Alpine
A PyTorch Library for Implicit Neural Representations.
Overview
Alpine is an easy and extensible library designed to rapidly prototype Implicit Neural Representations (INRs) or Neural Fields in PyTorch. It offers modular, object-oriented interfaces that minimize boilerplate code, allowing researchers to focus on scientific discovery. The library includes powerful visualization tools to inspect learned features, gradients, and histograms.
Technical Stack & Architecture
Core Framework
Built on PyTorch and Lightning for robust, scalable training loops. It leverages standard scientific computing libraries like NumPy, Scikit-learn, and Pandas for data manipulation.
Visualization & Analysis
Integrates Matplotlib and custom tools to provide interpretable visualizations such as PCA of learned features and real-time gradient monitoring.
Processing Pipelines
Utilizes OpenCV, Open3D, and Mcubes for handling complex 2D/3D data, including gigapixel images and protein structures.
Key Capabilities
- Multi-Dimensional Fitting: Supports tasks ranging from 1D Audio signals to 2D Images and 3D Volumes.
- Scientific Applications: capable of Protein structure modelling (RCSB PDB), Hyperspectral volume representation, and fitting gigapixel signals via MINER.
- Inverse Problems: Includes solvers for complex tasks like Phase Recovery in optics and CT reconstruction from sparse measurements.
- Modular Design: Object-oriented architecture allows for easy extension and integration of new neural field architectures.
Citation
If you find Alpine useful in your research, please consider citing the project:
@software{vyas_alpine_2025,
author = {Vyas, Kushal and Saragadam, Vishwanath and Veeraraghavan, Ashok and Balakrishnan, Guha},
title = {Alpine - A PyTorch Library for Implicit Neural Representations},
year = {2025},
url = {https://github.com/kushalvyas/alpine}
}