Model Preparation Algorithm

Introduction

Model Preparation is a stage in DL developer's workflow in which users could create or re-train models based on given dataset. This package provides various types of Model Preparation Algorithm (MPA) as the form of Transfer Learning Recipe (TL-Recipe).

TL-Recipe

Given datasets and base models as inputs, TL-Recipe offers model adaptation schemes in terms of data domain, model tasks and also NN architectures.

This package consists of

• Custom core modules which implement various TL algorithms
• Training configurations specifying module selection & hyper-parameters
• Tools and APIs to run transfer learning on given datasets & models

Design Concept

TL ( RECIPE, MODEL, DATA[, H-PARAMS] ) -> MODEL

• Model: NN arch + params to be transfer-learned
  • Explicitly enabled models only
  • No model input -> train default model from scratch
• Recipe: Custom modules & configs defining transfer learning schemes / modes
  • Defined up to NN arch (e.g. Incremental learning recipe for FasterRCNN, SSD, etc)
  • Expose & accept controllable hyper-params w/ default value

Archtecture

TL-Recipe is based on pytorch as base training framework. And MMCV is adopted as modular configuration framework. Recipes in this package are implemented using config & module libraries which are also based on MMCV

• MMClassification
• MMDetection (OTE version)
• MMSegmentation (OTE version)

On top of above framework and libraries, we designed Multi-stage Workflow Framework

• Workflow: Model training w/ series of sub-stages (RECIPE)
• Stage: Individual (pre)train tasks (SUB-RECIPE)

Features

TL-Recipe supports various TL methods for Computer Vision tasks and their related NN model architecures.

TL Tasks

• Classification
• Detection
• Segmentation
  • (WIP) Instance segmentation
  • Semantic segmentation
  • (TBD) Panoptic segmentation

Train / Infer / Evaluate / Export operations are supported for each task

TL Methods

Follwing table describes supported TL methods for each tasks.

• Class-Incremental Learning
• Self-Supervised Learning (available soon!)
• Semi-Supervised Learning (available soon!)

TL Models

TL-Recipe supports transfer learning for subset of

• OTE (OpenVINO Traning Exetension) models
• Some custom pytorch models

For detailed list, please refer to: OTE Model Preparation Algorithms

Interface

Users could use TL-Recipe to retrain their models via

• Model templates
• CLI
• Python APIs

Changelog

Please refer to: TBD

Developer's Guide

System Requirement

• Ubuntu 16.04 / 18.04 / 20.04 LTS x64
• Intel Core/Xeon
• (optional) NVIDIA GPUs
  • tested with
    • GTX 1080Ti
    • RTX 3080
    • RTX 3090
    • Tesla V100

Prerequisites

To use MPA on your system, there are two different way to setup the environment for the development.

• Setup environment on your host machine using virtual environments (virtualenv or conda)
• Setup environment using Docker

For the contributors

To contribute your code to the MPA project, there is a restriction to do liniting to all your changes. we are using Flake8 for doing this and it's recommended to do it using the Git hook before commiting your changes.

• how to install: please go to the link below to install on your development environment

  https://pre-commit.com/#install

• For example, you can install Flake8 and pre-commit using Anaconda

  # install flake8 to your development environment (Anaconda)
  $ conda install flake8=3.9 -y
  # install pre-commit module
  $ conda install pre-commit -c conda-forge -y
  # install pre-commit hook to the local git repo
  $ pre-commit install
  

  note: After installing and setting Flake8 using instructions above, your commit action will be ignored when your changes (staged files) have any LINT issues.

Setup environment on your host machine

We provided a shell script init_venv.sh to make virtual environment for the MPA using tool virtualenv.

# init submodules
...mpa$ git submodule update --init --recursive
# create virtual environment to the path ./venv and named as 'mpa'
...mpa$ ./init_venv.sh ./venv
# activate created virtualenv
...mpa$ source ./venv/bin/activate
# run mpa cli to get help message
(mpa)...mpa$ python -m tools.cli -h

note: This script assume that your system has installed suitable version of CUDA (10.2 or 11.1) for the MPA.

Quick start

Refer to OTE CLI quide