Class RandZoomd

monai/transforms/spatial/dictionary.py:2038–2171

Dict-based version :py:class:`monai.transforms.RandZoom`. This transform is capable of lazy execution. See the :ref:`Lazy Resampling topic ` for more information. Args: keys: Keys to pick data for transformation. prob: Probability of zooming.

class RandZoomd(RandomizableTransform, MapTransform, InvertibleTransform, LazyTransform):
    """
    Dict-based version :py:class:`monai.transforms.RandZoom`.

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

    Args:
        keys: Keys to pick data for transformation.
        prob: Probability of zooming.
        min_zoom: Min zoom factor. Can be float or sequence same size as image.
            If a float, select a random factor from `[min_zoom, max_zoom]` then apply to all spatial dims
            to keep the original spatial shape ratio.
            If a sequence, min_zoom should contain one value for each spatial axis.
            If 2 values provided for 3D data, use the first value for both H & W dims to keep the same zoom ratio.
        max_zoom: Max zoom factor. Can be float or sequence same size as image.
            If a float, select a random factor from `[min_zoom, max_zoom]` then apply to all spatial dims
            to keep the original spatial shape ratio.
            If a sequence, max_zoom should contain one value for each spatial axis.
            If 2 values provided for 3D data, use the first value for both H & W dims to keep the same zoom ratio.
        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
            It also can be a sequence of string, each element corresponds to a key in ``keys``.
        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
            It also can be a sequence of bool or None, each element corresponds to a key in ``keys``.
        dtype: data type for resampling computation. Defaults to ``float32``.
            If None, use the data type of input data.
        keep_size: Should keep original size (pad if needed), default is True.
        allow_missing_keys: don&#x27;t raise exception if key is missing.
        lazy: a flag to indicate whether this transform should execute lazily or not.
            Defaults to False
        kwargs: other args for `np.pad` API, note that `np.pad` treats channel dimension as the first dimension.
            more details: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html
    """

    backend = RandZoom.backend

    def __init__(
        self,
        keys: KeysCollection,
        prob: float = 0.1,
        min_zoom: Sequence[float] | float = 0.9,
        max_zoom: Sequence[float] | float = 1.1,
        mode: SequenceStr = InterpolateMode.AREA,
        padding_mode: SequenceStr = NumpyPadMode.EDGE,
        align_corners: Sequence[bool | None] | bool | None = None,
        dtype: Sequence[DtypeLike | torch.dtype] | DtypeLike | torch.dtype = np.float32,
        keep_size: bool = True,