avirambh / MSDNet-GCN

ICLR 2018 reproducibility challenge - Multi-Scale Dense Convolutional Networks for Efficient Prediction

MSDNet - reproducibility and applying GCN blocks with separable kernel

This repository contains a reproduction code (in PyTorch) for "MSDNet: Multi-Scale Dense Networks for Resource Efficient Image Classification"

Contents

1. Introduction
2. Usage

Introduction

MSDNet is a novel approach fo image classification with computational resource limits at test time. This repository provides an implementation based on the technical description provided in the paper. Currently this code implements the support for Cifar-10 and Cifar-100.

Moreover, this code integrates the support for GCN based layers instead of normal convolution layers, in order to reduce the model parameters.

Usage

Dependencies

• Python3
• PyTorch(0.1.12+)

Train

As an example, use the following command to train an MSDNet on Cifar10

python3 main.py --model msdnet -b 64 -j 2 cifar10 --msd-blocks 10 --msd-base 4 \
--msd-step 2 --msd-stepmode even --growth 6-12-24 --gpu 0

As an example, use the following command to train an MSDNet on Cifar100 with GCN block

python3 main.py --model msdnet -b 64 -j 2 cifar100 --msd-blocks 10 --msd-base 3 \
--msd-step 1 --msd-stepmode even --growth 6-12-24 --gpu 0  --msd-gcn --msd-gcn-kernel 5 \
--msd-share-weights --msd-all-gcn

Evaluation

We take the Cifar10 model trained above as an example.

To evaluate the trained model, use evaluate to evaluate from the default checkpoint directory:

python3 main.py --model msdnet -b 64 -j 2 cifar100 --msd-blocks 10 --msd-base 3 \
--msd-step 1 --msd-stepmode even --growth 6-12-24 --gpu 0 --msd-gcn --msd-gcn-kernel 5 \
--msd-share-weights --msd-all-gcn --resume --evaluate

Other Options

For detailed options, please python main.py --help

For more examples and using pre-trained models, please less script.sh