Awesome

Contextual Instance Decoupling for Robust Multi-Person Pose Estimation

[Paper]

Contextual Instance Decoupling for Robust Multi-Person Pose Estimation
Dongkai Wang, Shiliang Zhang
CVPR 2022 Oral

Installation

1. Clone code

    git clone https://github.com/kennethwdk/CID
    cd ./CID

2. Create a conda environment for this repo

    conda create -n CID python=3.9
    conda activate CID

3. Install PyTorch >= 1.6.0 following official instruction, e.g.,

    conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch

There is no requirement for cudatoolkit version for CID, so just use the newest version.

4. Install other dependency python packages

    pip install -r requirements.txt

5. Prepare dataset

Download COCO , CrowdPose and OCHuman from website and put the zip file under the directory following below structure, (xxx.json) denotes their original name.

./data
|── coco
│   └── annotations
|   |   └──coco_train.json(person_keypoints_train2017.json)
|   |   └──coco_val.json(person_keypoints_val2017.json)
|   |   └──coco_test.json(image_info_test-dev2017.json)
|   └── images
|   |   └──train2017
|   |   |   └──000000000009.jpg
|   |   └──val2017
|   |   |   └──000000000139.jpg
|   |   └──test2017
|   |   |   └──000000000001.jpg
├── crowdpose
│   └── annotations
|   |   └──crowdpose_trainval.json(refer to DEKR, link:https://github.com/HRNet/DEKR)
|   |   └──crowdpose_test.json
|   └── images
|   |   └──100000.jpg
├── ochuman
│   └── annotations
|   |   └──ochuman_val.json(ochuman_coco_format_val_range_0.00_1.00.json)
|   |   └──ochuman_test.json(ochuman_coco_format_test_range_0.00_1.00.json)
|   └── images
|   |   └──000001.jpg

Usage

1. Download trained model

• COCO
• CrowdPose
• OCHuman
• ImageNet pre-trained

2. Evaluate Model

Change the checkpoint path by modifying TEST.MODEL_FILE option in .yaml or command line.

--gpus option specifies the gpu ids for evaluation, multiple ids denotes multiple gpu evaluation.

# evaluate on coco val set with 2 gpus
python tools/valid.py --cfg experiments/coco.yaml --gpus 0,1 TEST.MODEL_FILE model/coco/checkpoint.pth.tar

# evaluate on coco test-dev set with 2 gpus (submit to codalab)
python tools/infer_coco_testdev.py --cfg experiments/coco.yaml --gpus 0,1 TEST.MODEL_FILE model/coco/checkpoint.pth.tar

# evaluate on crowdpose test set with 2 gpus
python tools/valid.py --cfg experiments/crowdpose.yaml --gpus 0,1 TEST.MODEL_FILE model/crowdpose/checkpoint.pth.tar

# evaluate on ochuman test set with 2 gpus (trained on ochuman val set)
python tools/valid.py --cfg experiments/ochuman_val.yaml --gpus 0,1 TEST.MODEL_FILE model/ochuman/checkpoint.pth.tar

# evaluate on ochuman test set with 2 gpus (trained on coco train set)
python tools/valid.py --cfg experiments/ochuman_coco.yaml --gpus 0,1 TEST.MODEL_FILE model/coco/checkpoint.pth.tar

# evaluate on ochuman val set with 2 gpus (trained on coco train set)
python tools/valid.py --cfg experiments/ochuman_coco.yaml --gpus 0,1 TEST.MODEL_FILE model/coco/checkpoint.pth.tar DATASET.TEST val

3. Train Model

You need to download HRNet-W32 imagenet pretrained model (see above) and change the checkpoint path by modifying MODEL.PRETRAINED in .yaml, and run following commands:

# train on coco with 2 gpus
python tools/train.py --cfg experiments/coco.yaml --gpus 0,1

# train on crowdpose with 2 gpus
python tools/train.py --cfg experiments/crowdpose.yaml --gpus 0,1

# train on ochuman with 2 gpus
python tools/train.py --cfg experiments/ochuman_val.yaml --gpus 0,1

The experimental results are obtained by training on two NVIDIA RTX 3090. You can use more gpu cards for model training by specifying gpu ids in --gpus optition, e.g., training model on crowdpose on 8 gpu cards by

# train on coco with 8 gpus
python tools/train.py --cfg experiments/coco.yaml --gpus 0,1,2,3,4,5,6,7

Note that you should modify corresponding batch size for each gpu by TRAIN.IMAGES_PER_GPU.

Main Results

With the code contained in this repo, you should be able to reproduce the following results.

Results on COCO val and test-dev set

Results on CrowdPose test set

Results on OCHuman dataset

Citations

If you find this code useful for your research, please cite our paper:

@InProceedings{Wang_2022_CVPR,
    author    = {Wang, Dongkai and Zhang, Shiliang},
    title     = {Contextual Instance Decoupling for Robust Multi-Person Pose Estimation},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2022},
    pages     = {11060-11068}
}

Contact me

If you have any questions about this code or paper, feel free to contact me at dongkai.wang@pku.edu.cn.

Acknowledgement

The code is mainly encouraged by HigherHRNet and DEKR.