[NeurIPS'20] Self-supervised Co-Training for Video Representation Learning. Tengda Han, Weidi Xie, Andrew Zisserman.

Related tags

Deep LearningCoCLR
Overview

CoCLR: Self-supervised Co-Training for Video Representation Learning

arch

This repository contains the implementation of:

  • InfoNCE (MoCo on videos)
  • UberNCE (supervised contrastive learning on videos)
  • CoCLR

Link:

[Project Page] [PDF] [Arxiv]

News

  • [2021.01.29] Upload both RGB and optical flow dataset for UCF101 (links).
  • [2021.01.11] Update our paper for NeurIPS2020 final version: corrected InfoNCE-RGB-linearProbe baseline result in Table1 from 52.3% (pretrained for 800 epochs, unnessary and unfair) to 46.8% (pretrained for 500 epochs, fair comparison). Thanks @liuhualin333 for pointing out.
  • [2020.12.08] Update instructions.
  • [2020.11.17] Upload pretrained weights for UCF101 experiments.
  • [2020.10.30] Update "draft" dataloader files, CoCLR code, evaluation code as requested by some researchers. Will check and add detailed instructions later.

Pretrain Instruction

  • InfoNCE pretrain on UCF101-RGB
CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch \
--nproc_per_node=2 main_nce.py --net s3d --model infonce --moco-k 2048 \
--dataset ucf101-2clip --seq_len 32 --ds 1 --batch_size 32 \
--epochs 300 --schedule 250 280 -j 16
  • InfoNCE pretrain on UCF101-Flow
CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch \
--nproc_per_node=2 main_nce.py --net s3d --model infonce --moco-k 2048 \
--dataset ucf101-f-2clip --seq_len 32 --ds 1 --batch_size 32 \
--epochs 300 --schedule 250 280 -j 16
  • CoCLR pretrain on UCF101 for one cycle
CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch \
--nproc_per_node=2 main_coclr.py --net s3d --topk 5 --moco-k 2048 \
--dataset ucf101-2stream-2clip --seq_len 32 --ds 1 --batch_size 32 \
--epochs 100 --schedule 80 --name_prefix Cycle1-FlowMining_ -j 8 \
--pretrain {rgb_infoNCE_checkpoint.pth.tar} {flow_infoNCE_checkpoint.pth.tar}

CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch \
--nproc_per_node=2 main_coclr.py --net s3d --topk 5 --moco-k 2048 --reverse \
--dataset ucf101-2stream-2clip --seq_len 32 --ds 1 --batch_size 32 \
--epochs 100 --schedule 80 --name_prefix Cycle1-RGBMining_ -j 8 \
--pretrain {flow_infoNCE_checkpoint.pth.tar} {rgb_cycle1_checkpoint.pth.tar}
  • InfoNCE pretrain on K400-RGB
CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch \
--nproc_per_node=4 main_infonce.py --net s3d --model infonce --moco-k 16384 \
--dataset k400-2clip --lr 1e-3 --seq_len 32 --ds 1 --batch_size 32 \
--epochs 300 --schedule 250 280 -j 16
  • InfoNCE pretrain on K400-Flow
CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch \
--nproc_per_node=4 teco_fb_main.py --net s3d --model infonce --moco-k 16384 \
--dataset k400-f-2clip --lr 1e-3 --seq_len 32 --ds 1 --batch_size 32 \
--epochs 300 --schedule 250 280 -j 16
  • CoCLR pretrain on K400 for one cycle
CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch \
--nproc_per_node=2 main_coclr.py --net s3d --topk 5 --moco-k 16384 \
--dataset k400-2stream-2clip --seq_len 32 --ds 1 --batch_size 32 \
--epochs 50 --schedule 40 --name_prefix Cycle1-FlowMining_ -j 8 \
--pretrain {rgb_infoNCE_checkpoint.pth.tar} {flow_infoNCE_checkpoint.pth.tar}

CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch \
--nproc_per_node=2 main_coclr.py --net s3d --topk 5 --moco-k 16384 --reverse \
--dataset k400-2stream-2clip --seq_len 32 --ds 1 --batch_size 32 \
--epochs 50 --schedule 40 --name_prefix Cycle1-RGBMining_ -j 8 \
--pretrain {flow_infoNCE_checkpoint.pth.tar} {rgb_cycle1_checkpoint.pth.tar}

Finetune Instruction

cd eval/ e.g. finetune UCF101-rgb:

CUDA_VISIBLE_DEVICES=0,1 python main_classifier.py --net s3d --dataset ucf101 \
--seq_len 32 --ds 1 --batch_size 32 --train_what ft --epochs 500 --schedule 400 450 \
--pretrain {selected_rgb_pretrained_checkpoint.pth.tar}

then run the test with 10-crop (test-time augmentation is helpful, 10-crop gives better result than center-crop):

CUDA_VISIBLE_DEVICES=0,1 python main_classifier.py --net s3d --dataset ucf101 \
--seq_len 32 --ds 1 --batch_size 32 --train_what ft --epochs 500 --schedule 400 450 \
--test {selected_rgb_finetuned_checkpoint.pth.tar} --ten_crop

Nearest-neighbour Retrieval Instruction

cd eval/ e.g. nn-retrieval for UCF101-rgb

CUDA_VISIBLE_DEVICES=0 python main_classifier.py --net s3d --dataset ucf101 \
--seq_len 32 --ds 1 --test {selected_rgb_pretrained_checkpoint.pth.tar} --retrieval

Linear-probe Instruction

cd eval/

from extracted feature

The code support two methods on linear-probe, either feed the data end-to-end and freeze the backbone, or train linear layer on extracted features. Both methods give similar best results in our experiments.

e.g. on extracted features (after run NN-retrieval command above, features will be saved in os.path.dirname(checkpoint))

CUDA_VISIBLE_DEVICES=0 python feature_linear_probe.py --dataset ucf101 \
--test {feature_dirname} --final_bn --lr 1.0 --wd 1e-3

Note that the default setting should give an alright performance, maybe 1-2% lower than our paper's figure. For different datasets, lr and wd need to be tuned from lr: 0.1 to 1.0; wd: 1e-4 to 1e-1.

load data and freeze backbone

alternatively, feed data end-to-end and freeze the backbone.

CUDA_VISIBLE_DEVICES=0,1 python main_classifier.py --net s3d --dataset ucf101 \
--seq_len 32 --ds 1 --batch_size 32 --train_what last --epochs 100 --schedule 60 80 \
--optim sgd --lr 1e-1 --wd 1e-3 --final_bn --pretrain {selected_rgb_pretrained_checkpoint.pth.tar}

Similarly, lr and wd need to be tuned for different datasets for best performance.

Dataset

Result

Finetune entire network for action classification on UCF101: arch

Pretrained Weights

Our models:

Baseline models:

Kinetics400-pretrained models：

Owner
Tengda Han
Tengda Han
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