voltools 0.2.2

CUDA-accelerated 3D affine transformations for NumPy and CuPy

voltools

CUDA-accelerated numpy/cupy 3D affine transformations

Features

1. transforms module that offers CUDA-accelerated affine transforms for 3D cupy and numpy arrays:

import cupy as cp
from voltools import transform, Interpolations

volume = cp.random.random((200, 200, 200), dtype=cp.float32)
transformed_volume = transform(volume, interpolation=Interpolations.FILT_BSPLINE,
                               translation=(10, 0, -10),
                               rotation=(0, 45, 0), rotation_units='deg', rotation_order='rzxz')

2. StaticVolume class optimized for multiple transformations of the same data. The data transfer is minimized to just the transformation matrix for each transformation.

import cupy as cp
from voltools import StaticVolume, Interpolations

volume = StaticVolume(cp.random.random((200, 200, 200), dtype=cp.float32), interpolation=Interpolations.FILT_BSPLINE, device_id=3)
for i in range(0, 180):
    rotated_vol = volume.rotate(rotation=(0, i, 0), rotation_units='deg', rotation_order='rzxz', profile=True)

3. If you don't need to move data back from GPU to CPU, you can specify output=some_cupy_array keyword and the result of transformation will be saved there. Works for both transforms and StaticVolume.

4. Support for multiple GPUs. Specify device_id=ID for StaticVolume and it will be stored and processed on that GPU. Transforms will be done on gpu 0 for numpy array and on the same gpu where the array is for cupy arrays.

5. Various interpolations currently supported:

• Interpolation.LINEAR, tri-linear interpolation
• Interpolation.BSPLINE, cubic b-spline interpolation (optimized, 8 texture lookups)
• Interpolation.BSPLINE_SIMPLE, cubic b-spline interpolation (simple implementation, 27 texture lookups)
• Interpolation.FILT_BSPLINE, prefiltered cubic b-spline interpolation (8 texture lookups)
• Interpolation.FILT_BSPLINE_SIMPLE, prefiltered cubic b-spline interpolation (27 texture lookups)

Installation

Important: Requires cupy >= 7.0.0b4 (please check cupy installation guide)

PIP: pip install voltools
Source: pip install git+https://github.com/the-lay/voltools

TODO

• Tests
• Travis? Other CI?
• Visualizations? Some kind of easy to launch volume viewer.
• Return scripts back: projections
• Develop branch for cleaner separation of code

Notes

• CUDA cubic b-spline interpolation is based on Danny Ruijters's implementation
• Transformation matrices are based on Christoph Gohlike's transformations.py

Benchmark

Source: tests/benchmark.py

Linear interpolation (Interpolations.LINEAR)

Scipy.affine_transform was run with order=1

                  scipy      np_transform  np_transform_out  cp_transform  cp_transform_out  static_vol  static_vol_out
5, 5, 5            0.099339      1.707743     0.198648         0.178715       0.149916         0.096302      0.057303
25, 25, 25         1.317875      0.355751     0.226222         0.193489       0.162452         0.113279      0.057102
50, 50, 50         9.752507      0.490053     0.290321         0.230922       0.198530         0.151064      0.092354
100, 100, 100     86.330383      1.569304     1.426492         0.835178       0.773746         0.494033      0.403363
250, 250, 250   1732.845833     22.273793    21.761067        13.274235      12.677875         9.971454      8.768116

Cubic b-spline interpolation optimized lookup (Interpolations.BSPLINE or Interpolations.FILT_BSPLINE)

Scipy.affine_transform was run with order=3

                   scipy      np_transform   np_transform_out  cp_transform  cp_transform_out  static_vol  static_vol_out
5, 5, 5           0.185161       1.467492        0.191925      0.176825          0.147542      0.095937        0.055644
25, 25, 25        5.506060       0.336234        0.208039      0.187378          0.155858      0.112726        0.061163
50, 50, 50        45.34205       0.571488        0.392732      0.329547          0.290759      0.242434        0.181332
100, 100, 100   368.032446       2.488342        2.331586      1.689142          1.627921      1.345535        1.250627
250, 250, 250  6003.420537      48.115719       47.672480     39.183325         38.772662     35.991094       34.685690

Cubic b-spline interpolation (Interpolations.BSPLINE_SIMPLE or Interpolations.FILT_BSPLINE_SIMPLE)

Scipy.affine_transform was run with order=3

                   scipy      np_transform   np_transform_out  cp_transform  cp_transform_out  static_vol  static_vol_out
5, 5, 5           0.201232      4.528787          0.207631      0.195885          0.163193    0.109361         0.059611
25, 25, 25        5.240529      0.332447          0.238356      0.217309          0.194111    0.138756         0.09321
50, 50, 50       43.515086      0.804508          0.632981      0.560689          0.527334    0.474820         0.416062
100, 100, 100   375.886700      4.232868          4.114524      3.454444          3.390018    3.091396         2.999451
250, 250, 250  6189.052927     95.083083         94.479406     85.200392         84.435548    81.808363       80.959284