai.transforms.resize`, this transform takes scaling factors as input, and provid

class Zoom(InvertibleTransform, LazyTransform):
    """
    Zooms an ND image using :py:class:`torch.nn.functional.interpolate`.
    For details, please see https://pytorch.org/docs/stable/generated/torch.nn.functional.interpolate.html.

    Different from :py:class:`monai.transforms.resize`, this transform takes scaling factors
    as input, and provides an option of preserving the input spatial size.

    This transform is capable of lazy execution. See the :ref:`Lazy Resampling topic<lazy_resampling>`
    for more information.

    Args:
        zoom: The zoom factor along the spatial axes.
            If a float, zoom is the same for each spatial axis.
            If a sequence, zoom should contain one value for each spatial axis.
        mode: {``"nearest"``, ``"nearest-exact"``, ``"linear"``, ``"bilinear"``, ``"bicubic"``, ``"trilinear"``, ``"area"``}
            The interpolation mode. Defaults to ``"area"``.
            See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.interpolate.html
        padding_mode: available modes for numpy array:{``"constant"``, ``"edge"``, ``"linear_ramp"``, ``"maximum"``,
            ``"mean"``, ``"median"``, ``"minimum"``, ``"reflect"``, ``"symmetric"``, ``"wrap"``, ``"empty"``}
            available modes for PyTorch Tensor: {``"constant"``, ``"reflect"``, ``"replicate"``, ``"circular"``}.
            One of the listed string values or a user supplied function. Defaults to ``"edge"``.
            The mode to pad data after zooming.
            See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html
            https://pytorch.org/docs/stable/generated/torch.nn.functional.pad.html
        align_corners: This only has an effect when mode is
            'linear', 'bilinear', 'bicubic' or 'trilinear'. Default: None.
            See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.interpolate.html
        dtype: data type for resampling computation. Defaults to ``float32``.
            If None, use the data type of input data.
        keep_size: Should keep original size (padding/slicing if needed), default is True.
        lazy: a flag to indicate whether this transform should execute lazily or not.
            Defaults to False
        kwargs: other arguments for the `np.pad` or `torch.pad` function.
            note that `np.pad` treats channel dimension as the first dimension.
    """

    backend = [TransformBackends.TORCH]

    def __init__(
        self,
        zoom: Sequence[float] | float,
        mode: str = InterpolateMode.AREA,
        padding_mode: str = NumpyPadMode.EDGE,
        align_corners: bool | None = None,
        dtype: DtypeLike | torch.dtype = torch.float32,
        keep_size: bool = True,
        lazy: bool = False,
        **kwargs,
    ) -> None:
        LazyTransform.__init__(self, lazy=lazy)
        self.zoom = zoom
        self.mode = mode
        self.padding_mode = padding_mode
        self.align_corners = align_corners
        self.dtype = dtype
        self.keep_size = keep_size
        self.kwargs = kwargs