RAMA: Rapid algorithm for multicut problem

Related tags

Deep LearningRAMA
Overview

RAMA: Rapid algorithm for multicut problem

Solves multicut (correlation clustering) problems orders of magnitude faster than CPU based solvers without compromising solution quality on NVIDIA GPU. It also gives lower bound guarantees. Paper available here.

animation

Requirements

We use CUDA 11.2 and GCC 10. Other combinations might also work but not tested. CMake is required for compilation.

Installation

C++ solver:

mkdir build
cd build
cmake ..
make -j 4

Python bindings:

We also provide python bindings using pybind. Simply run the following command:

python -m pip install git+https://github.com/pawelswoboda/RAMA.git

Usage

C++ solver:

We require multicut instance stored in a (.txt) file in the following format:

MULTICUT
i_1, j_1, cost_1
i_2, j_2, cost_2
...
i_n, j_n, cost_n

which corresponds to a graph with N edges. Where i and j should be vertex indices and cost is a floating point number. Positive costs implies that the nodes are similar and thus would prefer to be in same component and viceversa. Afterwards run:

./rama_text_input -f <PATH_TO_MULTICUT_INSTANCE>

For more details and downloading multicut instances see LPMP.

Python solver:

An example to compute multicut on a triangle graph:

import rama_py
rama_py.rama_cuda([0, 1, 2], [1, 2, 0], [1.1, -2, 3], rama_py.multicut_solver_options()) 

Parameters:

The default set of parameters are defined here which correspond to algorithm PD from the paper. This algorithm offers best compute time versus solution quality trade-off. Parameters for other variants are:

  • Fast purely primal algorithm (P): This algorithm can be slightly worse than sequential CPU heuristics but is 30 to 50 times faster.
    ./rama_text_input -f <PATH_TO_MULTICUT_INSTANCE> 0 0 0 0
  • Best primal algorithm (PD+) : This algorithm can even be better than CPU solvers in terms of solution quality as it uses dual information. Still, it is 5 to 10 faster than best CPU solver.
     ./rama_text_input -f <PATH_TO_MULTICUT_INSTANCE> 5 10 5 10
  • Dual algorithm (D): Use this algorithm for only computing the lower bound. Our lower bounds are slightly better than ICP and are computed up to 100 times faster.
     ./rama_text_input -f <PATH_TO_MULTICUT_INSTANCE> 5 10 0 0 5

Run ./rama_text_input --help for details about the parameters.

Owner
Paul Swoboda
Paul Swoboda
Code for reproducing our paper: LMSOC: An Approach for Socially Sensitive Pretraining

LMSOC: An Approach for Socially Sensitive Pretraining Code for reproducing the paper LMSOC: An Approach for Socially Sensitive Pretraining to appear a

Twitter Research 11 Dec 20, 2022
Ppq - A powerful offline neural network quantization tool with custimized IR

PPL Quantization Tool(PPL 量化工具) PPL Quantization Tool (PPQ) is a powerful offlin

605 Jan 03, 2023
A distributed, plug-n-play algorithm for multi-robot applications with a priori non-computable objective functions

A distributed, plug-n-play algorithm for multi-robot applications with a priori non-computable objective functions Kapoutsis, A.C., Chatzichristofis,

Athanasios Ch. Kapoutsis 5 Oct 15, 2022
Translate darknet to tensorflow. Load trained weights, retrain/fine-tune using tensorflow, export constant graph def to mobile devices

Intro Real-time object detection and classification. Paper: version 1, version 2. Read more about YOLO (in darknet) and download weight files here. In

Trieu 6.1k Dec 30, 2022
Perspective: Julia for Biologists

Perspective: Julia for Biologists 1. Examples Speed: Example 1 - Single cell data and network inference Domain: Single cell data Methodology: Network

Elisabeth Roesch 55 Dec 02, 2022
High-resolution networks and Segmentation Transformer for Semantic Segmentation

High-resolution networks and Segmentation Transformer for Semantic Segmentation Branches This is the implementation for HRNet + OCR. The PyTroch 1.1 v

HRNet 2.8k Jan 07, 2023
Net2net - Network-to-Network Translation with Conditional Invertible Neural Networks

Net2Net Code accompanying the NeurIPS 2020 oral paper Network-to-Network Translation with Conditional Invertible Neural Networks Robin Rombach*, Patri

CompVis Heidelberg 206 Dec 20, 2022
Image-retrieval-baseline - MUGE Multimodal Retrieval Baseline

MUGE Multimodal Retrieval Baseline This repo is implemented based on the open_cl

47 Dec 16, 2022
Add-on for importing and auto setup of character creator 3 character exports.

CC3 Blender Tools An add-on for importing and automatically setting up materials for Character Creator 3 character exports. Using Blender in the Chara

260 Jan 05, 2023
Yet Another Reinforcement Learning Tutorial

This repo contains self-contained RL implementations

Sungjoon 65 Dec 10, 2022
LERP : Label-dependent and event-guided interpretable disease risk prediction using EHRs

LERP : Label-dependent and event-guided interpretable disease risk prediction using EHRs This is the code for the LERP. Dataset The dataset used is MI

5 Jun 18, 2022
this is a lite easy to use virtual keyboard project for anyone to use

virtual_Keyboard this is a lite easy to use virtual keyboard project for anyone to use motivation I made this for this year's recruitment for RobEn AA

Mohamed Emad 3 Oct 23, 2021
Pytorch implementation of FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks

flownet2-pytorch Pytorch implementation of FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks. Multiple GPU training is supported, a

NVIDIA Corporation 2.8k Dec 27, 2022
Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity

[ICLR 2022] Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity by Shiwei Liu, Tianlong Chen, Zahra Atashgahi, Xiaohan Chen, Ghada Sokar, Elen

VITA 18 Dec 31, 2022
Deep Watershed Transform for Instance Segmentation

Deep Watershed Transform Performs instance level segmentation detailed in the following paper: Min Bai and Raquel Urtasun, Deep Watershed Transformati

193 Nov 20, 2022
Advancing mathematics by guiding human intuition with AI

Advancing mathematics by guiding human intuition with AI This repo contains two colab notebooks which accompany the paper, available online at https:/

DeepMind 315 Dec 26, 2022
A Comprehensive Empirical Study of Vision-Language Pre-trained Model for Supervised Cross-Modal Retrieval

CLIP4CMR A Comprehensive Empirical Study of Vision-Language Pre-trained Model for Supervised Cross-Modal Retrieval The original data and pre-calculate

24 Dec 26, 2022
Rayvens makes it possible for data scientists to access hundreds of data services within Ray with little effort.

Rayvens augments Ray with events. With Rayvens, Ray applications can subscribe to event streams, process and produce events. Rayvens leverages Apache

CodeFlare 32 Dec 25, 2022
Official implementation of Deep Burst Super-Resolution

Deep-Burst-SR Official implementation of Deep Burst Super-Resolution Publication: Deep Burst Super-Resolution. Goutam Bhat, Martin Danelljan, Luc Van

Goutam Bhat 113 Dec 19, 2022
Python package facilitating the use of Bayesian Deep Learning methods with Variational Inference for PyTorch

PyVarInf PyVarInf provides facilities to easily train your PyTorch neural network models using variational inference. Bayesian Deep Learning with Vari

342 Dec 02, 2022