The hippynn python package - a modular library for atomistic machine learning with pytorch.

Related tags

Deep Learninghippynn
Overview

The hippynn python package - a modular library for atomistic machine learning with pytorch.

We aim to provide a powerful library for the training of atomistic (or physical point-cloud) machine learning. We want entry-level users to be able to efficiently train models to millions of datapoints, and a modular structure for extension or contribution.

While hippynn's development so-far has centered around the HIP-NN architecture, don't let that discourage you if you are performing research with another model. Get in touch, and let's work together to provide a high-quality implementation of your work, either as a contribution or an interface extension to your own package.

Features:

Modular set of pytorch layers for atomistic operations

  • Atomistic operations can be tricky to write in native pytorch. Most operations provided here support linear-scaling models.
  • Model energy, force charge & charge moments, bond orders, and more!
  • nn.Modules are written with minimal reference to the rest of the library; if you want to use them in your scripts without using the rest of the features provided here -- no problem!

Graph level API for simple and flexible construction of models from pytorch components.

  • Build models based on the abstract physics/mathematics of the problem, without having to think about implementation details.
  • Graph nodes support native python syntax, for example different forms of loss can be directly added.
  • Link predicted values in the model with a database entry to compare predicted and true values
  • IndexType logic records metadata about tensor structure, and provides automatic conversion to compatible structures when possible.
  • Graph API is independent of module implementation.

Plot level API for tracking your training.

  • Using the graph API, define quantities to evaluate before, during, or after training as figures using matplotlib.

Training & Experiment API

  • Integrated with graph level API
  • Pretty-printing loss metrics, generating plots periodically
  • Callbacks and checkpointing

Custom Kernels for fast execution

  • Certain operations are not efficiently written in pure pytorch, we provide alternative implementations with numba
  • These are directly linked in with pytorch Autograd -- use them like native pytorch functions.
  • These provide advantages in memory footprint and speed
  • Includes CPU and GPU execution for custom kernels

Interfaces

  • ASE: Define ASE calculators based on the graph-level API.
  • PYSEQM: Use PYSEQM calculations as nodes in a graph.

Installation

  • Clone this repository and navigate into it.
  • Run pip install .

If you fee like tinkering, do an editable install: pip install -e .

You can install using all optional dependencies from pip with: pip install -e .[full]

Notes

  • Install dependencies with pip from requirements.txt .
  • Install dependencies with conda from conda_requirements.txt .
  • If you don't want pip to install them, conda install from file before installing hippynn. You may want to use -c pytorch for the pytorch channel. For ase, you may want to use -c conda-forge.
  • Optional dependencies are in optional_dependencies.txt

We are currently under development. At the moment you should be prepared for breaking changes -- keep track of what version you are using if you need to maintain consistency.

As we clean up the rough edges, we are preparing a manuscript. If, in the mean time, you are using hippynn in your work, please cite this repository and the HIP-NN paper:

Lubbers, N., Smith, J. S., & Barros, K. (2018). Hierarchical modeling of molecular energies using a deep neural network. The Journal of chemical physics, 148(24), 241715.

See AUTHORS.txt for information on authors.

See LICENSE.txt for licensing information. hippynn is licensed under the BSD-3 license.

Triad National Security, LLC (Triad) owns the copyright to hippynn, which it identifies as project number LA-CC-19-093.

Copyright 2019. Triad National Security, LLC. All rights reserved. This program was produced under U.S. Government contract 89233218CNA000001 for Los Alamos National Laboratory (LANL), which is operated by Triad National Security, LLC for the U.S. Department of Energy/National Nuclear Security Administration. All rights in the program are reserved by Triad National Security, LLC, and the U.S. Department of Energy/National Nuclear Security Administration. The Government is granted for itself and others acting on its behalf a nonexclusive, paid-up, irrevocable worldwide license in this material to reproduce, prepare derivative works, distribute copies to the public, perform publicly and display publicly, and to permit others to do so.

Owner
Los Alamos National Laboratory
Los Alamos National Laboratory
This repository is for EMNLP 2021 paper: It is Not as Good as You Think! Evaluating Simultaneous Machine Translation on Interpretation Data

InterpretationData This repository is for our EMNLP 2021 paper: It is Not as Good as You Think! Evaluating Simultaneous Machine Translation on Interpr

4 Apr 21, 2022
Real-time Object Detection for Streaming Perception, CVPR 2022

StreamYOLO Real-time Object Detection for Streaming Perception Jinrong Yang, Songtao Liu, Zeming Li, Xiaoping Li, Sun Jian Real-time Object Detection

Jinrong Yang 237 Dec 27, 2022
ProjectOxford-ClientSDK - This repo has moved :house: Visit our website for the latest SDKs & Samples

This project has moved 🏠 We heard your feedback! This repo has been deprecated and each project has moved to a new home in a repo scoped by API and p

Microsoft 970 Nov 28, 2022
Pytorch implementation of our paper accepted by NeurIPS 2021 -- Revisiting Discriminator in GAN Compression: A Generator-discriminator Cooperative Compression Scheme

Revisiting Discriminator in GAN Compression: A Generator-discriminator Cooperative Compression Scheme (NeurIPS2021) (Link) Overview Prerequisites Linu

Shaojie Li 34 Mar 31, 2022
Implementation for paper "Towards the Generalization of Contrastive Self-Supervised Learning"

Contrastive Self-Supervised Learning on CIFAR-10 Paper "Towards the Generalization of Contrastive Self-Supervised Learning", Weiran Huang, Mingyang Yi

Weiran Huang 13 Nov 30, 2022
The original implementation of TNDM used in the NeurIPS 2021 paper (no longer being updated)

TNDM - Targeted Neural Dynamical Modeling Note: This code is no longer being updated. The official re-implementation can be found at: https://github.c

1 Jul 21, 2022
Efficient Multi Collection Style Transfer Using GAN

Proposed a new model that can make style transfer from single style image, and allow to transfer into multiple different styles in a single model.

Zhaozheng Shen 2 Jan 15, 2022
Rank 1st in the public leaderboard of ScanRefer (2021-03-18)

InstanceRefer InstanceRefer: Cooperative Holistic Understanding for Visual Grounding on Point Clouds through Instance Multi-level Contextual Referring

63 Dec 07, 2022
[CVPR2021] UAV-Human: A Large Benchmark for Human Behavior Understanding with Unmanned Aerial Vehicles

UAV-Human Official repository for CVPR2021: UAV-Human: A Large Benchmark for Human Behavior Understanding with Unmanned Aerial Vehicle Paper arXiv Res

129 Jan 04, 2023
YOLOv5🚀 reproduction by Guo Quanhao using PaddlePaddle

YOLOv5-Paddle YOLOv5 🚀 reproduction by Guo Quanhao using PaddlePaddle 支持AutoBatch 支持AutoAnchor 支持GPU Memory 快速开始 使用AIStudio高性能环境快速构建YOLOv5训练(PaddlePa

QuanHao Guo 20 Nov 14, 2022
[ICCV 2021] A Simple Baseline for Semi-supervised Semantic Segmentation with Strong Data Augmentation

[ICCV 2021] A Simple Baseline for Semi-supervised Semantic Segmentation with Strong Data Augmentation

CodingMan 45 Dec 12, 2022
Accompanying code for the paper "A Kernel Test for Causal Association via Noise Contrastive Backdoor Adjustment".

#backdoor-HSIC (bd_HSIC) Accompanying code for the paper "A Kernel Test for Causal Association via Noise Contrastive Backdoor Adjustment". To generate

Robert Hu 0 Nov 25, 2021
This code is for our paper "VTGAN: Semi-supervised Retinal Image Synthesis and Disease Prediction using Vision Transformers"

ICCV Workshop 2021 VTGAN This code is for our paper "VTGAN: Semi-supervised Retinal Image Synthesis and Disease Prediction using Vision Transformers"

Sharif Amit Kamran 25 Dec 08, 2022
PyTorch implementation of Constrained Policy Optimization

PyTorch implementation of Constrained Policy Optimization (CPO) This repository has a simple to understand and use implementation of CPO in PyTorch. A

Sapana Chaudhary 25 Dec 08, 2022
UMT is a unified and flexible framework which can handle different input modality combinations, and output video moment retrieval and/or highlight detection results.

Unified Multi-modal Transformers This repository maintains the official implementation of the paper UMT: Unified Multi-modal Transformers for Joint Vi

Applied Research Center (ARC), Tencent PCG 84 Jan 04, 2023
Discovering Interpretable GAN Controls [NeurIPS 2020]

GANSpace: Discovering Interpretable GAN Controls Figure 1: Sequences of image edits performed using control discovered with our method, applied to thr

Erik Härkönen 1.7k Jan 03, 2023
Code for Fully Context-Aware Image Inpainting with a Learned Semantic Pyramid

SPN: Fully Context-Aware Image Inpainting with a Learned Semantic Pyramid Code for Fully Context-Aware Image Inpainting with a Learned Semantic Pyrami

12 Jun 27, 2022
unet-family: Ultimate version

unet-family: Ultimate version 基于之前my-unet代码,我整理出来了这一份终极版本unet-family,方便其他人阅读。 相比于之前的my-unet代码,代码分类更加规范,有条理 对于clone下来的代码不需要修改各种复杂繁琐的路径问题,直接就可以运行。 并且代码有

2 Sep 19, 2022
[ICML 2021, Long Talk] Delving into Deep Imbalanced Regression

Delving into Deep Imbalanced Regression This repository contains the implementation code for paper: Delving into Deep Imbalanced Regression Yuzhe Yang

Yuzhe Yang 568 Dec 30, 2022
This is a repository of our model for weakly-supervised video dense anticipation.

Introduction This is a repository of our model for weakly-supervised video dense anticipation. More results on GTEA, Epic-Kitchens etc. will come soon

2 Apr 09, 2022