Snip

SNIP: Single-shot network pruning based on connection sensitivity

This is an unofficial PyTorch implementation of the paper SNIP: Single-shot Network Pruning based on Connection Sensitivity by Namhoon Lee, Thalaiyasingam Ajanthan and Philip H. S. Torr.

It doesn not cover all the experiment in the paper but it does include the main ones:

• LeNet5-Caffe on MNIST
• VGG-D on CIFAR-10

I haven't had the time to add an argparser yet the network type and pruning level should be changed directly in the code.

Environment

This has been tested with Python 3.7.1 and PyTorch 1.0.0. The exact environment can be replicated by:

$ conda env create -f environment.yml

This would create a conda environment called snip-env.

Usage

$ conda activate snip-env
$ python train.py

Results

Three runs with different seeds with LeNet5-Caffe on MNIST (sparsity level of 98%):

<img src="results_mnist.svg" alt="Results with LeNet5-Caffe - Sparsity 9%" width="95%"/>

Two runs with different seeds with VGG-D on CIFAR-10 (sparsity level of 95%):

<img src="results_cifar10.svg" alt="Results with VGG-D - Sparsity 95%" width="95%"/>