Introduction

This tries to reproduce Capsule Network results in pytorch.

Installation

• Pre-reqs are found here
• git clone https://github.com/teju85/dockerfiles
• cd dockerfiles/ubuntu1604
• make pytorch90_70
• cd ../..
• git clone https://github.com/teju85/capsnet

USAGE

$ ./dockerfiles/scripts/launch -runas user pytorch:latest-90_70 /bin/bash
container$ cd /work/capsnet
container$ python net.py

Use the '-h' option to net.py to know more about its customizations.

Benchmarking

$ ./dockerfiles/scripts/launch -runas user pytorch:latest-90_70 /bin/bash
container$ cd /work/capsnet
container$ ./benchmark.sh

Accuracy/Perf Numbers

• uhat detach - detaching here means to perform backprop only for the last routing iteration
• training epochs of 25 is being used for comparisons below
• accuracy numbers are ratios between 0 and 1
• train and test timings are in seconds and are per epoch
• measurements are all taken using cuda 9.0 with cudnn 7.0

P100

Dataset	detach?	Train acc	Train time	Test acc	Test time
mnist	no	0.9953	41.269	0.9920	2.463
mnist	yes	0.9927	35.511	0.9909	2.580
cifar10	no	0.8693	54.802	0.6422	4.250
cifar10	yes	0.8281	45.726	0.6690	3.969

V100

Dataset	detach?	Train acc	Train time	Test acc	Test time
mnist	no	0.9953	24.670	0.9920	1.766
mnist	yes	0.9927	20.539	0.9909	1.744
cifar10	no	0.8794	31.602	0.6572	2.917
cifar10	yes	0.8290	26.718	0.6733	2.950

Notes

Regarding Cifar10

Main paper runs this dataset using an ensemble of 7 models to attain 10.6% test error. In here, we only run one model and that too keeping most of the hyper-params pretty much the same as those with MNIST.

Runtime differences between Cifar10 and Mnist

The increase in runtime when compared to Mnist is totally attributable to the difference in input image dimension between these 2 datasets.