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torch-interpol

High-order spline interpolation in PyTorch

Description

This package contains a pure python implementation of high-order spline interpolation for ND tensors (including 2D and 3D images). It makes use of the just-in-time capabilities of TorchScript and explicitly implements the forward and backward passes of all functions, making it fast and memory-efficient.

All the functions available in this (small) package were originally implemented in NITorch, a larger PyTorch-based package dedicated to NeuroImaging and Medical Image Computing.

Installation

Dependency

  • torch >= 1.3

Conda

conda install torch-interpol -c balbasty -c pytorch

Pip

pip install torch-interpol

Usage

See our example notebooks

Quick doc

Notes
-----

`interpolation` can be an int, a string or an InterpolationType.
Possible values are:
    - 0 or 'nearest'
    - 1 or 'linear'
    - 2 or 'quadratic'
    - 3 or 'cubic'
    - 4 or 'fourth'
    - 5 or 'fifth'
    - etc.
A list of values can be provided, in the order [W, H, D],
to specify dimension-specific interpolation orders.

`bound` can be an int, a string or a BoundType.
Possible values are:
    - 'replicate'  or 'nearest'     :  a  a  a  |  a  b  c  d  |  d  d  d
    - 'dct1'       or 'mirror'      :  d  c  b  |  a  b  c  d  |  c  b  a
    - 'dct2'       or 'reflect'     :  c  b  a  |  a  b  c  d  |  d  c  b
    - 'dst1'       or 'antimirror'  : -b -a  0  |  a  b  c  d  |  0 -d -c
    - 'dst2'       or 'antireflect' : -c -b -a  |  a  b  c  d  | -d -c -b
    - 'dft'        or 'wrap'        :  b  c  d  |  a  b  c  d  |  a  b  c
    - 'zero'       or 'zeros'       :  0  0  0  |  a  b  c  d  |  0  0  0
A list of values can be provided, in the order [W, H, D],
to specify dimension-specific boundary conditions.
Note that
- `dft` corresponds to circular padding
- `dct2` corresponds to Neumann boundary conditions (symmetric)
- `dst2` corresponds to Dirichlet boundary conditions (antisymmetric)
See https://en.wikipedia.org/wiki/Discrete_cosine_transform
    https://en.wikipedia.org/wiki/Discrete_sine_transform

interpol.grid_pull(
    input,
    grid,
    interpolation='linear',
    bound='zero',
    extrapolate=False,
    prefilter=False,
)
"""
Sample an image with respect to a deformation field.

If the input dtype is not a floating point type, the input image is 
assumed to contain labels. Then, unique labels are extracted 
and resampled individually, making them soft labels. Finally, 
the label map is reconstructed from the individual soft labels by 
assigning the label with maximum soft value.

Parameters
----------
input : (..., [channel], *inshape) tensor
    Input image.
grid : (..., *outshape, dim) tensor
    Transformation field.
interpolation : int or sequence[int], default=1
    Interpolation order.
bound : BoundType or sequence[BoundType], default='zero'
    Boundary conditions.
extrapolate : bool or int, default=True
    Extrapolate out-of-bound data.
prefilter : bool, default=False
    Apply spline pre-filter (= interpolates the input)

Returns
-------
output : (..., [channel], *outshape) tensor
    Deformed image.
"""
interpol.grid_push(
    input,
    grid,
    shape=None,
    interpolation='linear',
    bound='zero',
    extrapolate=False,
    prefilter=False,
)
"""
Splat an image with respect to a deformation field (pull adjoint).

Parameters
----------
input : (..., [channel], *inshape) tensor
    Input image.
grid : (..., *inshape, dim) tensor
    Transformation field.
shape : sequence[int], default=inshape
    Output shape
interpolation : int or sequence[int], default=1
    Interpolation order.
bound : BoundType, or sequence[BoundType], default='zero'
    Boundary conditions.
extrapolate : bool or int, default=True
    Extrapolate out-of-bound data.
prefilter : bool, default=False
    Apply spline pre-filter.

Returns
-------
output : (..., [channel], *shape) tensor
    Spatted image.
"""
interpol.grid_grad(
    input,
    grid,
    interpolation='linear',
    bound='zero',
    extrapolate=False,
    prefilter=False,
)
"""
Sample spatial gradients of an image with respect to a deformation field.

Parameters
----------
input : (..., [channel], *inshape) tensor
    Input image.
grid : (..., *inshape, dim) tensor
    Transformation field.
shape : sequence[int], default=inshape
    Output shape
interpolation : int or sequence[int], default=1
    Interpolation order.
bound : BoundType, or sequence[BoundType], default='zero'
    Boundary conditions.
extrapolate : bool or int, default=True
    Extrapolate out-of-bound data.
prefilter : bool, default=False
    Apply spline pre-filter (= interpolates the input)

Returns
-------
output : (..., [channel], *shape, dim) tensor
    Sampled gradients.
"""
interpol.spline_coeff_nd(
    input,
    interpolation='linear',
    bound='dct2',
    dim=None,
    inplace=False,
)
"""
Compute the interpolating spline coefficients, for a given spline order
and boundary conditions, along the last `dim` dimensions.

References
----------
..[1]  M. Unser, A. Aldroubi and M. Eden.
       "B-Spline Signal Processing: Part I-Theory,"
       IEEE Transactions on Signal Processing 41(2):821-832 (1993).
..[2]  M. Unser, A. Aldroubi and M. Eden.
       "B-Spline Signal Processing: Part II-Efficient Design and Applications,"
       IEEE Transactions on Signal Processing 41(2):834-848 (1993).
..[3]  M. Unser.
       "Splines: A Perfect Fit for Signal and Image Processing,"
       IEEE Signal Processing Magazine 16(6):22-38 (1999).

Parameters
----------
input : (..., *spatial) tensor
    Input image.
interpolation : int or sequence[int], default=1
    Interpolation order.
bound : BoundType or sequence[BoundType], default='dct1'
    Boundary conditions.
dim : int, default=-1
    Number of spatial dimensions
inplace : bool, default=False
    Process the volume in place.

Returns
-------
output : (..., *spatial) tensor
    Coefficient image.
"""
interpol.resize(
    image, 
    factor=None, 
    shape=None, 
    anchor='c',
    interpolation=1, 
    prefilter=True
)
"""Resize an image by a factor or to a specific shape.

Notes
-----
.. A least one of `factor` and `shape` must be specified
.. If `anchor in ('centers', 'edges')`, exactly one of `factor` or
   `shape must be specified.
.. If `anchor in ('first', 'last')`, `factor` must be provided even
   if `shape` is specified.
.. Because of rounding, it is in general not assured that
   `resize(resize(x, f), 1/f)` returns a tensor with the same shape as x.

        edges          centers          first           last
    e - + - + - e   + - + - + - +   + - + - + - +   + - + - + - +
    | . | . | . |   | c | . | c |   | f | . | . |   | . | . | . |
    + _ + _ + _ +   + _ + _ + _ +   + _ + _ + _ +   + _ + _ + _ +
    | . | . | . |   | . | . | . |   | . | . | . |   | . | . | . |
    + _ + _ + _ +   + _ + _ + _ +   + _ + _ + _ +   + _ + _ + _ +
    | . | . | . |   | c | . | c |   | . | . | . |   | . | . | l |
    e _ + _ + _ e   + _ + _ + _ +   + _ + _ + _ +   + _ + _ + _ +

Parameters
----------
image : (batch, channel, *inshape) tensor
    Image to resize
factor : float or list[float], optional
    Resizing factor
    * > 1 : larger image <-> smaller voxels
    * < 1 : smaller image <-> larger voxels
shape : (ndim,) list[int], optional
    Output shape
anchor : {'centers', 'edges', 'first', 'last'} or list, default='centers'
    * In cases 'c' and 'e', the volume shape is multiplied by the
      zoom factor (and eventually truncated), and two anchor points
      are used to determine the voxel size.
    * In cases 'f' and 'l', a single anchor point is used so that
      the voxel size is exactly divided by the zoom factor.
      This case with an integer factor corresponds to subslicing
      the volume (e.g., `vol[::f, ::f, ::f]`).
    * A list of anchors (one per dimension) can also be provided.
interpolation : int or sequence[int], default=1
    Interpolation order.
prefilter : bool, default=True
    Apply spline pre-filter (= interpolates the input)

Returns
-------
resized : (batch, channel, *shape) tensor
    Resized image

"""

License

torch-interpol is released under the MIT license.