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
Open-source codebase for EfficientZero, from "Mastering Atari Games with Limited Data" at NeurIPS 2021.

EfficientZero (NeurIPS 2021) Open-source codebase for EfficientZero, from "Mastering Atari Games with Limited Data" at NeurIPS 2021. Thank you for you

Weirui Ye 671 Jan 03, 2023
Using contrastive learning and OpenAI's CLIP to find good embeddings for images with lossy transformations

Creating Robust Representations from Pre-Trained Image Encoders using Contrastive Learning Sriram Ravula, Georgios Smyrnis This is the code for our pr

Sriram Ravula 26 Dec 10, 2022
An 16kHz implementation of HiFi-GAN for soft-vc.

HiFi-GAN An 16kHz implementation of HiFi-GAN for soft-vc. Relevant links: Official HiFi-GAN repo HiFi-GAN paper Soft-VC repo Soft-VC paper Example Usa

Benjamin van Niekerk 42 Dec 27, 2022
The Unsupervised Reinforcement Learning Benchmark (URLB)

The Unsupervised Reinforcement Learning Benchmark (URLB) URLB provides a set of leading algorithms for unsupervised reinforcement learning where agent

259 Dec 26, 2022
PyTorch implementation for Convolutional Networks with Adaptive Inference Graphs

Convolutional Networks with Adaptive Inference Graphs (ConvNet-AIG) This repository contains a PyTorch implementation of the paper Convolutional Netwo

Andreas Veit 176 Dec 07, 2022
Easy genetic ancestry predictions in Python

ezancestry Easily visualize your direct-to-consumer genetics next to 2500+ samples from the 1000 genomes project. Evaluate the performance of a custom

Kevin Arvai 38 Jan 02, 2023
Flexible time series feature extraction & processing

tsflex is a toolkit for flexible time series processing & feature extraction, that is efficient and makes few assumptions about sequence data. Useful

PreDiCT.IDLab 206 Dec 28, 2022
Template repository for managing machine learning research projects built with PyTorch-Lightning

Tutorial Repository with a minimal example for showing how to deploy training across various compute infrastructure.

Sidd Karamcheti 3 Feb 11, 2022
Hide screen when boss is approaching.

BossSensor Hide your screen when your boss is approaching. Demo The boss stands up. He is approaching. When he is approaching, the program fetches fac

Hiroki Nakayama 6.2k Jan 07, 2023
Generating retro pixel game characters with Generative Adversarial Networks. Dataset "TinyHero" included.

pixel_character_generator Generating retro pixel game characters with Generative Adversarial Networks. Dataset "TinyHero" included. Dataset TinyHero D

Agnieszka Mikołajczyk 88 Nov 17, 2022
Pytorch implementation of Straight Sampling Network For Point Cloud Learning (ICIP2021).

Pytorch code for SS-Net This is a pytorch implementation of Straight Sampling Network For Point Cloud Learning (ICIP2021). Environment Code is tested

Sun Ran 1 May 18, 2022
Alpha-Zero - Telegram Group Manager Bot Written In Python Using Pyrogram

✨ Alpha Zero Bot ✨ Telegram Group Manager Bot + Userbot Written In Python Using

1 Feb 17, 2022
The PyTorch implementation of paper REST: Debiased Social Recommendation via Reconstructing Exposure Strategies

REST The PyTorch implementation of paper REST: Debiased Social Recommendation via Reconstructing Exposure Strategies. Usage Download dataset Download

DMIRLAB 2 Mar 13, 2022
A toolkit for document-level event extraction, containing some SOTA model implementations

❤️ A Toolkit for Document-level Event Extraction with & without Triggers Hi, there 👋 . Thanks for your stay in this repo. This project aims at buildi

Tong Zhu（朱桐） 159 Dec 22, 2022
Utilities to bridge Canvas-generated course rosters with GitLab's API.

gitlab-canvas-utils A collection of scripts originally written for CSE 13S. Oversees everything from GitLab course group creation, student repository

Eugene Chou 5 Jun 08, 2022
Python scripts form performing stereo depth estimation using the CoEx model in ONNX.

ONNX-CoEx-Stereo-Depth-estimation Python scripts form performing stereo depth estimation using the CoEx model in ONNX. Stereo depth estimation on the

Ibai Gorordo 8 Dec 29, 2022
RLMeta is a light-weight flexible framework for Distributed Reinforcement Learning Research.

RLMeta rlmeta - a flexible lightweight research framework for Distributed Reinforcement Learning based on PyTorch and moolib Installation To build fro

Meta Research 281 Dec 22, 2022
CS50x-AI - Artificial Intelligence with Python from Harvard University

CS50x-AI Artificial Intelligence with Python from Harvard University 📖 Table of

Hosein Damavandi 6 Aug 22, 2022
Hands-On Machine Learning for Algorithmic Trading, published by Packt

Hands-On Machine Learning for Algorithmic Trading Hands-On Machine Learning for Algorithmic Trading, published by Packt This is the code repository fo

Packt 981 Dec 29, 2022
Pytorch implementation of the paper Progressive Growing of Points with Tree-structured Generators (BMVC 2021)

PGpoints Pytorch implementation of the paper Progressive Growing of Points with Tree-structured Generators (BMVC 2021) Hyeontae Son, Young Min Kim Pre

Hyeontae Son 9 Jun 06, 2022