Shifting Perspective to See Difference: a novel Multi-View method 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
Kinetics 400 Skeleton

NTU RGB+D 60 and 120

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

Kinetics Skeleton 400

Download dataset from ST-GCN repo: https://github.com/yysijie/st-gcn/blob/master/OLD_README.md#kinetics-skeleton
This might be useful if you want to wget the dataset from Google Drive

Data Preprocessing

Directory Structure

Put downloaded data into the following directory structure:

- data/
  - kinetics_raw/
    - kinetics_train/
      ...
    - kinetics_val/
      ...
    - kinetics_train_label.json
    - keintics_val_label.json
  - 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, Kinetics Skeleton 400:

Google Drive
Put the folder of pretrained models at repo root:

- sap/
  - 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 six-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 frame-level angle model> \
  --four  <work_dir of your test command for sequence-level joint model> \
  --five  <work_dir of your test command for sequence-level bone model> \
  --six   <work_dir of your test command for sequence-level angle model>

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

Acknowledgements

This repo is based on

Thanks to the original authors for their work!

ideal-idea/SAP