Continual learning using hash-routed convolutional neural networks

Description

This software package contains the code to run and evaluate HRN, based on the paper Continual learning using hash-routed convolutional neural networks (https://arxiv.org/abs/2010.05880)

1- System requirements

This software package was developed and tested using the following configuration:

Hardware: At least 8 GB of RAM and preferably a NVIDIA P100 GPU (tested using CUDA 10.1 and libcudnn7)

OS: Tested on Ubuntu 18.04 LTS and 20.04 LTS

Python: Tested using Python 3.7 and python 3.8 (Anaconda3 2020.02 and Anaconda 2020.07)

2- Installation

The requirements.txt file contains pip dependencies needed to run the software.

pip install -r requirements.txt

The software can also be installed as python package:

pip install -e hrn/

3- Training

If the software was not installed as python package, add the path to the hrn folder to PYTHONPATH.

Go into the software package root folder

cd hrn

To train a HRN, run:

python Scripts/training/training_s.py Scripts/conf/pmnist.yaml cpu

If your machine has one or more GPUs, you can run:

python Scripts/training/training_s.py Scripts/conf/pmnist.yaml cuda -d 0

(this selects GPU 0 for training)

The Scripts/conf folder contains YAML configuration files that can be used to describe the HRN and the scenarios to run.

4- Data analysis

This software uses MLflow to log training metrics . Running a training script will create the hrn/mlruns folder containing the MLflow database. Tensorboard can also be used to view realtime metrics. Logs can be found in the created ../data folder.

5- Hyperparameters search

It is possible to search for optimal hyperparameters using HyperOpt and jinja2 . The generic yaml file is given as a jinja2 template (see Scripts/conf/tiny .yaml.j2 as an example). The generic fields exploration space is given in the python script (see Scripts/hypertrain_tiny_s.py as an example). The hyperparemeter search can be run as such:

python Scripts/training/hypertrain_tiny_s.py $(pwd)/Scripts/conf/ tiny.yaml.j2 cuda 0

The first parameter is the absolute path to the folder containing the jinja2 template and the second parameter is the name of the template file.

6- Paper experiments

Configuration files used for the experiments presented in the paper can be found in the Scripts/conf/paper folder. The Scripts/conf/paper/ablation folder contains the configuration files used for the ablation study (see the description inside each file for more detail). Scripts for Elastic Weight Consolidation (EWC), Encoder based Lifelong Learning (ELL) and Vanilla Convolution (VC) are available in the Scripts/benchmarks folder. For example, to run a vanilla convolution on a pairwise-MNIST configuration:

python Scripts/training/benchmarks/training_vc.py Scripts/conf/paper/vc/pmnist_vc.yaml cuda -d 0