README.md

STGCN++

PYSKL: Towards Good Practices for Skeleton Action Recognition

Abstract

We present PYSKL: an open-source toolbox for skeleton-based action recognition based on PyTorch. The toolbox supports a wide variety of skeleton action recognition algorithms, including approaches based on GCN and CNN. In contrast to existing open-source skeleton action recognition projects that include only one or two algorithms, PYSKL implements six different algorithms under a unified framework with both the latest and original good practices to ease the comparison of efficacy and efficiency. We also provide an original GCN-based skeleton action recognition model named ST-GCN++, which achieves competitive recognition performance without any complicated attention schemes, serving as a strong baseline. Meanwhile, PYSKL supports the training and testing of nine skeleton-based action recognition benchmarks and achieves state-of-the-art recognition performance on eight of them. To facilitate future research on skeleton action recognition, we also provide a large number of trained models and detailed benchmark results to give some insights. PYSKL is released at this https URL and is actively maintained. We will update this report when we add new features or benchmarks. The current version corresponds to PYSKL v0.2.

Results and Models

NTU60_XSub_2D

frame sampling strategy	modality	gpus	backbone	top1 acc	testing protocol	FLOPs	params	config	ckpt	log
uniform 100	joint	8	STGCN++	89.29	10 clips	1.95G	1.39M	config	ckpt	log
uniform 100	bone	8	STGCN++	92.30	10 clips	1.95G	1.39M	config	ckpt	log
uniform 100	joint-motion	8	STGCN++	87.30	10 clips	1.95G	1.39M	config	ckpt	log
uniform 100	bone-motion	8	STGCN++	88.76	10 clips	1.95G	1.39M	config	ckpt	log
	two-stream			92.61
	four-stream			92.77

NTU60_XSub_3D

frame sampling strategy	modality	gpus	backbone	top1 acc	testing protocol	FLOPs	params	config	ckpt	log
uniform 100	joint	8	STGCN++	89.14	10 clips	2.96G	1.4M	config	ckpt	log
uniform 100	bone	8	STGCN++	90.21	10 clips	2.96G	1.4M	config	ckpt	log
uniform 100	joint-motion	8	STGCN++	86.67	10 clips	2.96G	1.4M	config	ckpt	log
uniform 100	bone-motion	8	STGCN++	87.45	10 clips	2.96G	1.4M	config	ckpt	log
	two-stream			91.39
	four-stream			91.87

1. The gpus indicates the number of gpus we used to get the checkpoint. If you want to use a different number of gpus or videos per gpu, the best way is to set --auto-scale-lr when calling tools/train.py, this parameter will auto-scale the learning rate according to the actual batch size, and the original batch size.
2. For two-stream fusion, we use joint : bone = 1 : 1. For four-stream fusion, we use joint : joint-motion : bone : bone-motion = 2 : 1 : 2 : 1. For more details about multi-stream fusion, please refer to this tutorial.

Train

You can use the following command to train a model.

python tools/train.py ${CONFIG_FILE} [optional arguments]

Example: train STGCN++ model on NTU60-2D dataset in a deterministic option with periodic validation.

python tools/train.py configs/skeleton/stgcnpp/stgcnpp_8xb16-joint-u100-80e_ntu60-xsub-keypoint-2d.py \
    --seed 0 --deterministic

For more details, you can refer to the Training part in the Training and Test Tutorial.

Test

You can use the following command to test a model.

python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [optional arguments]

Example: test STGCN++ model on NTU60-2D dataset and dump the result to a pickle file.

python tools/test.py configs/skeleton/stgcnpp/stgcnpp_8xb16-joint-u100-80e_ntu60-xsub-keypoint-2d.py \
    checkpoints/SOME_CHECKPOINT.pth --dump result.pkl

For more details, you can refer to the Test part in the Training and Test Tutorial.

Citation

@misc{duan2022PYSKL,
  url = {https://arxiv.org/abs/2205.09443},
  author = {Duan, Haodong and Wang, Jiaqi and Chen, Kai and Lin, Dahua},
  title = {PYSKL: Towards Good Practices for Skeleton Action Recognition},
  publisher = {arXiv},
  year = {2022}
}