Codes and models of NeurIPS2021 paper - DominoSearch: Find layer-wise fine-grained N:M sparse schemes from dense neural networks

Related tags

Deep LearningDominoSearch
Overview

DominoSearch

This is repository for codes and models of NeurIPS2021 paper - DominoSearch: Find layer-wise fine-grained N:M sparse schemes from dense neural networks

Instructions and other materials will be released soon.

Search:

git clone https://github.com/NM-sparsity/DominoSearch.git
cd DominoSearch/DominoSearch/search/script_resnet_ImageNet

We provide several search scripts for different sparse-ratio target, you can specify your own target and change the parameters accordingly. Note, you need to first specify your ImageNet dataset path

The searching phase could take 2-3 hours, then you will get searched schemes stored in a txt file, which will be needed as input for mixed-sparsity training.

Below is an example of output formate.

{'SparseConv0_3-64-(7, 7)': [16, 16], 'SparseConv1_64-64-(1, 1)': [16, 16], 'SparseConv2_64-64-(3, 3)': [4, 16], 'SparseConv3_64-256-(1, 1)': [8, 16], 'SparseConv4_64-256-(1, 1)': [8, 16], 'SparseConv5_256-64-(1, 1)': [8, 16], 'SparseConv6_64-64-(3, 3)': [4, 16], 'SparseConv7_64-256-(1, 1)': [8, 16], 'SparseConv8_256-64-(1, 1)': [8, 16], 'SparseConv9_64-64-(3, 3)': [4, 16], 'SparseConv10_64-256-(1, 1)': [8, 16], 'SparseConv11_256-128-(1, 1)': [8, 16], 'SparseConv12_128-128-(3, 3)': [2, 16], 'SparseConv13_128-512-(1, 1)': [8, 16], 'SparseConv14_256-512-(1, 1)': [4, 16], 'SparseConv15_512-128-(1, 1)': [8, 16], 'SparseConv16_128-128-(3, 3)': [4, 16], 'SparseConv17_128-512-(1, 1)': [8, 16], 'SparseConv18_512-128-(1, 1)': [8, 16], 'SparseConv19_128-128-(3, 3)': [4, 16], 'SparseConv20_128-512-(1, 1)': [8, 16], 'SparseConv21_512-128-(1, 1)': [8, 16], 'SparseConv22_128-128-(3, 3)': [2, 16], 'SparseConv23_128-512-(1, 1)': [8, 16], 'SparseConv24_512-256-(1, 1)': [4, 16], 'SparseConv25_256-256-(3, 3)': [2, 16], 'SparseConv26_256-1024-(1, 1)': [4, 16], 'SparseConv27_512-1024-(1, 1)': [4, 16], 'SparseConv28_1024-256-(1, 1)': [4, 16], 'SparseConv29_256-256-(3, 3)': [2, 16], 'SparseConv30_256-1024-(1, 1)': [4, 16], 'SparseConv31_1024-256-(1, 1)': [4, 16], 'SparseConv32_256-256-(3, 3)': [2, 16], 'SparseConv33_256-1024-(1, 1)': [4, 16], 'SparseConv34_1024-256-(1, 1)': [4, 16], 'SparseConv35_256-256-(3, 3)': [2, 16], 'SparseConv36_256-1024-(1, 1)': [4, 16], 'SparseConv37_1024-256-(1, 1)': [4, 16], 'SparseConv38_256-256-(3, 3)': [2, 16], 'SparseConv39_256-1024-(1, 1)': [4, 16], 'SparseConv40_1024-256-(1, 1)': [4, 16], 'SparseConv41_256-256-(3, 3)': [2, 16], 'SparseConv42_256-1024-(1, 1)': [4, 16], 'SparseConv43_1024-512-(1, 1)': [4, 16], 'SparseConv44_512-512-(3, 3)': [2, 16], 'SparseConv45_512-2048-(1, 1)': [4, 16], 'SparseConv46_1024-2048-(1, 1)': [2, 16], 'SparseConv47_2048-512-(1, 1)': [4, 16], 'SparseConv48_512-512-(3, 3)': [2, 16], 'SparseConv49_512-2048-(1, 1)': [4, 16], 'SparseConv50_2048-512-(1, 1)': [4, 16], 'SparseConv51_512-512-(3, 3)': [2, 16], 'SparseConv52_512-2048-(1, 1)': [4, 16], 'Linear0_2048-1000': [4, 16]}

Train:

After getting the layer-wise sparse schemes, we need to fine-tune with the schemes to recover the accuracy. The training code is based on NM-sparsity, where we made some changes to support flexible N:M schemes.

Below is an example of training layer-wise sparse resnet50 with 80% overall sparsity.

cd DominoSearch\DominoSearch\train\classification_sparsity_level\train_imagenet
 python -m torch.distributed.launch --nproc_per_node=8 ../train_imagenet.py --config ./configs/config_resnet50.yaml  --base_lr 0.01 --decay 0.0005 --epochs 120 --schemes_file ./schemes/resnet50_M16_0.80.txt --model_dir ./resnet50/resnet50_0.80_M16

Experiments

We provide the trained models of the experiments. Please check our paper for details and intepretations of the experiments.

ResNet50 experiments in section 4.1

Model Name TOP1 Accuracy Trained Model Searched schemes
resnet50 - 0.80 model size 76.7 google drive google drive
resnet50 - 0.875 model size 75.7 google drive google drive
resnet50 - 0.9375 model size 73.5 google drive google drive
resnet50 - 8x FLOPs 75.4 google drive google drive
resnet50- 16x FLOPs 73.4 google drive google drive

Ablation experiments of ResNet50 in section 5.3

Model Name TOP1 Accuracy Trained Model Train log
Ablation E3 76.1 google drive google drive
Ablation E4 76.4 google drive google drive
Ablation E6 76.6 google drive google drive
Ablation E7 75.6 google drive google drive

Citation

@inproceedings{
sun2021dominosearch,
title={DominoSearch: Find layer-wise fine-grained N:M sparse schemes from dense neural networks},
author={Wei Sun and Aojun Zhou and Sander Stuijk and Rob G. J. Wijnhoven and Andrew Nelson and Hongsheng Li and Henk Corporaal},
booktitle={Thirty-Fifth Conference on Neural Information Processing Systems},
year={2021},
url={https://openreview.net/forum?id=IGrC6koW_g}
}
Owner
GitHub Repository
Official Code for VideoLT: Large-scale Long-tailed Video Recognition (ICCV 2021)

Pytorch Code for VideoLT [Website][Paper] Updates [10/29/2021] Features uploaded to Google Drive, for access please send us an e-mail: zhangxing18 at

Skye 26 Sep 18, 2022
Direct design of biquad filter cascades with deep learning by sampling random polynomials.

IIRNet Direct design of biquad filter cascades with deep learning by sampling random polynomials. Usage git clone https://github.com/csteinmetz1/IIRNe

Christian J. Steinmetz 55 Nov 02, 2022
Official PyTorch code of DeepPanoContext: Panoramic 3D Scene Understanding with Holistic Scene Context Graph and Relation-based Optimization (ICCV 2021 Oral).

DeepPanoContext (DPC) [Project Page (with interactive results)][Paper] DeepPanoContext: Panoramic 3D Scene Understanding with Holistic Scene Context G

Cheng Zhang 66 Nov 16, 2022
Hepsiburada - Hepsiburada Urun Bilgisi Cekme

Hepsiburada Urun Bilgisi Cekme from hepsiburada import Marka nike = Marka("nike"

Ilker Manap 8 Oct 26, 2022
Kinetics-Data-Preprocessing

Kinetics-Data-Preprocessing Kinetics-400 and Kinetics-600 are common video recognition datasets used by popular video understanding projects like Slow

Kaihua Tang 7 Oct 27, 2022
Official Code for ICML 2021 paper "Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline"

Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline Ankit Goyal, Hei Law, Bowei Liu, Alejandro Newell, Jia Deng Internati

Princeton Vision & Learning Lab 115 Jan 04, 2023
Dieser Scanner findet Websites, die nicht direkt in Suchmaschinen auftauchen, aber trotzdem erreichbar sind.

Deep Web Scanner Dieses Script findet Websites, die per IPv4-Adresse erreichbar sind und speichert deren Metadaten. Die Ausgabe im Terminal wird nach

Alex K. 30 Nov 18, 2022
Example-custom-ml-block-keras - Custom Keras ML block example for Edge Impulse

Custom Keras ML block example for Edge Impulse This repository is an example on

Edge Impulse 8 Nov 02, 2022
This repository accompanies the ACM TOIS paper "What can I cook with these ingredients?" - Understanding cooking-related information needs in conversational search

In this repository you find data that has been gathered when conducting in-situ experiments in a conversational cooking setting. These data include tr

6 Sep 22, 2022
Simple node deletion tool for onnx.

snd4onnx Simple node deletion tool for onnx. I only test very miscellaneous and limited patterns as a hobby. There are probably a large number of bugs

Katsuya Hyodo 6 May 15, 2022
Rethinking Transformer-based Set Prediction for Object Detection

Rethinking Transformer-based Set Prediction for Object Detection Here are the code for the ICCV paper. The code is adapted from Detectron2 and AdelaiD

Zhiqing Sun 62 Dec 03, 2022
Pytorch implementation of paper: "NeurMiPs: Neural Mixture of Planar Experts for View Synthesis"

NeurMips: Neural Mixture of Planar Experts for View Synthesis This is the official repo for PyTorch implementation of paper "NeurMips: Neural Mixture

James Lin 101 Dec 13, 2022
Novel and high-performance medical image classification pipelines are heavily utilizing ensemble learning strategies

An Analysis on Ensemble Learning optimized Medical Image Classification with Deep Convolutional Neural Networks Novel and high-performance medical ima

14 Dec 18, 2022
You Only Look Once for Panopitic Driving Perception

You Only 👀 Once for Panoptic 🚗 Perception You Only Look at Once for Panoptic driving Perception by Dong Wu, Manwen Liao, Weitian Zhang, Xinggang Wan

Hust Visual Learning Team 1.4k Jan 04, 2023
Official implementation of VaxNeRF (Voxel-Accelearated NeRF).

VaxNeRF Paper | Google Colab This is the official implementation of VaxNeRF (Voxel-Accelearated NeRF). VaxNeRF provides very fast training and slightl

naruya 132 Nov 21, 2022
Event sourced bank - A wide-and-shallow example using the Python event sourcing library

Event Sourced Bank A "wide but shallow" example of using the Python event sourci

3 Mar 09, 2022
Offline Reinforcement Learning with Implicit Q-Learning

Offline Reinforcement Learning with Implicit Q-Learning This repository contains the official implementation of Offline Reinforcement Learning with Im

Ilya Kostrikov 126 Jan 06, 2023
Differentiable Neural Computers, Sparse Access Memory and Sparse Differentiable Neural Computers, for Pytorch

Differentiable Neural Computers and family, for Pytorch Includes: Differentiable Neural Computers (DNC) Sparse Access Memory (SAM) Sparse Differentiab

ixaxaar 302 Dec 14, 2022
EMNLP 2021 Findings' paper, SCICAP: Generating Captions for Scientific Figures

SCICAP: Scientific Figures Dataset This is the Github repo of the EMNLP 2021 Findings' paper, SCICAP: Generating Captions for Scientific Figures (Hsu

Edward 26 Nov 21, 2022
the code for our CVPR 2021 paper Bilateral Grid Learning for Stereo Matching Network [BGNet]

BGNet This repository contains the code for our CVPR 2021 paper Bilateral Grid Learning for Stereo Matching Network [BGNet] Environment Python 3.6.* C

3DCV developer 87 Nov 29, 2022