# Source code for rising.transforms.functional.spatial

```
import torch
from typing import Sequence, Union, Optional
from rising.utils import check_scalar
__all__ = ["mirror", "rot90", "resize_native"]
[docs]def mirror(data: torch.Tensor, dims: Union[int, Sequence[int]]) -> torch.Tensor:
"""
Mirror data at dims
Args:
data: input data
dims: dimensions to mirror
Returns:
torch.Tensor: tensor with mirrored dimensions
"""
if check_scalar(dims):
dims = (dims,)
# batch and channel dims
dims = [d + 2 for d in dims]
return data.flip(dims)
[docs]def rot90(data: torch.Tensor, k: int, dims: Union[int, Sequence[int]]):
"""
Rotate 90 degrees around dims
Args:
data: input data
k: number of times to rotate
dims: dimensions to mirror
Returns:
torch.Tensor: tensor with mirrored dimensions
"""
dims = [int(d + 2) for d in dims]
return torch.rot90(data, int(k), dims)
[docs]def resize_native(data: torch.Tensor,
size: Optional[Union[int, Sequence[int]]] = None,
scale_factor: Optional[Union[float, Sequence[float]]] = None,
mode: str = 'nearest', align_corners: Optional[bool] = None,
preserve_range: bool = False):
"""
Down/up-sample sample to either the given :attr:`size` or the given
:attr:`scale_factor`
The modes available for resizing are: nearest, linear (3D-only), bilinear,
bicubic (4D-only), trilinear (5D-only), area
Args:
data: input tensor of shape batch x channels x height x width x [depth]
size: spatial output size (excluding batch size and number of channels)
scale_factor: multiplier for spatial size
mode: one of ``nearest``, ``linear``, ``bilinear``, ``bicubic``,
``trilinear``, ``area``
(for more inforamtion see :func:`torch.nn.functional.interpolate`)
align_corners: input and output tensors are aligned by the center
points of their corners pixels, preserving the values at the
corner pixels.
preserve_range: output tensor has same range as input tensor
Returns:
torch.Tensor: interpolated tensor
See Also:
:func:`torch.nn.functional.interpolate`
"""
if check_scalar(scale_factor):
# pytorch internally checks for an iterable. Single value tensors are still iterable
scale_factor = float(scale_factor)
out = torch.nn.functional.interpolate(
data, size=size, scale_factor=scale_factor, mode=mode,
align_corners=align_corners)
if preserve_range:
out.clamp_(data.min(), data.max())
return out
```