Awesome
DecoupleGCN-DropGraph
The implementation for "Decoupling GCN with DropGraph Module for Skeleton-Based Action Recognition" (ECCV2020). The proposed method boosts the performance of spatial-temporal graph convolutional network with NO extra FLOPs, NO extra latency, and NO extra GPU memory cost.
Prerequisite
- PyTorch 0.4.1
- Cuda 9.0
Data Preparation
-
Download the raw data of NTU-RGBD and NTU-RGBD120. Put NTU-RGBD data under the directory
./data/nturgbd_raw
. Put NTU-RGBD120 data under the directory./data/nturgbd120_raw
. -
For NTU-RGBD, preprocess data with
python data_gen/ntu_gendata.py
. For NTU-RGBD120, preprocess data withpython data_gen/ntu120_gendata.py
. -
Generate the bone data with
python data_gen/gen_bone_data.py
. -
Generate the motion data with
python data_gen/gen_motion_data.py
.
Training & Testing
-
NTU X-view
python main.py --config ./config/nturgbd-cross-view/train_joint.yaml
python main.py --config ./config/nturgbd-cross-view/train_bone.yaml
python main.py --config ./config/nturgbd-cross-view/train_joint_motion.yaml
python main.py --config ./config/nturgbd-cross-view/train_bone_motion.yaml
-
NTU X-sub
python main.py --config ./config/nturgbd-cross-subject/train_joint.yaml
python main.py --config ./config/nturgbd-cross-subject/train_bone.yaml
python main.py --config ./config/nturgbd-cross-subject/train_joint_motion.yaml
python main.py --config ./config/nturgbd-cross-subject/train_bone_motion.yaml
-
For NTU120, change the dataset path in config files, and change
num_class
in config files from 60 to 120.
Multi-stream ensemble
To ensemble the results of 4 streams. Change models name in ensemble.py
depending on your experiment setting. Then run python ensemble.py
.
Trained models
We release several trained models:
Model | Dataset | Setting | Top1(%) |
---|---|---|---|
./save_models/ntu_joint_xview.pt | NTU-RGBD | X-view | 95.2 |
./save_models/ntu_joint_xsub.pt | NTU-RGBD | X-sub | 88.2 |
./save_models/ntu120_joint_xsetup.pt | NTU-RGBD120 | X-setup | 84.3 |
./save_models/ntu120_joint_xsub.pt | NTU-RGBD120 | X-sub | 82.4 |
Citation
If you find this model useful for your resesarch, please use the following BibTeX entry.
@inproceedings{cheng2020eccv,
title = {Decoupling GCN with DropGraph Module for Skeleton-Based Action Recognition},
author = {Ke Cheng and Yifan Zhang and Congqi Cao and Lei Shi and Jian Cheng and Hanqing Lu},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2020},
}