Multi-Channel Network: Constructing Efficient GCN Baselines for Skeleton-based Action Recognition

Dependencies

• Python == 3.8.5
• torch== 1.7.1
• apex==0.1

Data Preparation

Disk usage warning: after preprocessing, the total sizes of datasets are around 77G, 157GB, 63GB for NTU RGB+D 60, NTU RGB+D 120, and Kinetics 400, respectively. The raw/intermediate sizes may be larger.

Download Datasets

There are 3 datasets to download:

• NTU RGB+D 60 Skeleton
• NTU RGB+D 120 Skeleton

NTU RGB+D 60 and 120

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

2. Download the skeleton-only datasets:

   • nturgbd_skeletons_s001_to_s017.zip (NTU RGB+D 60)
   • nturgbd_skeletons_s018_to_s032.zip (NTU RGB+D 120, on top of NTU RGB+D 60)
   • Total size should be 5.8GB + 4.5GB.

3. Download missing skeletons lookup files from the authors' GitHub repo:

   • NTU RGB+D 60 Missing Skeletons: wget https://raw.githubusercontent.com/shahroudy/NTURGB-D/master/Matlab/NTU_RGBD_samples_with_missing_skeletons.txt

   • NTU RGB+D 120 Missing Skeletons: wget https://raw.githubusercontent.com/shahroudy/NTURGB-D/master/Matlab/NTU_RGBD120_samples_with_missing_skeletons.txt

   • Remember to remove the first few lines of text in these files!

     NW-UCLA

4. Download dataset from here

5. Move all_sqe to ./data/NW-UCLA

Data Preprocessing

Directory Structure

Put downloaded data into the following directory structure:

- data/
  - nturgbd_raw/
    - nturgb+d_skeletons/     # from `nturgbd_skeletons_s001_to_s017.zip`
      ...
    - nturgb+d_skeletons120/  # from `nturgbd_skeletons_s001_to_s017.zip``nturgbd_skeletons_s018_to_s032.zip`
      ...
    - NTU_RGBD_samples_with_missing_skeletons.txt
    - NTU_RGBD120_samples_with_missing_skeletons.txt

Generating Data

• Run bash gene_data.sh

Pretrained Models

• Download pretrained models for producing the final results on NTU RGB+D 60, NTU RGB+D 120, NW-ucla:

  Google Drive

• Put the folder of pretrained models at repo root:

- multi-stream/
  - pretrained-models/
  - main.py
  - ...

• Run bash eval_pretrained.sh

Training & Testing

• The general training template command:

  python3 main.py
  --config <config file>
  --work-dir <place to keep things (weights, checkpoints, logs)>

• The general testing template command:

  python3 main.py
  --config <config file>
  --work-dir <place to keep things>
  --weights <path to model weights>

• Template for stream fusion:

  python3 ensemble.py
  --dataset <val_label path> \
  --one   <work_dir of your test command for frame-level joint model> \
  --two   <work_dir of your test command for frame-level bone model> \
  --three  <work_dir of your test command for sequence-level joint model> \
  --four  <work_dir of your test command for sequence-level bone model> \

• Use the corresponding config files from ./config to train/test different datasets

Acknowledgements

This repo is based on

• MS-G3D
• 2s-AGCN
• ST-GCN

Thanks to the original authors for their work!