PINet

Pytorch implementation of paper "Robust Pose Estimation in Crowded Scenes with Direct Pose-Level Inference" (NeurIPS 2021).

Installation

1. Clone code

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

2. Install dependency python packages

    conda create --name PINet --file requirements.txt

3. Prepare dataset

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

./data
├── 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
|   |   └──xxxx.jpg
├── crowdpose
│   └── annotations
|   |   └──crowdpose_trainval.json(refer to DEKR, link:https://github.com/HRNet/DEKR)
|   |   └──crowdpose_test.json
|   └── images
|   |   └──xxxx.jpg
|── 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
|   |   |   └──xxxx.jpg
|   |   └──val2017
|   |   |   └──xxxx.jpg
|   |   └──test2017
|   |   |   └──xxxx.jpg

Usage

1. Download trained model

• OCHuman
• CrowdPose
• COCO
• 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 ochuman with 2 gpus
python tools/valid.py --cfg experiments/ochuman.yaml --gpus 0,1 TEST.MODEL_FILE model/ochuman/checkpoint.pth.tar

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

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

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

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

# train on coco with 2 gpus
python tools/train.py --cfg experiments/coco.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 crowdpose with 8 gpus
python tools/train.py --cfg experiments/crowdpose.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.

Results

Datasets	AP	AP50	AP75
OCHuman	59.8	74.9	65.9
CrowdPose	68.9	88.7	74.7
COCO (val set)	67.3	86.8	74.0

Citations

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

@inproceedings{wang2021robust,
  title={Robust Pose Estimation in Crowded Scenes with Direct Pose-Level Inference},
  author={Wang, Dongkai and Zhang, Shiliang and Hua, Gang},
  booktitle={Thirty-Fifth Conference on Neural Information Processing Systems},
  year={2021}
}

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.