testbed/Project-MONAI__MONAI/monai/transforms/spatial/array.py

# Copyright 2020 MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#     http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
A collection of "vanilla" transforms for spatial operations
https://github.com/Project-MONAI/MONAI/wiki/MONAI_Design
"""

import warnings
from typing import Any, Callable, List, Optional, Sequence, Tuple, Union

import numpy as np
import torch

from monai.config import get_torch_version_tuple
from monai.data.utils import compute_shape_offset, to_affine_nd, zoom_affine
from monai.networks.layers import AffineTransform, GaussianFilter
from monai.transforms.compose import Randomizable, Transform
from monai.transforms.croppad.array import CenterSpatialCrop
from monai.transforms.utils import (
    create_control_grid,
    create_grid,
    create_rotate,
    create_scale,
    create_shear,
    create_translate,
)
from monai.utils import (
    GridSampleMode,
    GridSamplePadMode,
    InterpolateMode,
    NumpyPadMode,
    ensure_tuple,
    ensure_tuple_rep,
    ensure_tuple_size,
    fall_back_tuple,
    optional_import,
)

nib, _ = optional_import("nibabel")

_torch_interp: Callable[..., torch.Tensor]

if get_torch_version_tuple() >= (1, 5):
    # additional argument since torch 1.5 (to avoid warnings)
    def _torch_interp(**kwargs):
        return torch.nn.functional.interpolate(recompute_scale_factor=True, **kwargs)


else:
    _torch_interp = torch.nn.functional.interpolate


class Spacing(Transform):
    """
    Resample input image into the specified `pixdim`.
    """

    def __init__(
        self,
        pixdim: Union[Sequence[float], float],
        diagonal: bool = False,
        mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,
        padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER,
        align_corners: bool = False,
        dtype: Optional[np.dtype] = np.float64,
    ) -> None:
        """
        Args:
            pixdim: output voxel spacing.
            diagonal: whether to resample the input to have a diagonal affine matrix.
                If True, the input data is resampled to the following affine::

                    np.diag((pixdim_0, pixdim_1, ..., pixdim_n, 1))

                This effectively resets the volume to the world coordinate system (RAS+ in nibabel).
                The original orientation, rotation, shearing are not preserved.

                If False, this transform preserves the axes orientation, orthogonal rotation and
                translation components from the original affine. This option will not flip/swap axes
                of the original data.
            mode: {``"bilinear"``, ``"nearest"``}
                Interpolation mode to calculate output values. Defaults to ``"bilinear"``.
                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
            padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
                Padding mode for outside grid values. Defaults to ``"border"``.
                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
            align_corners: Geometrically, we consider the pixels of the input as squares rather than points.
                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
            dtype: data type for resampling computation. Defaults to ``np.float64`` for best precision.
                If None, use the data type of input data. To be compatible with other modules,
                the output data type is always ``np.float32``.
        """
        self.pixdim = np.array(ensure_tuple(pixdim), dtype=np.float64)
        self.diagonal = diagonal
        self.mode: GridSampleMode = GridSampleMode(mode)
        self.padding_mode: GridSamplePadMode = GridSamplePadMode(padding_mode)
        self.align_corners = align_corners
        self.dtype = dtype

    def __call__(
        self,
        data_array: np.ndarray,
        affine: Optional[np.ndarray] = None,
        mode: Optional[Union[GridSampleMode, str]] = None,
        padding_mode: Optional[Union[GridSamplePadMode, str]] = None,
        align_corners: Optional[bool] = None,
        dtype: Optional[np.dtype] = None,
    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
        """
        Args:
            data_array: in shape (num_channels, H[, W, ...]).
            affine (matrix): (N+1)x(N+1) original affine matrix for spatially ND `data_array`. Defaults to identity.
            mode: {``"bilinear"``, ``"nearest"``}
                Interpolation mode to calculate output values. Defaults to ``self.mode``.
                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
            padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
                Padding mode for outside grid values. Defaults to ``self.padding_mode``.
                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
            align_corners: Geometrically, we consider the pixels of the input as squares rather than points.
                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
            dtype: data type for resampling computation. Defaults to ``self.dtype``.
                If None, use the data type of input data. To be compatible with other modules,
                the output data type is always ``np.float32``.

        Raises:
            ValueError: When ``data_array`` has no spatial dimensions.
            ValueError: When ``pixdim`` is nonpositive.

        Returns:
            data_array (resampled into `self.pixdim`), original pixdim, current pixdim.

        """
        _dtype = dtype or self.dtype or data_array.dtype
        sr = data_array.ndim - 1
        if sr <= 0:
            raise ValueError("data_array must have at least one spatial dimension.")
        if affine is None:
            # default to identity
            affine = np.eye(sr + 1, dtype=np.float64)
            affine_ = np.eye(sr + 1, dtype=np.float64)
        else:
            affine_ = to_affine_nd(sr, affine)
        out_d = self.pixdim[:sr]
        if out_d.size < sr:
            out_d = np.append(out_d, [1.0] * (out_d.size - sr))
        if np.any(out_d <= 0):
            raise ValueError(f"pixdim must be positive, got {out_d}.")
        # compute output affine, shape and offset
        new_affine = zoom_affine(affine_, out_d, diagonal=self.diagonal)
        output_shape, offset = compute_shape_offset(data_array.shape[1:], affine_, new_affine)
        new_affine[:sr, -1] = offset[:sr]
        transform = np.linalg.inv(affine_) @ new_affine
        # adapt to the actual rank
        transform = to_affine_nd(sr, transform)

        # no resampling if it's identity transform
        if np.allclose(transform, np.diag(np.ones(len(transform))), atol=1e-3):
            output_data = data_array.copy().astype(np.float32)
            new_affine = to_affine_nd(affine, new_affine)
            return output_data, affine, new_affine

        # resample
        affine_xform = AffineTransform(
            normalized=False,
            mode=mode or self.mode,
            padding_mode=padding_mode or self.padding_mode,
            align_corners=self.align_corners if align_corners is None else align_corners,
            reverse_indexing=True,
        )
        output_data = affine_xform(
            # AffineTransform requires a batch dim
            torch.as_tensor(np.ascontiguousarray(data_array).astype(_dtype)).unsqueeze(0),
            torch.as_tensor(np.ascontiguousarray(transform).astype(_dtype)),
            spatial_size=output_shape,
        )
        output_data = output_data.squeeze(0).detach().cpu().numpy().astype(np.float32)
        new_affine = to_affine_nd(affine, new_affine)
        return output_data, affine, new_affine


class Orientation(Transform):
    """
    Change the input image's orientation into the specified based on `axcodes`.
    """

    def __init__(
        self,
        axcodes: Optional[str] = None,
        as_closest_canonical: bool = False,
        labels: Optional[Sequence[Tuple[str, str]]] = tuple(zip("LPI", "RAS")),
    ) -> None:
        """
        Args:
            axcodes: N elements sequence for spatial ND input's orientation.
                e.g. axcodes='RAS' represents 3D orientation:
                (Left, Right), (Posterior, Anterior), (Inferior, Superior).
                default orientation labels options are: 'L' and 'R' for the first dimension,
                'P' and 'A' for the second, 'I' and 'S' for the third.
            as_closest_canonical: if True, load the image as closest to canonical axis format.
            labels: optional, None or sequence of (2,) sequences
                (2,) sequences are labels for (beginning, end) of output axis.
                Defaults to ``(('L', 'R'), ('P', 'A'), ('I', 'S'))``.

        Raises:
            ValueError: When ``axcodes=None`` and ``as_closest_canonical=True``. Incompatible values.

        See Also: `nibabel.orientations.ornt2axcodes`.

        """
        if axcodes is None and not as_closest_canonical:
            raise ValueError("Incompatible values: axcodes=None and as_closest_canonical=True.")
        if axcodes is not None and as_closest_canonical:
            warnings.warn("using as_closest_canonical=True, axcodes ignored.")
        self.axcodes = axcodes
        self.as_closest_canonical = as_closest_canonical
        self.labels = labels

    def __call__(
        self, data_array: np.ndarray, affine: Optional[np.ndarray] = None
    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
        """
        original orientation of `data_array` is defined by `affine`.

        Args:
            data_array: in shape (num_channels, H[, W, ...]).
            affine (matrix): (N+1)x(N+1) original affine matrix for spatially ND `data_array`. Defaults to identity.

        Raises:
            ValueError: When ``data_array`` has no spatial dimensions.
            ValueError: When ``axcodes`` spatiality differs from ``data_array``.

        Returns:
            data_array (reoriented in `self.axcodes`), original axcodes, current axcodes.

        """
        sr = data_array.ndim - 1
        if sr <= 0:
            raise ValueError("data_array must have at least one spatial dimension.")
        if affine is None:
            affine = np.eye(sr + 1, dtype=np.float64)
            affine_ = np.eye(sr + 1, dtype=np.float64)
        else:
            affine_ = to_affine_nd(sr, affine)
        src = nib.io_orientation(affine_)
        if self.as_closest_canonical:
            spatial_ornt = src
        else:
            assert self.axcodes is not None
            dst = nib.orientations.axcodes2ornt(self.axcodes[:sr], labels=self.labels)
            if len(dst) < sr:
                raise ValueError(
                    f"axcodes must match data_array spatially, got axcodes={len(self.axcodes)}D data_array={sr}D"
                )
            spatial_ornt = nib.orientations.ornt_transform(src, dst)
        ornt = spatial_ornt.copy()
        ornt[:, 0] += 1  # skip channel dim
        ornt = np.concatenate([np.array([[0, 1]]), ornt])
        shape = data_array.shape[1:]
        data_array = nib.orientations.apply_orientation(data_array, ornt)
        new_affine = affine_ @ nib.orientations.inv_ornt_aff(spatial_ornt, shape)
        new_affine = to_affine_nd(affine, new_affine)
        return data_array, affine, new_affine


class Flip(Transform):
    """
    Reverses the order of elements along the given spatial axis. Preserves shape.
    Uses ``np.flip`` in practice. See numpy.flip for additional details.
    https://docs.scipy.org/doc/numpy/reference/generated/numpy.flip.html

    Args:
        spatial_axis: spatial axes along which to flip over. Default is None.
    """

    def __init__(self, spatial_axis: Optional[Union[Sequence[int], int]]) -> None:
        self.spatial_axis = spatial_axis

    def __call__(self, img: np.ndarray) -> np.ndarray:
        """
        Args:
            img: channel first array, must have shape: (num_channels, H[, W, ..., ]),
        """
        flipped = list()
        for channel in img:
            flipped.append(np.flip(channel, self.spatial_axis))
        return np.stack(flipped).astype(img.dtype)


class Resize(Transform):
    """
    Resize the input image to given spatial size.
    Implemented using :py:class:`torch.nn.functional.interpolate`.

    Args:
        spatial_size: expected shape of spatial dimensions after resize operation.
            if the components of the `spatial_size` are non-positive values, the transform will use the
            corresponding components of img size. For example, `spatial_size=(32, -1)` will be adapted
            to `(32, 64)` if the second spatial dimension size of img is `64`.
        mode: {``"nearest"``, ``"linear"``, ``"bilinear"``, ``"bicubic"``, ``"trilinear"``, ``"area"``}
            The interpolation mode. Defaults to ``"area"``.
            See also: https://pytorch.org/docs/stable/nn.functional.html#interpolate
        align_corners: This only has an effect when mode is
            'linear', 'bilinear', 'bicubic' or 'trilinear'. Default: None.
            See also: https://pytorch.org/docs/stable/nn.functional.html#interpolate
    """

    def __init__(
        self,
        spatial_size: Union[Sequence[int], int],
        mode: Union[InterpolateMode, str] = InterpolateMode.AREA,
        align_corners: Optional[bool] = None,
    ) -> None:
        self.spatial_size = ensure_tuple(spatial_size)
        self.mode: InterpolateMode = InterpolateMode(mode)
        self.align_corners = align_corners

    def __call__(
        self,
        img: np.ndarray,
        mode: Optional[Union[InterpolateMode, str]] = None,
        align_corners: Optional[bool] = None,
    ) -> np.ndarray:
        """
        Args:
            img: channel first array, must have shape: (num_channels, H[, W, ..., ]).
            mode: {``"nearest"``, ``"linear"``, ``"bilinear"``, ``"bicubic"``, ``"trilinear"``, ``"area"``}
                The interpolation mode. Defaults to ``self.mode``.
                See also: https://pytorch.org/docs/stable/nn.functional.html#interpolate
            align_corners: This only has an effect when mode is
                'linear', 'bilinear', 'bicubic' or 'trilinear'. Defaults to ``self.align_corners``.
                See also: https://pytorch.org/docs/stable/nn.functional.html#interpolate

        Raises:
            ValueError: When ``self.spatial_size`` length is less than ``img`` spatial dimensions.

        """
        input_ndim = img.ndim - 1  # spatial ndim
        output_ndim = len(self.spatial_size)
        if output_ndim > input_ndim:
            input_shape = ensure_tuple_size(img.shape, output_ndim + 1, 1)
            img = img.reshape(input_shape)
        elif output_ndim < input_ndim:
            raise ValueError(
                "len(spatial_size) must be greater or equal to img spatial dimensions, "
                f"got spatial_size={output_ndim} img={input_ndim}."
            )
        spatial_size = fall_back_tuple(self.spatial_size, img.shape[1:])
        resized = _torch_interp(
            input=torch.as_tensor(np.ascontiguousarray(img), dtype=torch.float).unsqueeze(0),
            size=spatial_size,
            mode=self.mode.value if mode is None else InterpolateMode(mode).value,
            align_corners=self.align_corners if align_corners is None else align_corners,
        )
        resized = resized.squeeze(0).detach().cpu().numpy()
        return resized


class Rotate(Transform):
    """
    Rotates an input image by given angle using :py:class:`monai.networks.layers.AffineTransform`.

    Args:
        angle: Rotation angle(s) in degrees. should a float for 2D, three floats for 3D.
        keep_size: If it is True, the output shape is kept the same as the input.
            If it is False, the output shape is adapted so that the
            input array is contained completely in the output. Default is True.
        mode: {``"bilinear"``, ``"nearest"``}
            Interpolation mode to calculate output values. Defaults to ``"bilinear"``.
            See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
        padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
            Padding mode for outside grid values. Defaults to ``"border"``.
            See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
        align_corners: Defaults to False.
            See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
        dtype: data type for resampling computation. Defaults to ``np.float64`` for best precision.
            If None, use the data type of input data. To be compatible with other modules,
            the output data type is always ``np.float32``.
    """

    def __init__(
        self,
        angle: Union[Sequence[float], float],
        keep_size: bool = True,
        mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,
        padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER,
        align_corners: bool = False,
        dtype: Optional[np.dtype] = np.float64,
    ) -> None:
        self.angle = angle
        self.keep_size = keep_size
        self.mode: GridSampleMode = GridSampleMode(mode)
        self.padding_mode: GridSamplePadMode = GridSamplePadMode(padding_mode)
        self.align_corners = align_corners
        self.dtype = dtype

    def __call__(
        self,
        img: np.ndarray,
        mode: Optional[Union[GridSampleMode, str]] = None,
        padding_mode: Optional[Union[GridSamplePadMode, str]] = None,
        align_corners: Optional[bool] = None,
        dtype: Optional[np.dtype] = None,
    ) -> np.ndarray:
        """
        Args:
            img: channel first array, must have shape: [chns, H, W] or [chns, H, W, D].
            mode: {``"bilinear"``, ``"nearest"``}
                Interpolation mode to calculate output values. Defaults to ``self.mode``.
                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
            padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
                Padding mode for outside grid values. Defaults to ``self.padding_mode``.
                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
                align_corners: Defaults to ``self.align_corners``.
                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
            align_corners: Defaults to ``self.align_corners``.
                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
            dtype: data type for resampling computation. Defaults to ``self.dtype``.
                If None, use the data type of input data. To be compatible with other modules,
                the output data type is always ``np.float32``.

        Raises:
            ValueError: When ``img`` spatially is not one of [2D, 3D].

        """
        _dtype = dtype or self.dtype or img.dtype
        im_shape = np.asarray(img.shape[1:])  # spatial dimensions
        input_ndim = len(im_shape)
        if input_ndim not in (2, 3):
            raise ValueError(f"Unsupported img dimension: {input_ndim}, available options are [2, 3].")
        _angle = ensure_tuple_rep(self.angle, 1 if input_ndim == 2 else 3)
        _rad = np.deg2rad(_angle)
        transform = create_rotate(input_ndim, _rad)
        shift = create_translate(input_ndim, (im_shape - 1) / 2)
        if self.keep_size:
            output_shape = im_shape
        else:
            corners = np.asarray(np.meshgrid(*[(0, dim) for dim in im_shape], indexing="ij")).reshape(
                (len(im_shape), -1)
            )
            corners = transform[:-1, :-1] @ corners
            output_shape = (corners.ptp(axis=1) + 0.5).astype(int)
        shift_1 = create_translate(input_ndim, -(output_shape - 1) / 2)
        transform = shift @ transform @ shift_1

        xform = AffineTransform(
            normalized=False,
            mode=mode or self.mode,
            padding_mode=padding_mode or self.padding_mode,
            align_corners=self.align_corners if align_corners is None else align_corners,
            reverse_indexing=True,
        )
        output = xform(
            torch.as_tensor(np.ascontiguousarray(img).astype(_dtype)).unsqueeze(0),
            torch.as_tensor(np.ascontiguousarray(transform).astype(_dtype)),
            spatial_size=output_shape,
        )
        output = output.squeeze(0).detach().cpu().numpy().astype(np.float32)
        return output


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

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

    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"``, ``"linear"``, ``"bilinear"``, ``"bicubic"``, ``"trilinear"``, ``"area"``}
            The interpolation mode. Defaults to ``"area"``.
            See also: https://pytorch.org/docs/stable/nn.functional.html#interpolate
        align_corners: This only has an effect when mode is
            'linear', 'bilinear', 'bicubic' or 'trilinear'. Default: None.
            See also: https://pytorch.org/docs/stable/nn.functional.html#interpolate
        keep_size: Should keep original size (padding/slicing if needed), default is True.
    """

    def __init__(
        self,
        zoom: Union[Sequence[float], float],
        mode: Union[InterpolateMode, str] = InterpolateMode.AREA,
        align_corners: Optional[bool] = None,
        keep_size: bool = True,
    ) -> None:
        self.zoom = zoom
        self.mode: InterpolateMode = InterpolateMode(mode)
        self.align_corners = align_corners
        self.keep_size = keep_size

    def __call__(
        self,
        img: np.ndarray,
        mode: Optional[Union[InterpolateMode, str]] = None,
        align_corners: Optional[bool] = None,
    ) -> np.ndarray:
        """
        Args:
            img: channel first array, must have shape: (num_channels, H[, W, ..., ]).
            mode: {``"nearest"``, ``"linear"``, ``"bilinear"``, ``"bicubic"``, ``"trilinear"``, ``"area"``}
                The interpolation mode. Defaults to ``self.mode``.
                See also: https://pytorch.org/docs/stable/nn.functional.html#interpolate
            align_corners: This only has an effect when mode is
                'linear', 'bilinear', 'bicubic' or 'trilinear'. Defaults to ``self.align_corners``.
                See also: https://pytorch.org/docs/stable/nn.functional.html#interpolate

        """
        _zoom = ensure_tuple_rep(self.zoom, img.ndim - 1)  # match the spatial image dim
        zoomed = _torch_interp(
            input=torch.as_tensor(np.ascontiguousarray(img), dtype=torch.float).unsqueeze(0),
            scale_factor=list(_zoom),
            mode=self.mode.value if mode is None else InterpolateMode(mode).value,
            align_corners=self.align_corners if align_corners is None else align_corners,
        )
        zoomed = zoomed.squeeze(0).detach().cpu().numpy()
        if not self.keep_size or np.allclose(img.shape, zoomed.shape):
            return zoomed

        pad_vec = [[0, 0]] * len(img.shape)
        slice_vec = [slice(None)] * len(img.shape)
        for idx, (od, zd) in enumerate(zip(img.shape, zoomed.shape)):
            diff = od - zd
            half = abs(diff) // 2
            if diff > 0:  # need padding
                pad_vec[idx] = [half, diff - half]
            elif diff < 0:  # need slicing
                slice_vec[idx] = slice(half, half + od)
        zoomed = np.pad(zoomed, pad_vec, mode=NumpyPadMode.EDGE.value)
        return zoomed[tuple(slice_vec)]


class Rotate90(Transform):
    """
    Rotate an array by 90 degrees in the plane specified by `axes`.
    """

    def __init__(self, k: int = 1, spatial_axes: Tuple[int, int] = (0, 1)) -> None:
        """
        Args:
            k: number of times to rotate by 90 degrees.
            spatial_axes: 2 int numbers, defines the plane to rotate with 2 spatial axes.
                Default: (0, 1), this is the first two axis in spatial dimensions.
        """
        self.k = k
        self.spatial_axes = spatial_axes

    def __call__(self, img: np.ndarray) -> np.ndarray:
        """
        Args:
            img: channel first array, must have shape: (num_channels, H[, W, ..., ]),
        """
        rotated = list()
        for channel in img:
            rotated.append(np.rot90(channel, self.k, self.spatial_axes))
        return np.stack(rotated).astype(img.dtype)


class RandRotate90(Randomizable, Transform):
    """
    With probability `prob`, input arrays are rotated by 90 degrees
    in the plane specified by `spatial_axes`.
    """

    def __init__(self, prob: float = 0.1, max_k: int = 3, spatial_axes: Tuple[int, int] = (0, 1)) -> None:
        """
        Args:
            prob: probability of rotating.
                (Default 0.1, with 10% probability it returns a rotated array)
            max_k: number of rotations will be sampled from `np.random.randint(max_k) + 1`, (Default 3).
            spatial_axes: 2 int numbers, defines the plane to rotate with 2 spatial axes.
                Default: (0, 1), this is the first two axis in spatial dimensions.
        """
        self.prob = min(max(prob, 0.0), 1.0)
        self.max_k = max_k
        self.spatial_axes = spatial_axes

        self._do_transform = False
        self._rand_k = 0

    def randomize(self, data: Optional[Any] = None) -> None:
        self._rand_k = self.R.randint(self.max_k) + 1
        self._do_transform = self.R.random() < self.prob

    def __call__(self, img: np.ndarray) -> np.ndarray:
        """
        Args:
            img: channel first array, must have shape: (num_channels, H[, W, ..., ]),
        """
        self.randomize()
        if not self._do_transform:
            return img
        rotator = Rotate90(self._rand_k, self.spatial_axes)
        return rotator(img)


class RandRotate(Randomizable, Transform):
    """
    Randomly rotate the input arrays.

    Args:
        range_x: Range of rotation angle in degrees in the plane defined by the first and second axes.
            If single number, angle is uniformly sampled from (-range_x, range_x).
        range_y: Range of rotation angle in degrees in the plane defined by the first and third axes.
            If single number, angle is uniformly sampled from (-range_y, range_y).
        range_z: Range of rotation angle in degrees in the plane defined by the second and third axes.
            If single number, angle is uniformly sampled from (-range_z, range_z).
        prob: Probability of rotation.
        keep_size: If it is False, the output shape is adapted so that the
            input array is contained completely in the output.
            If it is True, the output shape is the same as the input. Default is True.
        mode: {``"bilinear"``, ``"nearest"``}
            Interpolation mode to calculate output values. Defaults to ``"bilinear"``.
            See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
        padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
            Padding mode for outside grid values. Defaults to ``"border"``.
            See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
        align_corners: Defaults to False.
            See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
        dtype: data type for resampling computation. Defaults to ``np.float64`` for best precision.
            If None, use the data type of input data. To be compatible with other modules,
            the output data type is always ``np.float32``.
    """

    def __init__(
        self,
        range_x: Union[Tuple[float, float], float] = 0.0,
        range_y: Union[Tuple[float, float], float] = 0.0,
        range_z: Union[Tuple[float, float], float] = 0.0,
        prob: float = 0.1,
        keep_size: bool = True,
        mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,
        padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER,
        align_corners: bool = False,
        dtype: Optional[np.dtype] = np.float64,
    ) -> None:
        self.range_x = ensure_tuple(range_x)
        if len(self.range_x) == 1:
            self.range_x = tuple(sorted([-self.range_x[0], self.range_x[0]]))
        self.range_y = ensure_tuple(range_y)
        if len(self.range_y) == 1:
            self.range_y = tuple(sorted([-self.range_y[0], self.range_y[0]]))
        self.range_z = ensure_tuple(range_z)
        if len(self.range_z) == 1:
            self.range_z = tuple(sorted([-self.range_z[0], self.range_z[0]]))

        self.prob = prob
        self.keep_size = keep_size
        self.mode: GridSampleMode = GridSampleMode(mode)
        self.padding_mode: GridSamplePadMode = GridSamplePadMode(padding_mode)
        self.align_corners = align_corners
        self.dtype = dtype

        self._do_transform = False
        self.x = 0.0
        self.y = 0.0
        self.z = 0.0

    def randomize(self, data: Optional[Any] = None) -> None:
        self._do_transform = self.R.random_sample() < self.prob
        self.x = self.R.uniform(low=self.range_x[0], high=self.range_x[1])
        self.y = self.R.uniform(low=self.range_y[0], high=self.range_y[1])
        self.z = self.R.uniform(low=self.range_z[0], high=self.range_z[1])

    def __call__(
        self,
        img: np.ndarray,
        mode: Optional[Union[GridSampleMode, str]] = None,
        padding_mode: Optional[Union[GridSamplePadMode, str]] = None,
        align_corners: Optional[bool] = None,
        dtype: Optional[np.dtype] = None,
    ) -> np.ndarray:
        """
        Args:
            img: channel first array, must have shape 2D: (nchannels, H, W), or 3D: (nchannels, H, W, D).
            mode: {``"bilinear"``, ``"nearest"``}
                Interpolation mode to calculate output values. Defaults to ``self.mode``.
                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
            padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
                Padding mode for outside grid values. Defaults to ``self.padding_mode``.
                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
            align_corners: Defaults to ``self.align_corners``.
                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
            dtype: data type for resampling computation. Defaults to ``self.dtype``.
                If None, use the data type of input data. To be compatible with other modules,
                the output data type is always ``np.float32``.
        """
        self.randomize()
        if not self._do_transform:
            return img
        rotator = Rotate(
            angle=self.x if img.ndim == 3 else (self.x, self.y, self.z),
            keep_size=self.keep_size,
            mode=mode or self.mode,
            padding_mode=padding_mode or self.padding_mode,
            align_corners=self.align_corners if align_corners is None else align_corners,
            dtype=dtype or self.dtype or img.dtype,
        )
        return rotator(img)


class RandFlip(Randomizable, Transform):
    """
    Randomly flips the image along axes. Preserves shape.
    See numpy.flip for additional details.
    https://docs.scipy.org/doc/numpy/reference/generated/numpy.flip.html

    Args:
        prob: Probability of flipping.
        spatial_axis: Spatial axes along which to flip over. Default is None.
    """

    def __init__(self, prob: float = 0.1, spatial_axis: Optional[Union[Sequence[int], int]] = None) -> None:
        self.prob = prob
        self.flipper = Flip(spatial_axis=spatial_axis)
        self._do_transform = False

    def randomize(self, data: Optional[Any] = None) -> None:
        self._do_transform = self.R.random_sample() < self.prob

    def __call__(self, img: np.ndarray) -> np.ndarray:
        """
        Args:
            img: channel first array, must have shape: (num_channels, H[, W, ..., ]),
        """
        self.randomize()
        if not self._do_transform:
            return img
        return self.flipper(img)


class RandZoom(Randomizable, Transform):
    """
    Randomly zooms input arrays with given probability within given zoom range.

    Args:
        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.
        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.
        mode: {``"nearest"``, ``"linear"``, ``"bilinear"``, ``"bicubic"``, ``"trilinear"``, ``"area"``}
            The interpolation mode. Defaults to ``"area"``.
            See also: https://pytorch.org/docs/stable/nn.functional.html#interpolate
        align_corners: This only has an effect when mode is
            'linear', 'bilinear', 'bicubic' or 'trilinear'. Default: None.
            See also: https://pytorch.org/docs/stable/nn.functional.html#interpolate
        keep_size: Should keep original size (pad if needed), default is True.
    """

    def __init__(
        self,
        prob: float = 0.1,
        min_zoom: Union[Sequence[float], float] = 0.9,
        max_zoom: Union[Sequence[float], float] = 1.1,
        mode: Union[InterpolateMode, str] = InterpolateMode.AREA,
        align_corners: Optional[bool] = None,
        keep_size: bool = True,
    ) -> None:
        self.min_zoom = ensure_tuple(min_zoom)
        self.max_zoom = ensure_tuple(max_zoom)
        assert len(self.min_zoom) == len(self.max_zoom), "min_zoom and max_zoom must have same length."
        self.prob = prob
        self.mode: InterpolateMode = InterpolateMode(mode)
        self.align_corners = align_corners
        self.keep_size = keep_size

        self._do_transform = False
        self._zoom: Union[List[float], float] = 1.0

    def randomize(self, data: Optional[Any] = None) -> None:
        self._do_transform = self.R.random_sample() < self.prob
        self._zoom = [self.R.uniform(l, h) for l, h in zip(self.min_zoom, self.max_zoom)]
        if len(self._zoom) == 1:
            # to keep the spatial shape ratio, use same random zoom factor for all dims
            self._zoom = self._zoom[0]

    def __call__(
        self,
        img: np.ndarray,
        mode: Optional[Union[InterpolateMode, str]] = None,
        align_corners: Optional[bool] = None,
    ) -> np.ndarray:
        """
        Args:
            img: channel first array, must have shape 2D: (nchannels, H, W), or 3D: (nchannels, H, W, D).
            mode: {``"nearest"``, ``"linear"``, ``"bilinear"``, ``"bicubic"``, ``"trilinear"``, ``"area"``}
                The interpolation mode. Defaults to ``self.mode``.
                See also: https://pytorch.org/docs/stable/nn.functional.html#interpolate
            align_corners: This only has an effect when mode is
                'linear', 'bilinear', 'bicubic' or 'trilinear'. Defaults to ``self.align_corners``.
                See also: https://pytorch.org/docs/stable/nn.functional.html#interpolate
        """
        # match the spatial image dim
        self.randomize()
        _dtype = np.float32
        if not self._do_transform:
            return img.astype(_dtype)
        zoomer = Zoom(self._zoom, keep_size=self.keep_size)
        return zoomer(
            img,
            mode=mode or self.mode,
            align_corners=self.align_corners if align_corners is None else align_corners,
        ).astype(_dtype)


class AffineGrid(Transform):
    """
    Affine transforms on the coordinates.

    Args:
        rotate_range: angle range in radians. rotate_range[0] with be used to generate the 1st rotation
            parameter from `uniform[-rotate_range[0], rotate_range[0])`. Similarly, `rotate_range[1]` and
            `rotate_range[2]` are used in 3D affine for the range of 2nd and 3rd axes.
        shear_range: shear_range[0] with be used to generate the 1st shearing parameter from
            `uniform[-shear_range[0], shear_range[0])`. Similarly, `shear_range[1]` to
            `shear_range[N]` controls the range of the uniform distribution used to generate the 2nd to
            N-th parameter.
        translate_range : translate_range[0] with be used to generate the 1st shift parameter from
            `uniform[-translate_range[0], translate_range[0])`. Similarly, `translate_range[1]`
            to `translate_range[N]` controls the range of the uniform distribution used to generate
            the 2nd to N-th parameter.
        scale_range: scaling_range[0] with be used to generate the 1st scaling factor from
            `uniform[-scale_range[0], scale_range[0]) + 1.0`. Similarly, `scale_range[1]` to
            `scale_range[N]` controls the range of the uniform distribution used to generate the 2nd to
            N-th parameter.
        as_tensor_output: whether to output tensor instead of numpy array.
            defaults to True.
        device: device to store the output grid data.

    """

    def __init__(
        self,
        rotate_params: Optional[Union[Sequence[float], float]] = None,
        shear_params: Optional[Union[Sequence[float], float]] = None,
        translate_params: Optional[Union[Sequence[float], float]] = None,
        scale_params: Optional[Union[Sequence[float], float]] = None,
        as_tensor_output: bool = True,
        device: Optional[torch.device] = None,
    ) -> None:
        self.rotate_params = rotate_params
        self.shear_params = shear_params
        self.translate_params = translate_params
        self.scale_params = scale_params

        self.as_tensor_output = as_tensor_output
        self.device = device

    def __call__(
        self, spatial_size: Optional[Sequence[int]] = None, grid: Optional[Union[np.ndarray, torch.Tensor]] = None
    ) -> Union[np.ndarray, torch.Tensor]:
        """
        Args:
            spatial_size: output grid size.
            grid: grid to be transformed. Shape must be (3, H, W) for 2D or (4, H, W, D) for 3D.

        Raises:
            ValueError: When ``grid=None`` and ``spatial_size=None``. Incompatible values.

        """
        if grid is None:
            if spatial_size is not None:
                grid = create_grid(spatial_size)
            else:
                raise ValueError("Incompatible values: grid=None and spatial_size=None.")

        spatial_dims = len(grid.shape) - 1
        affine = np.eye(spatial_dims + 1)
        if self.rotate_params:
            affine = affine @ create_rotate(spatial_dims, self.rotate_params)
        if self.shear_params:
            affine = affine @ create_shear(spatial_dims, self.shear_params)
        if self.translate_params:
            affine = affine @ create_translate(spatial_dims, self.translate_params)
        if self.scale_params:
            affine = affine @ create_scale(spatial_dims, self.scale_params)
        affine = torch.as_tensor(np.ascontiguousarray(affine), device=self.device)

        grid = torch.tensor(grid) if not torch.is_tensor(grid) else grid.detach().clone()
        if self.device:
            grid = grid.to(self.device)
        grid = (affine.float() @ grid.reshape((grid.shape[0], -1)).float()).reshape([-1] + list(grid.shape[1:]))
        if self.as_tensor_output:
            return grid
        return grid.cpu().numpy()


class RandAffineGrid(Randomizable, Transform):
    """
    Generate randomised affine grid.
    """

    def __init__(
        self,
        rotate_range: Optional[Union[Sequence[float], float]] = None,
        shear_range: Optional[Union[Sequence[float], float]] = None,
        translate_range: Optional[Union[Sequence[float], float]] = None,
        scale_range: Optional[Union[Sequence[float], float]] = None,
        as_tensor_output: bool = True,
        device: Optional[torch.device] = None,
    ) -> None:
        """
        Args:
            rotate_range: angle range in radians. rotate_range[0] with be used to generate the 1st rotation
                parameter from `uniform[-rotate_range[0], rotate_range[0])`. Similarly, `rotate_range[1]` and
                `rotate_range[2]` are used in 3D affine for the range of 2nd and 3rd axes.
            shear_range: shear_range[0] with be used to generate the 1st shearing parameter from
                `uniform[-shear_range[0], shear_range[0])`. Similarly, `shear_range[1]` to
                `shear_range[N]` controls the range of the uniform distribution used to generate the 2nd to
                N-th parameter.
            translate_range : translate_range[0] with be used to generate the 1st shift parameter from
                `uniform[-translate_range[0], translate_range[0])`. Similarly, `translate_range[1]`
                to `translate_range[N]` controls the range of the uniform distribution used to generate
                the 2nd to N-th parameter.
            scale_range: scaling_range[0] with be used to generate the 1st scaling factor from
                `uniform[-scale_range[0], scale_range[0]) + 1.0`. Similarly, `scale_range[1]` to
                `scale_range[N]` controls the range of the uniform distribution used to generate the 2nd to
                N-th parameter.
            as_tensor_output: whether to output tensor instead of numpy array.
                defaults to True.
            device: device to store the output grid data.

        See also:
            - :py:meth:`monai.transforms.utils.create_rotate`
            - :py:meth:`monai.transforms.utils.create_shear`
            - :py:meth:`monai.transforms.utils.create_translate`
            - :py:meth:`monai.transforms.utils.create_scale`
        """
        self.rotate_range = ensure_tuple(rotate_range)
        self.shear_range = ensure_tuple(shear_range)
        self.translate_range = ensure_tuple(translate_range)
        self.scale_range = ensure_tuple(scale_range)

        self.rotate_params: Optional[List[float]] = None
        self.shear_params: Optional[List[float]] = None
        self.translate_params: Optional[List[float]] = None
        self.scale_params: Optional[List[float]] = None

        self.as_tensor_output = as_tensor_output
        self.device = device

    def randomize(self, data: Optional[Any] = None) -> None:
        if self.rotate_range:
            self.rotate_params = [self.R.uniform(-f, f) for f in self.rotate_range if f is not None]
        if self.shear_range:
            self.shear_params = [self.R.uniform(-f, f) for f in self.shear_range if f is not None]
        if self.translate_range:
            self.translate_params = [self.R.uniform(-f, f) for f in self.translate_range if f is not None]
        if self.scale_range:
            self.scale_params = [self.R.uniform(-f, f) + 1.0 for f in self.scale_range if f is not None]

    def __call__(
        self, spatial_size: Optional[Sequence[int]] = None, grid: Optional[Union[np.ndarray, torch.Tensor]] = None
    ) -> Union[np.ndarray, torch.Tensor]:
        """
        Args:
            spatial_size: output grid size.
            grid: grid to be transformed. Shape must be (3, H, W) for 2D or (4, H, W, D) for 3D.

        Returns:
            a 2D (3xHxW) or 3D (4xHxWxD) grid.
        """
        self.randomize()
        affine_grid = AffineGrid(
            rotate_params=self.rotate_params,
            shear_params=self.shear_params,
            translate_params=self.translate_params,
            scale_params=self.scale_params,
            as_tensor_output=self.as_tensor_output,
            device=self.device,
        )
        return affine_grid(spatial_size, grid)


class RandDeformGrid(Randomizable, Transform):
    """
    Generate random deformation grid.
    """

    def __init__(
        self,
        spacing: Union[Sequence[float], float],
        magnitude_range: Tuple[float, float],
        as_tensor_output: bool = True,
        device: Optional[torch.device] = None,
    ) -> None:
        """
        Args:
            spacing: spacing of the grid in 2D or 3D.
                e.g., spacing=(1, 1) indicates pixel-wise deformation in 2D,
                spacing=(1, 1, 1) indicates voxel-wise deformation in 3D,
                spacing=(2, 2) indicates deformation field defined on every other pixel in 2D.
            magnitude_range: the random offsets will be generated from
                `uniform[magnitude[0], magnitude[1])`.
            as_tensor_output: whether to output tensor instead of numpy array.
                defaults to True.
            device: device to store the output grid data.
        """
        self.spacing = spacing
        self.magnitude = magnitude_range

        self.rand_mag = 1.0
        self.as_tensor_output = as_tensor_output
        self.random_offset = 0.0
        self.device = device

    def randomize(self, grid_size: Sequence[int]) -> None:
        self.random_offset = self.R.normal(size=([len(grid_size)] + list(grid_size))).astype(np.float32)
        self.rand_mag = self.R.uniform(self.magnitude[0], self.magnitude[1])

    def __call__(self, spatial_size: Sequence[int]) -> Union[np.ndarray, torch.Tensor]:
        """
        Args:
            spatial_size: spatial size of the grid.
        """
        self.spacing = fall_back_tuple(self.spacing, (1.0,) * len(spatial_size))
        control_grid = create_control_grid(spatial_size, self.spacing)
        self.randomize(control_grid.shape[1:])
        control_grid[: len(spatial_size)] += self.rand_mag * self.random_offset
        if self.as_tensor_output:
            control_grid = torch.as_tensor(np.ascontiguousarray(control_grid), device=self.device)
        return control_grid


class Resample(Transform):
    def __init__(
        self,
        mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,
        padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER,
        as_tensor_output: bool = False,
        device: Optional[torch.device] = None,
    ) -> None:
        """
        computes output image using values from `img`, locations from `grid` using pytorch.
        supports spatially 2D or 3D (num_channels, H, W[, D]).

        Args:
            mode: {``"bilinear"``, ``"nearest"``}
                Interpolation mode to calculate output values. Defaults to ``"bilinear"``.
                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
            padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
                Padding mode for outside grid values. Defaults to ``"border"``.
                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
            as_tensor_output: whether to return a torch tensor. Defaults to False.
            device: device on which the tensor will be allocated.
        """
        self.mode: GridSampleMode = GridSampleMode(mode)
        self.padding_mode: GridSamplePadMode = GridSamplePadMode(padding_mode)
        self.as_tensor_output = as_tensor_output
        self.device = device

    def __call__(
        self,
        img: Union[np.ndarray, torch.Tensor],
        grid: Optional[Union[np.ndarray, torch.Tensor]] = None,
        mode: Optional[Union[GridSampleMode, str]] = None,
        padding_mode: Optional[Union[GridSamplePadMode, str]] = None,
    ) -> Union[np.ndarray, torch.Tensor]:
        """
        Args:
            img: shape must be (num_channels, H, W[, D]).
            grid: shape must be (3, H, W) for 2D or (4, H, W, D) for 3D.
            mode: {``"bilinear"``, ``"nearest"``}
                Interpolation mode to calculate output values. Defaults to ``self.mode``.
                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
            padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
                Padding mode for outside grid values. Defaults to ``self.padding_mode``.
                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
        """

        if not torch.is_tensor(img):
            img = torch.as_tensor(np.ascontiguousarray(img))
        assert grid is not None, "Error, grid argument must be supplied as an ndarray or tensor "
        grid = torch.tensor(grid) if not torch.is_tensor(grid) else grid.detach().clone()
        if self.device:
            img = img.to(self.device)
            grid = grid.to(self.device)

        for i, dim in enumerate(img.shape[1:]):
            grid[i] = 2.0 * grid[i] / (dim - 1.0)
        grid = grid[:-1] / grid[-1:]
        index_ordering: List[int] = list(range(img.ndimension() - 2, -1, -1))
        grid = grid[index_ordering]
        grid = grid.permute(list(range(grid.ndimension()))[1:] + [0])
        out = torch.nn.functional.grid_sample(
            img.unsqueeze(0).float(),
            grid.unsqueeze(0).float(),
            mode=self.mode.value if mode is None else GridSampleMode(mode).value,
            padding_mode=self.padding_mode.value if padding_mode is None else GridSamplePadMode(padding_mode).value,
            align_corners=True,
        )[0]
        if self.as_tensor_output:
            return out
        return out.cpu().numpy()


class Affine(Transform):
    """
    Transform ``img`` given the affine parameters.
    """

    def __init__(
        self,
        rotate_params: Optional[Union[Sequence[float], float]] = None,
        shear_params: Optional[Union[Sequence[float], float]] = None,
        translate_params: Optional[Union[Sequence[float], float]] = None,
        scale_params: Optional[Union[Sequence[float], float]] = None,
        spatial_size: Optional[Union[Sequence[int], int]] = None,
        mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,
        padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.REFLECTION,
        as_tensor_output: bool = False,
        device: Optional[torch.device] = None,
    ) -> None:
        """
        The affine transformations are applied in rotate, shear, translate, scale order.

        Args:
            rotate_params: a rotation angle in radians, a scalar for 2D image, a tuple of 3 floats for 3D.
                Defaults to no rotation.
            shear_params: a tuple of 2 floats for 2D, a tuple of 6 floats for 3D. Defaults to no shearing.
            translate_params: a tuple of 2 floats for 2D, a tuple of 3 floats for 3D. Translation is in
                pixel/voxel relative to the center of the input image. Defaults to no translation.
            scale_params: a tuple of 2 floats for 2D, a tuple of 3 floats for 3D. Defaults to no scaling.
            spatial_size: output image spatial size.
                if `spatial_size` and `self.spatial_size` are not defined, or smaller than 1,
                the transform will use the spatial size of `img`.
                if the components of the `spatial_size` are non-positive values, the transform will use the
                corresponding components of img size. For example, `spatial_size=(32, -1)` will be adapted
                to `(32, 64)` if the second spatial dimension size of img is `64`.
            mode: {``"bilinear"``, ``"nearest"``}
                Interpolation mode to calculate output values. Defaults to ``"bilinear"``.
                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
            padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
                Padding mode for outside grid values. Defaults to ``"reflection"``.
                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
            as_tensor_output: the computation is implemented using pytorch tensors, this option specifies
                whether to convert it back to numpy arrays.
            device: device on which the tensor will be allocated.
        """
        self.affine_grid = AffineGrid(
            rotate_params=rotate_params,
            shear_params=shear_params,
            translate_params=translate_params,
            scale_params=scale_params,
            as_tensor_output=True,
            device=device,
        )
        self.resampler = Resample(as_tensor_output=as_tensor_output, device=device)
        self.spatial_size = spatial_size
        self.mode: GridSampleMode = GridSampleMode(mode)
        self.padding_mode: GridSamplePadMode = GridSamplePadMode(padding_mode)

    def __call__(
        self,
        img: Union[np.ndarray, torch.Tensor],
        spatial_size: Optional[Union[Sequence[int], int]] = None,
        mode: Optional[Union[GridSampleMode, str]] = None,
        padding_mode: Optional[Union[GridSamplePadMode, str]] = None,
    ) -> Union[np.ndarray, torch.Tensor]:
        """
        Args:
            img: shape must be (num_channels, H, W[, D]),
            spatial_size: output image spatial size.
                if `spatial_size` and `self.spatial_size` are not defined, or smaller than 1,
                the transform will use the spatial size of `img`.
                if `img` has two spatial dimensions, `spatial_size` should have 2 elements [h, w].
                if `img` has three spatial dimensions, `spatial_size` should have 3 elements [h, w, d].
            mode: {``"bilinear"``, ``"nearest"``}
                Interpolation mode to calculate output values. Defaults to ``self.mode``.
                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
            padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
                Padding mode for outside grid values. Defaults to ``self.padding_mode``.
                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
        """
        sp_size = fall_back_tuple(spatial_size or self.spatial_size, img.shape[1:])
        grid = self.affine_grid(spatial_size=sp_size)
        return self.resampler(
            img=img, grid=grid, mode=mode or self.mode, padding_mode=padding_mode or self.padding_mode
        )


class RandAffine(Randomizable, Transform):
    """
    Random affine transform.
    """

    def __init__(
        self,
        prob: float = 0.1,
        rotate_range: Optional[Union[Sequence[float], float]] = None,
        shear_range: Optional[Union[Sequence[float], float]] = None,
        translate_range: Optional[Union[Sequence[float], float]] = None,
        scale_range: Optional[Union[Sequence[float], float]] = None,
        spatial_size: Optional[Union[Sequence[float], float]] = None,
        mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,
        padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.REFLECTION,
        as_tensor_output: bool = True,
        device: Optional[torch.device] = None,
    ) -> None:
        """
        Args:
            prob: probability of returning a randomized affine grid.
                defaults to 0.1, with 10% chance returns a randomized grid.
            rotate_range: angle range in radians. rotate_range[0] with be used to generate the 1st rotation
                parameter from `uniform[-rotate_range[0], rotate_range[0])`. Similarly, `rotate_range[1]` and
                `rotate_range[2]` are used in 3D affine for the range of 2nd and 3rd axes.
            shear_range: shear_range[0] with be used to generate the 1st shearing parameter from
                `uniform[-shear_range[0], shear_range[0])`. Similarly, `shear_range[1]` to
                `shear_range[N]` controls the range of the uniform distribution used to generate the 2nd to
                N-th parameter.
            translate_range : translate_range[0] with be used to generate the 1st shift parameter from
                `uniform[-translate_range[0], translate_range[0])`. Similarly, `translate_range[1]`
                to `translate_range[N]` controls the range of the uniform distribution used to generate
                the 2nd to N-th parameter.
            scale_range: scaling_range[0] with be used to generate the 1st scaling factor from
                `uniform[-scale_range[0], scale_range[0]) + 1.0`. Similarly, `scale_range[1]` to
                `scale_range[N]` controls the range of the uniform distribution used to generate the 2nd to
                N-th parameter.
            spatial_size: output image spatial size.
                if `spatial_size` and `self.spatial_size` are not defined, or smaller than 1,
                the transform will use the spatial size of `img`.
                if the components of the `spatial_size` are non-positive values, the transform will use the
                corresponding components of img size. For example, `spatial_size=(32, -1)` will be adapted
                to `(32, 64)` if the second spatial dimension size of img is `64`.
            mode: {``"bilinear"``, ``"nearest"``}
                Interpolation mode to calculate output values. Defaults to ``"bilinear"``.
                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
            padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
                Padding mode for outside grid values. Defaults to ``"reflection"``.
                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
            as_tensor_output: the computation is implemented using pytorch tensors, this option specifies
                whether to convert it back to numpy arrays.
            device: device on which the tensor will be allocated.

        See also:
            - :py:class:`RandAffineGrid` for the random affine parameters configurations.
            - :py:class:`Affine` for the affine transformation parameters configurations.
        """

        self.rand_affine_grid = RandAffineGrid(
            rotate_range=rotate_range,
            shear_range=shear_range,
            translate_range=translate_range,
            scale_range=scale_range,
            as_tensor_output=True,
            device=device,
        )
        self.resampler = Resample(as_tensor_output=as_tensor_output, device=device)

        self.spatial_size = spatial_size
        self.mode: GridSampleMode = GridSampleMode(mode)
        self.padding_mode: GridSamplePadMode = GridSamplePadMode(padding_mode)

        self.do_transform = False
        self.prob = prob

    def set_random_state(
        self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None
    ) -> "RandAffine":
        self.rand_affine_grid.set_random_state(seed, state)
        super().set_random_state(seed, state)
        return self

    def randomize(self, data: Optional[Any] = None) -> None:
        self.do_transform = self.R.rand() < self.prob
        self.rand_affine_grid.randomize()

    def __call__(
        self,
        img: Union[np.ndarray, torch.Tensor],
        spatial_size: Optional[Union[Sequence[int], int]] = None,
        mode: Optional[Union[GridSampleMode, str]] = None,
        padding_mode: Optional[Union[GridSamplePadMode, str]] = None,
    ) -> Union[np.ndarray, torch.Tensor]:
        """
        Args:
            img: shape must be (num_channels, H, W[, D]),
            spatial_size: output image spatial size.
                if `spatial_size` and `self.spatial_size` are not defined, or smaller than 1,
                the transform will use the spatial size of `img`.
                if `img` has two spatial dimensions, `spatial_size` should have 2 elements [h, w].
                if `img` has three spatial dimensions, `spatial_size` should have 3 elements [h, w, d].
            mode: {``"bilinear"``, ``"nearest"``}
                Interpolation mode to calculate output values. Defaults to ``self.mode``.
                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
            padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
                Padding mode for outside grid values. Defaults to ``self.padding_mode``.
                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
        """
        self.randomize()

        sp_size = fall_back_tuple(spatial_size or self.spatial_size, img.shape[1:])
        if self.do_transform:
            grid = self.rand_affine_grid(spatial_size=sp_size)
        else:
            grid = create_grid(spatial_size=sp_size)
        return self.resampler(
            img=img, grid=grid, mode=mode or self.mode, padding_mode=padding_mode or self.padding_mode
        )


class Rand2DElastic(Randomizable, Transform):
    """
    Random elastic deformation and affine in 2D
    """

    def __init__(
        self,
        spacing: Union[Tuple[float, float], float],
        magnitude_range: Tuple[float, float],
        prob: float = 0.1,
        rotate_range: Optional[Union[Sequence[float], float]] = None,
        shear_range: Optional[Union[Sequence[float], float]] = None,
        translate_range: Optional[Union[Sequence[float], float]] = None,
        scale_range: Optional[Union[Sequence[float], float]] = None,
        spatial_size: Optional[Union[Sequence[int], int]] = None,
        mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,
        padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.REFLECTION,
        as_tensor_output: bool = False,
        device: Optional[torch.device] = None,
    ) -> None:
        """
        Args:
            spacing : distance in between the control points.
            magnitude_range: the random offsets will be generated from ``uniform[magnitude[0], magnitude[1])``.
            prob: probability of returning a randomized elastic transform.
                defaults to 0.1, with 10% chance returns a randomized elastic transform,
                otherwise returns a ``spatial_size`` centered area extracted from the input image.
            rotate_range: angle range in radians. rotate_range[0] with be used to generate the 1st rotation
                parameter from `uniform[-rotate_range[0], rotate_range[0])`.
            shear_range: shear_range[0] with be used to generate the 1st shearing parameter from
                `uniform[-shear_range[0], shear_range[0])`. Similarly, `shear_range[1]` controls
                the range of the uniform distribution used to generate the 2nd parameter.
            translate_range : translate_range[0] with be used to generate the 1st shift parameter from
                `uniform[-translate_range[0], translate_range[0])`. Similarly, `translate_range[1]` controls
                the range of the uniform distribution used to generate the 2nd parameter.
            scale_range: scaling_range[0] with be used to generate the 1st scaling factor from
                `uniform[-scale_range[0], scale_range[0]) + 1.0`. Similarly, `scale_range[1]` controls
                the range of the uniform distribution used to generate the 2nd parameter.
            spatial_size: specifying output image spatial size [h, w].
                if `spatial_size` and `self.spatial_size` are not defined, or smaller than 1,
                the transform will use the spatial size of `img`.
                if the components of the `spatial_size` are non-positive values, the transform will use the
                corresponding components of img size. For example, `spatial_size=(32, -1)` will be adapted
                to `(32, 64)` if the second spatial dimension size of img is `64`.
            mode: {``"bilinear"``, ``"nearest"``}
                Interpolation mode to calculate output values. Defaults to ``"bilinear"``.
                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
            padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
                Padding mode for outside grid values. Defaults to ``"reflection"``.
                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
            as_tensor_output: the computation is implemented using pytorch tensors, this option specifies
                whether to convert it back to numpy arrays.
            device: device on which the tensor will be allocated.

        See also:
            - :py:class:`RandAffineGrid` for the random affine parameters configurations.
            - :py:class:`Affine` for the affine transformation parameters configurations.
        """
        self.deform_grid = RandDeformGrid(
            spacing=spacing, magnitude_range=magnitude_range, as_tensor_output=True, device=device
        )
        self.rand_affine_grid = RandAffineGrid(
            rotate_range=rotate_range,
            shear_range=shear_range,
            translate_range=translate_range,
            scale_range=scale_range,
            as_tensor_output=True,
            device=device,
        )
        self.resampler = Resample(as_tensor_output=as_tensor_output, device=device)

        self.spatial_size = spatial_size
        self.mode: GridSampleMode = GridSampleMode(mode)
        self.padding_mode: GridSamplePadMode = GridSamplePadMode(padding_mode)
        self.prob = prob
        self.do_transform = False

    def set_random_state(
        self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None
    ) -> "Rand2DElastic":
        self.deform_grid.set_random_state(seed, state)
        self.rand_affine_grid.set_random_state(seed, state)
        super().set_random_state(seed, state)
        return self

    def randomize(self, spatial_size: Sequence[int]) -> None:
        self.do_transform = self.R.rand() < self.prob
        self.deform_grid.randomize(spatial_size)
        self.rand_affine_grid.randomize()

    def __call__(
        self,
        img: Union[np.ndarray, torch.Tensor],
        spatial_size: Optional[Union[Tuple[int, int], int]] = None,
        mode: Optional[Union[GridSampleMode, str]] = None,
        padding_mode: Optional[Union[GridSamplePadMode, str]] = None,
    ) -> Union[np.ndarray, torch.Tensor]:
        """
        Args:
            img: shape must be (num_channels, H, W),
            spatial_size: specifying output image spatial size [h, w].
                if `spatial_size` and `self.spatial_size` are not defined, or smaller than 1,
                the transform will use the spatial size of `img`.
            mode: {``"bilinear"``, ``"nearest"``}
                Interpolation mode to calculate output values. Defaults to ``self.mode``.
                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
            padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
                Padding mode for outside grid values. Defaults to ``self.padding_mode``.
                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
        """
        sp_size = fall_back_tuple(spatial_size or self.spatial_size, img.shape[1:])
        self.randomize(spatial_size=sp_size)
        if self.do_transform:
            grid = self.deform_grid(spatial_size=sp_size)
            grid = self.rand_affine_grid(grid=grid)
            grid = _torch_interp(
                input=grid.unsqueeze(0),
                scale_factor=list(ensure_tuple(self.deform_grid.spacing)),
                mode=InterpolateMode.BICUBIC.value,
                align_corners=False,
            )
            grid = CenterSpatialCrop(roi_size=sp_size)(grid[0])
        else:
            grid = create_grid(spatial_size=sp_size)
        return self.resampler(img, grid, mode=mode or self.mode, padding_mode=padding_mode or self.padding_mode)


class Rand3DElastic(Randomizable, Transform):
    """
    Random elastic deformation and affine in 3D
    """

    def __init__(
        self,
        sigma_range: Tuple[float, float],
        magnitude_range: Tuple[float, float],
        prob: float = 0.1,
        rotate_range: Optional[Union[Sequence[float], float]] = None,
        shear_range: Optional[Union[Sequence[float], float]] = None,
        translate_range: Optional[Union[Sequence[float], float]] = None,
        scale_range: Optional[Union[Sequence[float], float]] = None,
        spatial_size: Optional[Union[Sequence[int], int]] = None,
        mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,
        padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.REFLECTION,
        as_tensor_output: bool = False,
        device: Optional[torch.device] = None,
    ) -> None:
        """
        Args:
            sigma_range: a Gaussian kernel with standard deviation sampled from
                ``uniform[sigma_range[0], sigma_range[1])`` will be used to smooth the random offset grid.
            magnitude_range: the random offsets on the grid will be generated from
                ``uniform[magnitude[0], magnitude[1])``.
            prob: probability of returning a randomized elastic transform.
                defaults to 0.1, with 10% chance returns a randomized elastic transform,
                otherwise returns a ``spatial_size`` centered area extracted from the input image.
            rotate_range: angle range in radians. rotate_range[0] with be used to generate the 1st rotation
                parameter from `uniform[-rotate_range[0], rotate_range[0])`. Similarly, `rotate_range[1]` and
                `rotate_range[2]` are used in 3D affine for the range of 2nd and 3rd axes.
            shear_range: shear_range[0] with be used to generate the 1st shearing parameter from
                `uniform[-shear_range[0], shear_range[0])`. Similarly, `shear_range[1]` and `shear_range[2]`
                controls the range of the uniform distribution used to generate the 2nd and 3rd parameters.
            translate_range : translate_range[0] with be used to generate the 1st shift parameter from
                `uniform[-translate_range[0], translate_range[0])`. Similarly, `translate_range[1]` and
                `translate_range[2]` controls the range of the uniform distribution used to generate
                the 2nd and 3rd parameters.
            scale_range: scaling_range[0] with be used to generate the 1st scaling factor from
                `uniform[-scale_range[0], scale_range[0]) + 1.0`. Similarly, `scale_range[1]` and `scale_range[2]`
                controls the range of the uniform distribution used to generate the 2nd and 3rd parameters.
            spatial_size: specifying output image spatial size [h, w, d].
                if `spatial_size` and `self.spatial_size` are not defined, or smaller than 1,
                the transform will use the spatial size of `img`.
                if the components of the `spatial_size` are non-positive values, the transform will use the
                corresponding components of img size. For example, `spatial_size=(32, 32, -1)` will be adapted
                to `(32, 32, 64)` if the third spatial dimension size of img is `64`.
            mode: {``"bilinear"``, ``"nearest"``}
                Interpolation mode to calculate output values. Defaults to ``"bilinear"``.
                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
            padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
                Padding mode for outside grid values. Defaults to ``"reflection"``.
                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
            as_tensor_output: the computation is implemented using pytorch tensors, this option specifies
                whether to convert it back to numpy arrays.
            device: device on which the tensor will be allocated.

        See also:
            - :py:class:`RandAffineGrid` for the random affine parameters configurations.
            - :py:class:`Affine` for the affine transformation parameters configurations.
        """
        self.rand_affine_grid = RandAffineGrid(rotate_range, shear_range, translate_range, scale_range, True, device)
        self.resampler = Resample(as_tensor_output=as_tensor_output, device=device)

        self.sigma_range = sigma_range
        self.magnitude_range = magnitude_range
        self.spatial_size = spatial_size
        self.mode: GridSampleMode = GridSampleMode(mode)
        self.padding_mode: GridSamplePadMode = GridSamplePadMode(padding_mode)
        self.device = device

        self.prob = prob
        self.do_transform = False
        self.rand_offset = None
        self.magnitude = 1.0
        self.sigma = 1.0

    def set_random_state(
        self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None
    ) -> "Rand3DElastic":
        self.rand_affine_grid.set_random_state(seed, state)
        super().set_random_state(seed, state)
        return self

    def randomize(self, grid_size: Sequence[int]) -> None:
        self.do_transform = self.R.rand() < self.prob
        if self.do_transform:
            self.rand_offset = self.R.uniform(-1.0, 1.0, [3] + list(grid_size)).astype(np.float32)
        self.magnitude = self.R.uniform(self.magnitude_range[0], self.magnitude_range[1])
        self.sigma = self.R.uniform(self.sigma_range[0], self.sigma_range[1])
        self.rand_affine_grid.randomize()

    def __call__(
        self,
        img: Union[np.ndarray, torch.Tensor],
        spatial_size: Optional[Union[Tuple[int, int, int], int]] = None,
        mode: Optional[Union[GridSampleMode, str]] = None,
        padding_mode: Optional[Union[GridSamplePadMode, str]] = None,
    ) -> Union[np.ndarray, torch.Tensor]:
        """
        Args:
            img: shape must be (num_channels, H, W, D),
            spatial_size: specifying spatial 3D output image spatial size [h, w, d].
                if `spatial_size` and `self.spatial_size` are not defined, or smaller than 1,
                the transform will use the spatial size of `img`.
            mode: {``"bilinear"``, ``"nearest"``}
                Interpolation mode to calculate output values. Defaults to ``self.mode``.
                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
            padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
                Padding mode for outside grid values. Defaults to ``self.padding_mode``.
                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
        """
        sp_size = fall_back_tuple(spatial_size or self.spatial_size, img.shape[1:])
        self.randomize(grid_size=sp_size)
        grid = create_grid(spatial_size=sp_size)
        if self.do_transform:
            assert self.rand_offset is not None
            grid = torch.as_tensor(np.ascontiguousarray(grid), device=self.device)
            gaussian = GaussianFilter(3, self.sigma, 3.0).to(device=self.device)
            offset = torch.as_tensor(self.rand_offset, device=self.device).unsqueeze(0)
            grid[:3] += gaussian(offset)[0] * self.magnitude
            grid = self.rand_affine_grid(grid=grid)
        return self.resampler(img, grid, mode=mode or self.mode, padding_mode=padding_mode or self.padding_mode)