Code for DisCo: Remedy Self-supervised Learning on Lightweight Models with Distilled Contrastive Learning

Related tags

Deep Learningdistillationself-supervised-learning
Overview

DisCo: Remedy Self-supervised Learning on Lightweight Models with Distilled Contrastive Learning

Pytorch Implementation for DisCo: Remedy Self-supervised Learning on Lightweight Models with Distilled Contrastive Learning

If the project is useful to you, please give us a star. ⭐️

image

@article{gao2021disco,
  title={DisCo: Remedy Self-supervised Learning on Lightweight Models with Distilled Contrastive Learning},
  author={Gao, Yuting and Zhuang, Jia-Xin and Li, Ke and Cheng, Hao and Guo, Xiaowei and Huang, Feiyue and Ji, Rongrong and Sun, Xing},
  journal={arXiv preprint arXiv:2104.09124},
  year={2021}
}

Checkpoints

Teacher Models

Architecture Self-supervised Methods Model Checkpoints
ResNet152 MoCo-V2 Model
ResNet101 MoCo-V2 Model
ResNet50 MoCo-V2 Model

For teacher models such as ResNet-50*2 etc, we use their official implementation, which can be downloaded from their github pages.

Student Models by DisCo

Teacher/Students Efficient-B0 ResNet-18 Vit-Tiny XCiT-Tiny
ResNet-50 Model Model - -
ResNet-101 Model Model - -
ResNet-152 Model Model - -
ResNet-50*2 Model Model - -
ViT-Small - - Model -
XCiT-Small - - - Model

Requirements

  • Python3

  • Pytorch 1.6+

  • Detectron2

  • 8 GPUs are preferred

  • ImageNet, Cifar10/100, VOC, COCO

Run

Before running, we firstly move all data into share memory

cp /path/to/ImageNet /dev/shm

Pretrain Model

For pretraining baseline models with default hidden layer dimension in Tab1

# Switch to moco directory
cd moco

# R-50
python3 -u main_moco.py -a resnet50 --batch-size 256 --learning-rate 0.03 --mlp --moco-t 0.2 --aug-plus --cos --epochs 200 --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 --hidden 2048 /dev/shm/ 2>&1 | tee ./logs/std.log
python3 main_lincls.py -a resnet50 --learning-rate 3.0 --batch-size 256 --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 --pretrained /path/to/ckpt/checkpoint_0199.pth.tar /dev/shm/ 2>&1 | tee ./logs/std.log

# R-101
python3 -u main_moco.py -a resnet101 --batch-size 256 --learning-rate 0.03 --mlp --moco-t 0.2 --aug-plus --cos --epochs 200 --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 --hidden 2048 /dev/shm/ 2>&1 | tee ./logs/std.log
python3 main_lincls.py -a resnet101 --learning-rate 3.0 --batch-size 256 --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 --pretrained /path/to/ckpt/checkpoint_0199.pth.tar /dev/shm/ 2>&1 | tee ./logs/std.log

# R-152
python3 -u main_moco.py -a resnet152 --batch-size 256 --learning-rate 0.03 --mlp --moco-t 0.2 --aug-plus --cos --epochs 800 --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 --hidden 2048 /dev/shm/ 2>&1 | tee ./logs/std.log
python3 main_lincls.py -a resnet152 --learning-rate 3.0 --batch-size 256 --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 --pretrained /path/to/ckpt/checkpoint_0799.pth.tar /dev/shm/ 2>&1 | tee ./logs/std.log

# Mob
python3 -u main_moco.py -a mobilenetv3 --batch-size 256 --learning-rate 0.03 --mlp --moco-t 0.2 --aug-plus --cos --epochs 200 --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 --hidden 512 /dev/shm 2>&1 |  tee ./logs/std.log
#          Evaluation
python3 main_lincls.py -a mobilenetv3 --learning-rate 3.0 --batch-size 256 --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 --pretrained /path/to/ckpt/checkpoint_0199.pth.tar /dev/shm/ 2>&1 | tee ./logs/std.log

# Effi-B0
python3 -u main_moco.py -a efficientb0 --batch-size 256 --learning-rate 0.03 --mlp --moco-t 0.2 --aug-plus --cos --epochs 200 --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 --hidden 1280 2>&1  |  tee ./logs/std.log
#          Evaluation
python3 main_lincls.py -a efficientb0 --learning-rate 3.0 --batch-size 256 --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 --pretrained /path/to/ckpt/checkpoint_0199.pth.tar /dev/shm/ 2>&1 | tee ./logs/std.log

# Effi-B1
python3 -u main_moco.py -a efficientb1 --batch-size 256 --learning-rate 0.03 --mlp --moco-t 0.2 --aug-plus --cos --epochs 200 --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0  --hidden 1280  /dev/shm  2>&1 | tee ./logs/std.log
#          Evaluation
python3 main_lincls.py -a efficientb1 --learning-rate 3.0 --batch-size 256 --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 --pretrained /path/to/ckpt/checkpoint_0199.pth.tar /dev/shm/ 2>&1 | tee ./logs/std.log

# R-18
python3 -u main_moco.py -a resnet18 --batch-size 256 --learning-rate 0.03 --mlp --moco-t 0.2 --aug-plus --cos --epochs 200 --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 --hidden 1280 /dev/shm/ 2>&1 | tee ./logs/std.log
#          Evaluation
python3 main_lincls.py -a resnet18 --learning-rate 3.0 --batch-size 256 --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 --pretrained /path/to/ckpt/checkpoint_0199.pth.tar /dev/shm/ 2>&1 | tee ./logs/std.log

# R-34
python3 -u main_moco.py -a resnet34 --batch-size 256 --learning-rate 0.03 --mlp --moco-t 0.2 --aug-plus --cos --epochs 200 --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 --hidden 1280 /dev/shm/ 2>&1 | tee ./logs/std.log
#          Evaluation
python3 main_lincls.py -a resnet34 --learning-rate 3.0 --batch-size 256 --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 --pretrained /path/to/ckpt/checkpoint_0199.pth.tar /dev/shm/ 2>&1 | tee ./logs/std.log

DisCo

For training DisCo in Tab1, Comparision with baseline

# Switch to DisCo directory
cd DisCo

# R-50 & Effib0
python3 -u main.py -a efficientb0 --lr 0.03 --batch-size 256 --moco-t 0.2 --aug-plus --dist-url 'tcp://localhost:10043' --multiprocessing-distributed --world-size 1 --rank 0 --mlp --cos --teacher_arch resnet50 --teacher /path/to/ckpt/checkpoint_0199.pth.tar /dev/shm/ 2>&1 | tee ./logs/std.log
#          Evaluation
python3 -u main_lincls.py -a efficientb0 --learning-rate 3.0 --batch-size 256 --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 --pretrained /path/to/ckpt/checkpoint_0199.pth.tar /dev/shm 2>&1 | tee ./logs/std.log

# R50w2 & Effib0
python3 -u main.py -a efficientb0 --lr 0.03 --batch-size 256 --moco-t 0.2 --aug-plus --dist-url 'tcp://localhost:10043' --multiprocessing-distributed --world-size 1 --rank 0 --mlp --cos --teacher_arch resnet50w2 --teacher /path/to/swav_RN50w2_400ep_pretrain.pth.tar /dev/shm 2>&1 | tee ./logs/std.log
#          Evaluation
python3 yt_main_lincls.py -a resnet18 --learning-rate 30.0 --batch-size 256 --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 --pretrained /path/to/ckpt/checkpoint_0199.pth.tar  /dev/shm 2>&1 | tee ./logs/std.log

For Tab2, Linear evaluation top-1 accuracy (%) on ImageNet compared with different distillation methods.

# RKD+DisCo, Eff-b0
python3 -u main_moco_distill_rkd.py -a efficientb0 --lr 0.03 --batch-size 256 --moco-t 0.2 --aug-plus --dist-url 'tcp://localhost:10043' --multiprocessing-distributed --world-size 1 --rank 0 --mlp --cos --teacher /path/to/teacher_res50.pth.tar --use-mse /dev/shm  2>&1 | tee ./logs/std.log
#                  Evaluation
python3 -u main_lincls.py -a efficientb0 --learning-rate 3.0 --batch-size 256 --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 --pretrained /path/to/ckpt/checkpoint_0199.pth.tar /dev/shm 2>&1 | tee ./logs/std.log

# RKD, Eff-b0
python3 -u main_moco_distill_rkd.py -a efficientb0 --lr 0.03 --batch-size 256 --moco-t 0.2 --aug-plus --dist-url 'tcp://localhost:10043' --multiprocessing-distributed --world-size 1 --rank 0 --mlp --cos --teacher /path/to/teacher_res50.pth.tar /dev/shm  2>&1 | tee ./logs/std.log
#                  Evaluation
python3 -u main_lincls.py -a efficientb0 --learning-rate 3.0 --batch-size 256 --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 --pretrained /path/to/ckpt/checkpoint_0199.pth.tar /dev/shm 2>&1 | tee ./logs/std.log

For Tab3 , **Object detection and instance segmentation results **

# Cp data to /dev/shm and set up path for Detectron2
cp -r /path/to/VOCdevkit/* /dev/shm/
cp -r /path/to/coco_2017 /dev/shm/coco
export DETECTRON2_DATASETS=/dev/shm

pip install /youtu-reid/jiaxzhuang/acmm/detectron2-0.4+cu101-cp36-cp36m-linux_x86_64.whl
cd detection

# Convert model for Detectron2
python3 convert-pretrain-to-detectron2.py /path/ckpt/checkpoint_0199.pth.tar ./output.pkl

# Evaluation on VOC
python3 train_net.py --config-file configs/pascal_voc_R_50_C4_24k_moco.yaml --num-gpus 8 --resume MODEL.RESNETS.DEPTH 34 MODEL.RESNETS.RES2_OUT_CHANNELS 64 2>&1 | tee ../logs/std.log
# Evaluation on CoCo
python3 train_net.py --config-file configs/coco_R_50_C4_2x_moco.yaml --num-gpus 8  --resume MODEL.RESNETS.DEPTH 18 MODEL.RESNETS.RES2_OUT_CHANNELS 64 2>&1 | tee ../logs/std.log

For Fig5 , evaluation on Semi-Supervised Tasks

# Copy 1%, 10% ImageNet from the complete ImageNet, according to split from SimCLR.
cd data
# Need to set up path to Compelete ImageNet and the output path.
python3 -u imagenet_1_fraction.py --ratio 1
python3 -u imagenet_1_fraction.py --ratio 10

# Evaluation on 1% ImageNet with Eff-B0 by DisCo
cp -r /path/to/imagenet_1_fraction/train  /dev/shm
cp -r /path/to/imagenet_1_fraction/val  /dev/shm/
python3 -u main_lincls_semi.py -a efficientb0 --learning-rate 3.0 --batch-size 256 --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 --pretrained /path/to/ckpt/checkpoint_0199.pth.tar /dev/shm  2>&1 | tee ./logs/std.log

# Evaluation on 10% ImageNet with R-18 by DisCo
cp -r /path/to/imagenet_10_fraction/train  /dev/shm
cp -r /path/to/imagenet_10_fraction/val  /dev/shm/
python3 -u main_lincls_semi.py -a resnet18 --learning-rate 3.0 --batch-size 256 --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 --pretrained /path/to/ckpt/checkpoint_0199.pth.tar /dev/shm  2>&1 | tee ./logs/std.log

For Fig6, evaluation on Cifar10/Cifar100

# Copy Cifar10/100 to /dev/shm
cp /path/to/Cifar10/100 /dev/shm

# Evaluation on 1% Cifar10 with Eff-B0 by DisCo
python3 cifar_main_lincls.py -a efficientb0 --dataset cifar10 --lr 3 --epochs 200 --pretrained /path/to/ckpt/checkpoint_0199.pth.tar /dev/shm 2>&1 | tee ./logs/std.log
# Evaluation on  Cifar100 with Resnet18 by DisCo
python3 cifar_main_lincls.py -a resnet18 --dataset cifar100 --lr 3 --epochs 200 --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 --pretrained /path/to/ckpt/checkpoint_0199.pth.tar /dev/shm 2>&1 | tee ./logs/std.log

For Tab4, Linear evaluation top-1 accuracy (%) on ImageNet, compared with SEED with consistent dimension in hidden layer.

python3 -u main.py -a efficientb0 --lr 0.03 --batch-size 256 --moco-t 0.2 --aug-plus --dist-url 'tcp://localhost:10043' --multiprocessing-distributed --world-size 1 --rank 0 --mlp --cos --teacher_arch resnet50 --teacher /path/to/ckpt/checkpoint_0199.pth.tar --hidden 2048 /dev/shm/ 2>&1 | tee ./logs/std.log
#          Evaluation
python3 -u main_lincls.py -a efficientb0 --learning-rate 3.0 --batch-size 256 --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 --pretrained /path/to/ckpt/checkpoint_0199.pth.tar /dev/shm 2>&1 | tee ./logs/std.log

For Tab5, Linear evaluation top-1 accuracy (%) on ImageNet with SwAV as the testbed.

# SwAV, Train with SwAV only
cd swav-master
python3 -m torch.distributed.launch --nproc_per_node=8 main_swav.py \
        --data_path /dev/shm/train \
        --base_lr 0.6 \
        --final_lr 0.0006 \
        --warmup_epochs 0 \
        --crops_for_assign 0 1 \
        --size_crops 224 96 \
        --nmb_crops 2 6 \
        --min_scale_crops 0.14 0.05 \
        --max_scale_crops 1. 0.14 \
        --use_fp16 true \
        --freeze_prototypes_niters 5005 \
        --queue_length 3840 \
        --epoch_queue_starts 15 \
        --dump_path ./ckpt \
        --sync_bn pytorch \
        --temperature 0.1 \
        --epsilon 0.05 \
        --sinkhorn_iterations 3 \
        --feat_dim 128 \
        --nmb_prototypes 3000 \
        --epochs 200 \
        --batch_size 64 \
        --wd 0.000001 \
        --arch efficientb0 \
        --use_fp16 true 2>&1 | tee ./logs/std.log
# Evaluation
python3 -m torch.distributed.launch --nproc_per_node=8 eval_linear.py --arch efficientb0 --data_path /dev/shm --pretrained /path/to/ckpt/checkpoints/ckp-199.pth 2>&1 | tee ./logs/std.log

# DisCo + SwAV
python3 -m torch.distributed.launch --nproc_per_node=8 main_swav_distill.py \
        --data_path /dev/shm/train \
        --base_lr 0.6 \
        --final_lr 0.0006 \
        --warmup_epochs 0 \
        --crops_for_assign 0 1 \
        --size_crops 224 96 \
        --nmb_crops 2 6 \
        --min_scale_crops 0.14 0.05 \
        --max_scale_crops 1. 0.14 \
        --use_fp16 true \
        --freeze_prototypes_niters 5005 \
        --queue_length 3840 \
        --epoch_queue_starts 15 \
        --dump_path ./ckpt \
        --sync_bn pytorch \
        --temperature 0.1 \
        --epsilon 0.05 \
        --sinkhorn_iterations 3 \
        --feat_dim 128 \
        --nmb_prototypes 3000 \
        --epochs 200 \
        --batch_size 64 \
        --wd 0.000001 \
        --arch efficientb0 \
        --pretrained /path/to/swav_800ep_pretrain.pth.tar 2>&1 | tee ./logs/std.log

For Tab6, Linear evaluation top-1 accuracy (%) on ImageNet with variants of teacher pre-training methods.

# SwAV
python3 -u main.py -a resnet34 --lr 0.03 --batch-size 256 --moco-t 0.2 --aug-plus --dist-url 'tcp://localhost:10043' --multiprocessing-distributed --world-size 1 --rank 0 --mlp --cos --teacher_arch SWAVresnet50 --teacher /path/to/swav_800ep_pretrain.pth.tar /dev/shm 2>&1 | tee ./logs/std.log

Visualization

cd DisCo
# Generate Embed
# Move Embed to data path

python -u draw.py

Thanks

Code heavily depends on MoCo-V2, Detectron2.

Owner
GitHub Repository
Evolution Strategies in PyTorch

Evolution Strategies This is a PyTorch implementation of Evolution Strategies. Requirements Python 3.5, PyTorch = 0.2.0, numpy, gym, universe, cv2 Wh

Andrew Gambardella 333 Nov 14, 2022
Text to Image Generation with Semantic-Spatial Aware GAN

text2image This repository includes the implementation for Text to Image Generation with Semantic-Spatial Aware GAN This repo is not completely. Netwo

CVDDL 124 Dec 30, 2022
A state of the art of new lightweight YOLO model implemented by TensorFlow 2.

CSL-YOLO: A New Lightweight Object Detection System for Edge Computing This project provides a SOTA level lightweight YOLO called "Cross-Stage Lightwe

Miles Zhang 54 Dec 21, 2022
Repository for reproducing `Model-Based Robust Deep Learning`

Model-Based Robust Deep Learning (MBRDL) In this repository, we include the code necessary for reproducing the code used in Model-Based Robust Deep Le

Alex Robey 16 Sep 19, 2022
A benchmark dataset for mesh multi-label-classification based on cube engravings introduced in MeshCNN

Double Cube Engravings This script creates a dataset for multi-label mesh clasification, with an intentionally difficult setup for point cloud classif

Yotam Erel 1 Nov 30, 2021
Predicting Semantic Map Representations from Images with Pyramid Occupancy Networks

This is the code associated with the paper Predicting Semantic Map Representations from Images with Pyramid Occupancy Networks, published at CVPR 2020.

Thomas Roddick 219 Dec 20, 2022
Graph Transformer Architecture. Source code for

Graph Transformer Architecture Source code for the paper "A Generalization of Transformer Networks to Graphs" by Vijay Prakash Dwivedi and Xavier Bres

NTU Graph Deep Learning Lab 561 Jan 08, 2023
Complete-IoU (CIoU) Loss and Cluster-NMS for Object Detection and Instance Segmentation (YOLACT)

Complete-IoU Loss and Cluster-NMS for Improving Object Detection and Instance Segmentation. Our paper is accepted by IEEE Transactions on Cybernetics

290 Dec 25, 2022
Metric learning algorithms in Python

metric-learn: Metric Learning in Python metric-learn contains efficient Python implementations of several popular supervised and weakly-supervised met

1.3k Jan 02, 2023
This is the repository for our paper SimpleTrack: Understanding and Rethinking 3D Multi-object Tracking

SimpleTrack This is the repository for our paper SimpleTrack: Understanding and Rethinking 3D Multi-object Tracking. We are still working on writing t

TuSimple 189 Dec 26, 2022
Content shared at DS-OX Meetup

Streamlit-Projects Streamlit projects available in this repo: An introduction to Streamlit presented at DS-OX (Feb 26, 2020) meetup Streamlit 101 - Ja

Arvindra 69 Dec 23, 2022
Video Instance Segmentation with a Propose-Reduce Paradigm (ICCV 2021)

Propose-Reduce VIS This repo contains the official implementation for the paper: Video Instance Segmentation with a Propose-Reduce Paradigm Huaijia Li

DV Lab 39 Nov 23, 2022
Simple (but Strong) Baselines for POMDPs

Recurrent Model-Free RL is a Strong Baseline for Many POMDPs Welcome to the POMDP world! This repo provides some simple baselines for POMDPs, specific

Tianwei V. Ni 172 Dec 29, 2022
Concept drift monitoring for HA model servers.

{Fast, Correct, Simple} - pick three Easily compare training and production ML data & model distributions Goals Boxkite is an instrumentation library

98 Dec 15, 2022
An Implementation of SiameseRPN with Feature Pyramid Networks

SiameseRPN with FPN This project is mainly based on HelloRicky123/Siamese-RPN. What I've done is just add a Feature Pyramid Network method to the orig

3 Apr 16, 2022
Official implementation of the NeurIPS 2021 paper Online Learning Of Neural Computations From Sparse Temporal Feedback

Online Learning Of Neural Computations From Sparse Temporal Feedback This repository is the official implementation of the NeurIPS 2021 paper Online L

Lukas Braun 3 Dec 15, 2021
a minimal terminal with python 😎😉

Meterm a terminal with python 😎 How to use Clone Project: $ git clone https://github.com/motahharm/meterm.git Run: in Terminal: meterm.exe Or pip ins

Motahhar.Mokfi 5 Jan 28, 2022
Its a Plant Leaf Disease Detection System based on Machine Learning.

My_Project_Code Its a Plant Leaf Disease Detection System based on Machine Learning. I have used Tomato Leaves Dataset from kaggle. This system detect

Sanskriti Sidola 3 Jun 15, 2022
A testcase generation tool for Persistent Memory Programs.

PMFuzz PMFuzz is a testcase generation tool to generate high-value tests cases for PM testing tools (XFDetector, PMDebugger, PMTest and Pmemcheck) If

Systems Research at ShiftLab 14 Jul 24, 2022
SCALoss: Side and Corner Aligned Loss for Bounding Box Regression (AAAI2022).

SCALoss PyTorch implementation of the paper "SCALoss: Side and Corner Aligned Loss for Bounding Box Regression" (AAAI 2022). Introduction IoU-based lo

TuZheng 20 Sep 07, 2022