Awesome

DGNN-PyTorch

An unofficial PyTorch implementation of the paper "Skeleton-Based Action Recognition with Directed Graph Neural Networks" in CVPR 2019.

NOTE: Experiment results are not being updated due to hardware limits.

• Paper: PDF
• Code is based on 2s-AGCN: GitHub

Dependencies

• Python >= 3.5
• scipy >= 1.3.0
• numpy >= 1.16.4
• PyTorch >= 1.1.0
• tensorboardX >= 1.8 (For logging)

Directory Structure

Most of the interesting stuff can be found in:

• model/dgnn.py: model definition of DGNN
• data_gen/: how raw datasets are processed into numpy tensors
• graphs/directed_ntu_rgb_d.py: graph definition for DGNN
• feeders/feeder.py: how datasets are read in
• main.py: general training/eval processes; graph freezing by disabling gradients; etc.

Downloading & Generating Data

NTU RGB+D

1. The NTU RGB+D dataset can be downloaded from here. We'll only need the Skeleton data (~ 5.8G).

2. After downloading, unzip it and put the folder nturgb+d_skeletons to ./data/nturgbd_raw/.

3. Generate the joint dataset first:

cd data_gen
python3 ntu_gen_joint_data.py

Specify the data location if the raw skeletons data are placed somewhere else. The default looks at ./data/nturgbd_raw/.

4. Then, in data_gen/, generate the bone dataset:

python3 ntu_gen_bone_data.py

5. Finally, generate the motion data from joints/bones:

python3 ntu_gen_motion_data.py

The generation scripts look for generated data in previous step. By default they look at ./data; change dir configs if needed.

Kinetics

(Currently, generating bone/motion data from Kinetics skeletons is not yet supported. Please feel free to add scripts based on kinetics_gendata.py)

1. Download the Kinetics dataset from ST-GCN repo (https://github.com/yysijie/st-gcn)
2. Generate joint data:

cd data_gen
python3 kinetics_gendata.py

3. Generate bone data: TODO, feel free to fork/submit PR :D
4. Generate motion data: TODO, feel free to fork/submit PR :D

Training

1st Stream: Spatial

To start training the network with the spatial stream, use the following command:

python3 main.py --config ./config/<dataset>/train_spatial.yaml

Here, <dataset> should be one of nturgbd-cross-subject, nturgbd-cross-view, or kinetics-skeleton depending on the dataset/task on which to train the model.

Note: At the moment, only nturgbd-cross-subject is supported. More config files will (hopefully) be added, or you could write your own config file using the existing ones for nturgbd-cross-subject.

2nd Stream: Motion

Similarly, to train on the motion stream data, do:

python3 main.py --config ./config/nturgbd-cross-subject/train_motion.yaml

and change the config file path for other datasets if needed.

Testing

Test individual streams

To test some model weights (by default saved in ./runs/), do:

python3 main.py --config ./config/<dataset>/test_spatial.yaml

Similarly, change the paths in config file, or change the config files (<dataset>) for different datasets as needed.

Ensemble results

Combine the generated scores with:

python ensemble.py --datasets <dataset>

where <dataset> is one of kinetics, ntu/xsub, ntu/xview

TODO

• Kinetics
  • Handling datasets
  • Config files