transformsmodule that offers CUDA-accelerated affine transforms for 3D numpy arrays:
import numpy as np
from voltools import transform
volume = np.random.random((200, 200, 200)).astype(np.float32)
transformed_volume = transform(volume, interpolation='filt_bspline', device='cpu',
translation=(10, 0, -10),
rotation=(0, 45, 0), rotation_units='deg', rotation_order='rzxz')StaticVolumeclass optimized for multiple transformations of the same data. The data transfer is minimized (especially for GPU devices) to just the transformation matrix for each transformation.
import numpy as np
from voltools import StaticVolume
volume = StaticVolume(np.random.random((200, 200, 200)).astype(np.float32), interpolation='filt_bspline', device='gpu:0')
for i in range(0, 180):
rotated_vol = volume.rotate(rotation=(0, i, 0), rotation_units='deg', rotation_order='rzxz', profile=True)-
If you don't need to move data back from GPU to CPU, you can specify
output=some_cupy_arraykeyword and the result of transformation will be saved there. Works for bothtransformsandStaticVolume. If output is not specified, methods will return a numpy array. -
Support for different devices. Each method accepts
devicekeyword with optioncpu(default). However, if cupy is present, additional options includegpu(cupy auto-selects gpu) andgpu:Xfor each GPU (X is ID of GPU). -
Various interpolations currently supported:
linear, tri-linear interpolationbspline, cubic b-spline interpolation (optimized, 8 texture lookups)bspline_simple, cubic b-spline interpolation (simple implementation, 27 texture lookups)filt_bspline, prefiltered cubic b-spline interpolation (8 texture lookups)filt_bspline_simple, prefiltered cubic b-spline interpolation (27 texture lookups)
PIP: pip install voltools
Source: pip install git+https://github.com/the-lay/voltools
If you want to use use GPUs, please install cupy >= 7.0.0b4 (cupy installation guide).
- Tests
- Travis? Other CI?
- Visualizations? Some kind of easy to launch volume viewer.
- Return scripts back: projections
- Develop branch for cleaner separation of code
- CUDA cubic b-spline interpolation is based on Danny Ruijters's implementation
- Transformation matrices are based on Christoph Gohlike's transformations.py
Source: combination of different runs (device='cpu' and 'gpu', order=1 or 3, interpolation='linear' or 'filt_bspline_simple' or 'filt_bspline') of tests/benchmark.py
Results on laptop GTX1050Ti, i7-7700HQ CPU @ 2.80GHz, timings are in ms, first column is the size of volume
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 (interpolation='bspline' or interpolation='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 (interpolation='bspline_simple' or interpolation='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
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