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Python implementation of 3D Zernike moments with Tensorflow

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ZMPY3D_TF

Update: ZMPY3D PyTorch implementation is available (August 25, 2024).

ZMPY3D: accelerating protein structure volume analysis through vectorized 3D Zernike Moments and Python-based GPU Integration

For CPU support only, please refer to the repository:

ZMPY3D supports NumPy (https://github.com/tawssie/ZMPY3D)

For GPU support with TensorFlow, CuPy and PyTorch, please refer to the other three repositories:

ZMPY3D_TF supports Tensorflow (https://github.com/tawssie/ZMPY3D_TF)

ZMPY3D_CP supports CuPy (https://github.com/tawssie/ZMPY3D_CP)

ZMPY3D_PT supports PyTorch (https://github.com/tawssie/ZMPY3D_PT)

Here presents a Python-based software package, ZMPY3D, to accelerate the moments computation by vectorizing the mathematical formulae, enabling their computation in graphical processing units (GPUs). The package offers popular GPU-supported libraries such as CuPy and TensorFlow along with NumPy implementations, aiming to improve computational efficiency, adaptability, and flexibility in future algorithmic development.

Installation

Prerequisites:

  • ZMPY3D : Python >=3.9.16, NumPy >=1.23.5
  • ZMPY3D_CP: Python >=3.9.16, NumPy, CuPy >=12.2.0
  • ZMPY3D_TF: Python >=3.9.16, NumPy >=1.23.5, Tensorflow >=2.12.0, Tensorflow-Probability >=0.20.1
  • ZMPY3D_PT: Python >=3.9.16, NumPy >=1.23.5, PyTorch >= 2.3.1
  1. Open the terminal
  2. Using pip to install the package through PyPI
  3. Run pip install ZMPY3D_TF for the installation

Usage

  • 3D Zernike moments with Tensorflow: Open In Colab
  • Shape similarity with CuPy: Open In Colab
  • Structure superposition with NumPy: Open In Colab
  • Runtime evaluation: Open In Colab

Performances

A voxel cube with dimensions of 100x100x100 was applied to perform 10,000 3D Zernike moment calculations, using 2 different maximum orders 20 and 40. Execution times for different hardware configurations using TensorFlow, CuPy, and NumPy libraries:

NumPy

Order CPU1 CPU2
20 33m20s 14m1s
40 951m40s 338m20s

TensorFlow

Order T4 RX3070Ti V100 L4
20 1m1s 0m36s 0m31s 0m39s
40 24m40s 9m3s 10m54s 11m13s

CuPy

Order T4 RX3070Ti V100 L4
20 4m45s 2m30s 1m42s 2m50s
40 35m20s 19m19s 14m45s 18m40s

Note: m = minutes, s = seconds.

Cache data for order 40

Due to GitHub's file size limitations, follow these steps to download the cache data for order 40 (1.3G) in the ZMPY3D_TF package:

1. Locate Package Folder

  • Open your terminal and execute the following command to find the folder of the ZMPY3D_TF package:
  • python -c "import ZMPY3D_TF; print(ZMPY3D_TF.__file__)"
  • Note the path, which ends with /User/path/ptyhon/site-packages/ZMPY3D_TF/__init__.py.

2. Navigate to Cache Data Folder

  • Go to the cache_data folder at the same level as __init__.py file, i.e., /User/path/ptyhon/site-packages/ZMPY3D_TF/cache_data.

3. Download the Cache File:

Further reading: What can 3D Zernike moments do?

Contributing

Feel free to submit pull requests for improvements or bug fixes.


Citation

Lai, J. S., Burley, S. K., & Duarte, J. M. (2024). ZMPY3D: Accelerating protein structure volume analysis through vectorized 3D Zernike moments and Python-based GPU integration. (Bioinformatics Advances, vbae111, https://doi.org/10.1093/bioadv/vbae111)

License

This project is licensed under the GNU General Public License v3.0. You can view the full license here.