| Name | voltools JSON |
| Version |
0.6.0
JSON |
| download |
| home_page | https://github.com/the-lay/voltools |
| Summary | CUDA-accelerated 3D affine transformations for NumPy and CuPy |
| upload_time | 2023-07-25 17:33:41 |
| maintainer | |
| docs_url | None |
| author | the-lay |
| requires_python | |
| license | MIT |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
|
| coveralls test coverage |
No coveralls.
|
## voltools
[](https://travis-ci.com/the-lay/voltools)
[](https://pypi.org/project/voltools/)
##### CUDA-accelerated numpy/cupy texture memory 3D affine transformations
#### Features
1. `transforms` module that offers CUDA-accelerated affine transforms for 3D numpy arrays:
```python
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')
```
2. `StaticVolume` class 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.
```python
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)
```
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`. If output is not
specified, methods will return a numpy array.
4. Support for different devices. Each method accepts `device` keyword with option `cpu` (default).
However, if cupy is present, additional options include `gpu` (cupy auto-selects gpu) and `gpu:X`
for each GPU (X is ID of GPU).
5. Various interpolations currently supported:
- `linear`, tri-linear interpolation
- `bspline`, 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)
#### Installation
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](https://docs-cupy.chainer.org/en/stable/install.html#install-cupy)).
#### 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](https://github.com/DannyRuijters/CubicInterpolationCUDA/)
- Transformation matrices are based on [Christoph Gohlike's transformations.py](https://www.lfd.uci.edu/~gohlke/code/transformations.py.html)
#### Benchmark
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
##### Linear interpolation (`interpolation='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 (`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
```
Raw data
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"description": "## voltools\n[](https://travis-ci.com/the-lay/voltools)\n[](https://pypi.org/project/voltools/)\n\n##### CUDA-accelerated numpy/cupy texture memory 3D affine transformations\n\n#### Features\n1. `transforms` module that offers CUDA-accelerated affine transforms for 3D numpy arrays:\n```python\nimport numpy as np\nfrom voltools import transform\n\nvolume = np.random.random((200, 200, 200)).astype(np.float32)\ntransformed_volume = transform(volume, interpolation='filt_bspline', device='cpu',\n translation=(10, 0, -10),\n rotation=(0, 45, 0), rotation_units='deg', rotation_order='rzxz')\n```\n\n2. `StaticVolume` class optimized for multiple transformations of the same data.\nThe data transfer is minimized (especially for GPU devices) to just the transformation matrix for each transformation.\n```python\nimport numpy as np\nfrom voltools import StaticVolume\n\nvolume = StaticVolume(np.random.random((200, 200, 200)).astype(np.float32), interpolation='filt_bspline', device='gpu:0')\nfor i in range(0, 180):\n rotated_vol = volume.rotate(rotation=(0, i, 0), rotation_units='deg', rotation_order='rzxz', profile=True)\n```\n\n3. If you don't need to move data back from GPU to CPU, you can specify `output=some_cupy_array` keyword\nand the result of transformation will be saved there. Works for both `transforms` and `StaticVolume`. If output is not\nspecified, methods will return a numpy array.\n\n4. Support for different devices. Each method accepts `device` keyword with option `cpu` (default).\nHowever, if cupy is present, additional options include `gpu` (cupy auto-selects gpu) and `gpu:X`\nfor each GPU (X is ID of GPU).\n\n5. Various interpolations currently supported:\n- `linear`, tri-linear interpolation\n- `bspline`, cubic b-spline interpolation (optimized, 8 texture lookups)\n- `bspline_simple`, cubic b-spline interpolation (simple implementation, 27 texture lookups)\n- `filt_bspline`, prefiltered cubic b-spline interpolation (8 texture lookups) \n- `filt_bspline_simple`, prefiltered cubic b-spline interpolation (27 texture lookups)\n\n#### Installation\n\nPIP: `pip install voltools` \nSource: `pip install git+https://github.com/the-lay/voltools`\n\nIf you want to use use GPUs, please install cupy >= 7.0.0b4\n([cupy installation guide](https://docs-cupy.chainer.org/en/stable/install.html#install-cupy)).\n\n\n#### TODO\n- Tests\n- Travis? Other CI?\n- Visualizations? Some kind of easy to launch volume viewer.\n- Return scripts back: projections\n- Develop branch for cleaner separation of code\n\n#### Notes\n- CUDA cubic b-spline interpolation is based on [Danny Ruijters's implementation](https://github.com/DannyRuijters/CubicInterpolationCUDA/)\n- Transformation matrices are based on [Christoph Gohlike's transformations.py](https://www.lfd.uci.edu/~gohlke/code/transformations.py.html)\n\n\n#### Benchmark\nSource: 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` \nResults on laptop GTX1050Ti, i7-7700HQ CPU @ 2.80GHz, timings are in ms, first column is the size of volume\n\n##### Linear interpolation (`interpolation='linear'`)\nScipy.affine_transform was run with `order=1`\n```\n scipy np_transform np_transform_out cp_transform cp_transform_out static_vol static_vol_out\n5, 5, 5 0.099339 1.707743 0.198648 0.178715 0.149916 0.096302 0.057303\n25, 25, 25 1.317875 0.355751 0.226222 0.193489 0.162452 0.113279 0.057102\n50, 50, 50 9.752507 0.490053 0.290321 0.230922 0.198530 0.151064 0.092354\n100, 100, 100 86.330383 1.569304 1.426492 0.835178 0.773746 0.494033 0.403363\n250, 250, 250 1732.845833 22.273793 21.761067 13.274235 12.677875 9.971454 8.768116\n```\n\n##### Cubic b-spline interpolation optimized lookup (`interpolation='bspline' or interpolation='filt_bspline'`)\nScipy.affine_transform was run with `order=3`\n```\n scipy np_transform np_transform_out cp_transform cp_transform_out static_vol static_vol_out\n5, 5, 5 0.185161 1.467492 0.191925 0.176825 0.147542 0.095937 0.055644\n25, 25, 25 5.506060 0.336234 0.208039 0.187378 0.155858 0.112726 0.061163\n50, 50, 50 45.34205 0.571488 0.392732 0.329547 0.290759 0.242434 0.181332\n100, 100, 100 368.032446 2.488342 2.331586 1.689142 1.627921 1.345535 1.250627\n250, 250, 250 6003.420537 48.115719 47.672480 39.183325 38.772662 35.991094 34.685690\n```\n\n##### Cubic b-spline interpolation (`interpolation='bspline_simple' or interpolation='filt_bspline_simple'`)\nScipy.affine_transform was run with `order=3`\n```\n scipy np_transform np_transform_out cp_transform cp_transform_out static_vol static_vol_out\n5, 5, 5 0.201232 4.528787 0.207631 0.195885 0.163193 0.109361 0.059611\n25, 25, 25 5.240529 0.332447 0.238356 0.217309 0.194111 0.138756 0.09321\n50, 50, 50 43.515086 0.804508 0.632981 0.560689 0.527334 0.474820 0.416062\n100, 100, 100 375.886700 4.232868 4.114524 3.454444 3.390018 3.091396 2.999451\n250, 250, 250 6189.052927 95.083083 94.479406 85.200392 84.435548 81.808363 80.959284\n```\n\n\n",
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