/CT-Net

[ICLR2021] CT-Net: Channel Tensorization Network for Video Classification

Primary LanguagePythonMIT LicenseMIT

[ICLR2021] CT-Net: Channel Tensorization Network for Video Classification

@inproceedings{
li2021ctnet,
title={{\{}CT{\}}-Net: Channel Tensorization Network for Video Classification},
author={Kunchang Li and Xianhang Li and Yali Wang and Jun Wang and Yu Qiao},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=UoaQUQREMOs}
}

PWC PWC PWC

Overview

[2021/6/3] We release the PyTorch code of CT-Net. More details and models will be available. img

Model Zoo

All models can be trained on a single machine (e.g., 8 1080Ti). Some tricks will help you save GPU memory, suck as mixed precision or torch.utils.checkpoint.

Some models are lost after hacking by mining malware. If there is any problem about training model, please create an issue or send me an email.

Now we release the model for visualization (Something-Something V1), please download it from here and put it in ./model. (passward: t3to)

Install

pip install -r requirements.txt

Dataset

In our paper, we conduct experiments on Kinetics-400, Something-Something V1&V2, UCF101, and HMDB51. Please refer to TSM repo for the detailed guide of data pre-processing.

Training and Testing

Please refer to scripts/train.sh and scripts/test.sh, more details can be found in the appendix of our paper.

Setting environment

source ./init.sh

Training

We use dense sampling and uniform sampling for Kinetics and Something-Something respecitively.

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
python3 main.py something RGB \
     --root-log ./log \
     --root-model ./model \
     --arch resnet50 --model CT_Net --num-segments 8 \
     --gd 20 --lr 0.02 --unfrozen-epoch 0 --lr-type cos \
     --warmup 10 --tune-epoch 10 --tune-lr 0.02 --epochs 45 \
     --batch-size 8 -j 24 --dropout 0.3 --consensus-type=avg \
     --npb --num-total 7 --full-res --gpus 0 1 2 3 4 5 6 7 --suffix 2021

Testing

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
python3 test_acc.py something RGB \
     --arch resnet50 --model CT_Net --num-segments 8 \
     --batch-size 64 -j 8 --consensus-type=avg \
     --resume ./model/ct_net_8f_r50.pth.tar \
     --npb --num-total 7 --evaluate --test-crops 1 --full-res --gpus 0 1 2 3 4 5 6 7

Demo and visiualization

See demo/show_cam.ipynb，

  1. source ./init.sh
  2. cd demo
  3. jupyter notebook

img