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

Last update: Dec 16, 2022

Related tags

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

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

Request dataset here: http://rose1.ntu.edu.sg/Datasets/actionRecognition.asp
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

Download dataset from here
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]

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

Related tags

Overview

CTR-GCN

Architecture of CTR-GC

Prerequisites

Data Preparation

Download datasets.

There are 3 datasets to download:

NTU RGB+D 60 and 120

NW-UCLA

Data Processing

Directory Structure

Generating Data

Training & Testing

Training

Testing

Pretrained Models

Acknowledgements

Citation

Contact

Owner

Yuxin Chen

Deep deconfounded recommender (Deep-Deconf) for paper "Deep causal reasoning for recommendations"

Non-Official Pytorch implementation of "Face Identity Disentanglement via Latent Space Mapping" https://arxiv.org/abs/2005.07728 Using StyleGAN2 instead of StyleGAN

Another pytorch implementation of FCN (Fully Convolutional Networks)

Canonical Appearance Transformations

Awesome-google-colab - Google Colaboratory Notebooks and Repositories

PASTRIE: A Corpus of Prepositions Annotated with Supersense Tags in Reddit International English

The source code of the ICCV2021 paper "PIRenderer: Controllable Portrait Image Generation via Semantic Neural Rendering"

Totally Versatile Miscellanea for Pytorch

Bayesian Deep Learning and Deep Reinforcement Learning for Object Shape Error Response and Correction of Manufacturing Systems

Running Google MoveNet Multipose Tracking models on OpenVINO.

DatasetGAN: Efficient Labeled Data Factory with Minimal Human Effort

A Dataset for Direct Quotation Extraction and Attribution in News Articles.

Multi-robot collaborative exploration and mapping through Voronoi partition and DRL in unknown environment

A Sign Language detection project using Mediapipe landmark detection and Tensorflow LSTM's

Graph parsing approach to structured sentiment analysis.

torchsummaryDynamic: support real FLOPs calculation of dynamic network or user-custom PyTorch ops

This repository is an implementation of paper : Improving the Training of Graph Neural Networks with Consistency Regularization

Python implementation of O-OFDMNet, a deep learning-based optical OFDM system,

Recursive Bayesian Networks

TensorFlow code for the neural network presented in the paper: "Structural Language Models of Code" (ICML'2020)