[ICCV2021] Official code for "Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognition"

Related tags

Deep Learningdeep-learningpytorch
Overview

CTR-GCN

This repo is the official implementation for Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognition. The paper is accepted to ICCV2021.

Note: We also provide a simple and strong baseline model, which achieves 83.7% on NTU120 CSub with joint modality only, to facilitate the development of skeleton-based action recognition.

Architecture of CTR-GC

image

Prerequisites

  • Python >= 3.6

  • PyTorch >= 1.1.0

  • PyYAML, tqdm, tensorboardX

  • We provide the dependency file of our experimental environment, you can install all dependencies by creating a new anaconda virtual environment and running pip install -r requirements.txt

  • Run pip install -e torchlight

Data Preparation

Download datasets.

There are 3 datasets to download:

  • NTU RGB+D 60 Skeleton
  • NTU RGB+D 120 Skeleton
  • NW-UCLA

NTU RGB+D 60 and 120

  1. Request dataset here: http://rose1.ntu.edu.sg/Datasets/actionRecognition.asp
  2. Download the skeleton-only datasets:
    1. nturgbd_skeletons_s001_to_s017.zip (NTU RGB+D 60)
    2. nturgbd_skeletons_s018_to_s032.zip (NTU RGB+D 120)
    3. Extract above files to ./data/nturgbd_raw

NW-UCLA

  1. Download dataset from here
  2. Move all_sqe to ./data/NW-UCLA

Data Processing

Directory Structure

Put downloaded data into the following directory structure:

- data/
  - NW-UCLA/
    - all_sqe
      ... # raw data of NW-UCLA
  - ntu/
  - ntu120/
  - nturgbd_raw/
    - nturgb+d_skeletons/     # from `nturgbd_skeletons_s001_to_s017.zip`
      ...
    - nturgb+d_skeletons120/  # from `nturgbd_skeletons_s018_to_s032.zip`
      ...

Generating Data

  • Generate NTU RGB+D 60 or NTU RGB+D 120 dataset:
 cd ./data/ntu # or cd ./data/ntu120
 # Get skeleton of each performer
 python get_raw_skes_data.py
 # Remove the bad skeleton 
 python get_raw_denoised_data.py
 # Transform the skeleton to the center of the first frame
 python seq_transformation.py

Training & Testing

Training

  • Change the config file depending on what you want.
# Example: training CTRGCN on NTU RGB+D 120 cross subject with GPU 0
python main.py --config config/nturgbd120-cross-subject/default.yaml --work-dir work_dir/ntu120/csub/ctrgcn --device 0
# Example: training provided baseline on NTU RGB+D 120 cross subject
python main.py --config config/nturgbd120-cross-subject/default.yaml --model model.baseline.Model--work-dir work_dir/ntu120/csub/baseline --device 0
  • To train model on NTU RGB+D 60/120 with bone or motion modalities, setting bone or vel arguments in the config file default.yaml or in the command line.
# Example: training CTRGCN on NTU RGB+D 120 cross subject under bone modality
python main.py --config config/nturgbd120-cross-subject/default.yaml --train_feeder_args bone=True --test_feeder_args bone=True --work-dir work_dir/ntu120/csub/ctrgcn_bone --device 0
  • To train model on NW-UCLA with bone or motion modalities, you need to modify data_path in train_feeder_args and test_feeder_args to "bone" or "motion" or "bone motion", and run
python main.py --config config/ucla/default.yaml --work-dir work_dir/ucla/ctrgcn_xxx --device 0
  • To train your own model, put model file your_model.py under ./model and run:
# Example: training your own model on NTU RGB+D 120 cross subject
python main.py --config config/nturgbd120-cross-subject/default.yaml --model model.your_model.Model --work-dir work_dir/ntu120/csub/your_model --device 0

Testing

  • To test the trained models saved in <work_dir>, run the following command:
python main.py --config <work_dir>/config.yaml --work-dir <work_dir> --phase test --save-score True --weights <work_dir>/xxx.pt --device 0
  • To ensemble the results of different modalities, run
# Example: ensemble four modalities of CTRGCN on NTU RGB+D 120 cross subject
python ensemble.py --datasets ntu120/xsub --joint-dir work_dir/ntu120/csub/ctrgcn --bone-dir work_dir/ntu120/csub/ctrgcn_bone --joint-motion-dir work_dir/ntu120/csub/ctrgcn_motion --bone-motion-dir work_dir/ntu120/csub/ctrgcn_bone_motion

Pretrained Models

  • Download pretrained models for producing the final results on NTU RGB+D 60&120 cross subject [Google Drive].
  • Put files to <work_dir> and run Testing command to produce the final result.

Acknowledgements

This repo is based on 2s-AGCN. The data processing is borrowed from SGN and HCN.

Thanks to the original authors for their work!

Citation

Please cite this work if you find it useful:.

@article{chen2021channel,
  title={Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognition},
  author={Chen, Yuxin and Zhang, Ziqi and Yuan, Chunfeng and Li, Bing and Deng, Ying and Hu, Weiming},
  journal={arXiv preprint arXiv:2107.12213},
  year={2021}
}

Contact

For any questions, feel free to contact: [email protected]

Owner
Yuxin Chen
PhD candidate at the Institute of Automation, Chinese Academy of Sciences.
Yuxin Chen
GitHub Repository
Adds timm pretrained backbone to pytorch's FasterRcnn model

Operating Systems Lab (ETCS-352) Experiments for Operating Systems Lab (ETCS-352) performed by me in 2021 at uni. All codes are written by me except t

Mriganka Nath 12 Dec 03, 2022
OCR Post Correction for Endangered Language Texts

📌 Coming soon: an update to the software including features from our paper on semi-supervised OCR post-correction, to be published in the Transaction

Shruti Rijhwani 96 Dec 31, 2022
These are the materials for the paper "Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations"

Few-shot-NLEs These are the materials for the paper "Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations". You can find the smal

Yordan Yordanov 0 Oct 21, 2022
Code for one-stage adaptive set-based HOI detector AS-Net.

AS-Net Code for one-stage adaptive set-based HOI detector AS-Net. Mingfei Chen*, Yue Liao*, Si Liu, Zhiyuan Chen, Fei Wang, Chen Qian. "Reformulating

Mingfei Chen 45 Dec 09, 2022
Weighted QMIX: Expanding Monotonic Value Function Factorisation

This repo contains the cleaned-up code that was used in "Weighted QMIX: Expanding Monotonic Value Function Factorisation"

whirl 82 Dec 29, 2022
Code for Referring Image Segmentation via Cross-Modal Progressive Comprehension, CVPR2020.

CMPC-Refseg Code of our CVPR 2020 paper Referring Image Segmentation via Cross-Modal Progressive Comprehension. Shaofei Huang*, Tianrui Hui*, Si Liu,

spyflying 55 Dec 01, 2022
Pytorch implementation of the paper SPICE: Semantic Pseudo-labeling for Image Clustering

SPICE: Semantic Pseudo-labeling for Image Clustering By Chuang Niu and Ge Wang This is a Pytorch implementation of the paper. (In updating) SOTA on 5

Chuang Niu 154 Dec 15, 2022
Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learning

Graph-InfoClust-GIC [PAKDD 2021] PAKDD'21 version Graph InfoClust: Maximizing Coarse-Grain Mutual Information in Graphs Preprint version Graph InfoClu

Costas Mavromatis 21 Dec 03, 2022
Semi-Autoregressive Transformer for Image Captioning

Semi-Autoregressive Transformer for Image Captioning Requirements Python 3.6 Pytorch 1.6 Prepare data Please use git clone --recurse-submodules to clo

YE Zhou 23 Dec 09, 2022
SuRE Evaluation: A Supplementary Material

SuRE Evaluation: A Supplementary Material This repository contains supplementary material regarding the evaluations presented in the paper Visual Expl

NYU Visualization Lab 0 Dec 14, 2021
PyTorch wrappers for using your model in audacity!

audacitorch This package contains utilities for prepping PyTorch audio models for use in Audacity. More specifically, it provides abstract classes for

Hugo Flores García 130 Dec 14, 2022
Consumer Fairness in Recommender Systems: Contextualizing Definitions and Mitigations

Consumer Fairness in Recommender Systems: Contextualizing Definitions and Mitigations This is the repository for the paper Consumer Fairness in Recomm

7 Nov 30, 2022
Code for "Localization with Sampling-Argmax", NeurIPS 2021

Localization with Sampling-Argmax [Paper] [arXiv] [Project Page] Localization with Sampling-Argmax Jiefeng Li, Tong Chen, Ruiqi Shi, Yujing Lou, Yong-

JeffLi 71 Dec 17, 2022
SiT: Self-supervised vIsion Transformer

This repository contains the official PyTorch self-supervised pretraining, finetuning, and evaluation codes for SiT (Self-supervised image Transformer).

Sara Ahmed 275 Dec 28, 2022
[NeurIPS 2021] Shape from Blur: Recovering Textured 3D Shape and Motion of Fast Moving Objects

[NeurIPS 2021] Shape from Blur: Recovering Textured 3D Shape and Motion of Fast Moving Objects YouTube | arXiv Prerequisites Kaolin is available here:

Denys Rozumnyi 107 Dec 26, 2022
Image process framework based on plugin like imagej, it is esay to glue with scipy.ndimage, scikit-image, opencv, simpleitk, mayavi...and any libraries based on numpy

Introduction ImagePy is an open source image processing framework written in Python. Its UI interface, image data structure and table data structure a

ImagePy 1.2k Dec 29, 2022
Bot developed in Python that automates races in pegaxy.

español | português About it: This is a fork from pega-racing-bot. This bot, developed in Python, is to automate races in pegaxy. The game developers

4 Apr 08, 2022
Code for the paper Progressive Pose Attention for Person Image Generation in CVPR19 (Oral).

Pose-Transfer Code for the paper Progressive Pose Attention for Person Image Generation in CVPR19(Oral). The paper is available here. Video generation

Tengteng Huang 679 Jan 04, 2023
Face Detection & Age Gender & Expression & Recognition

Face Detection & Age Gender & Expression & Recognition

Sajjad Ayobi 188 Dec 28, 2022
Official PyTorch implementation of "Improving Face Recognition with Large AgeGaps by Learning to Distinguish Children" (BMVC 2021)

Inter-Prototype (BMVC 2021): Official Project Webpage This repository provides the official PyTorch implementation of the following paper: Improving F

Jungsoo Lee 16 Jun 30, 2022