i-SpaSP: Structured Neural Pruning via Sparse Signal Recovery

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Deep Learningi-SpaSP
Overview

i-SpaSP: Structured Neural Pruning via Sparse Signal Recovery

This is a public code repository for the publication:

i-SpaSP: Structured Neural Pruning via Sparse Signal Recovery
Cameron R Wolfe, Anastasios Kyrillidis

Environment/Dependencies

Requires anaconda to be installed (python3) Anaconda can be installed at https://www.anaconda.com/products/individual

conda create -n ispasp python=3.6 anaconda
conda activate ispasp
conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch
pip install -r requirements.txt

File Arrangement

Here we summarize all files present in this repo and their purpose.

+-- prune_resnet34_ispasp.py : i-SpaSP implementation for ResNet34
+-- prune_mobilenetv2_ispasp.py : i-SpaSP implementation for MobileNetV2
+-- lib/ : utility/helper functions
|   +-- data.py: helper functions for handling data
|   +-- utils.py: helper functions for computing performance metrics
+-- scripts/ : contains python scripts for running pruning experiments
|   +-- prune_rn34_ispasp.py: run an i-SpaSP pruning experiment for ResNet34  
|   +-- prune_mbnv2_ispasp.py: run an i-SpaSP pruning experiment for MobileNetV2
+-- requirements.txt : dependencies for pruning experiments
Owner
Cameron Ronald Wolfe
Research Scientist at Alegion; PhD Student at Rice University
Cameron Ronald Wolfe
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