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
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@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
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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.
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