Graphonomy: Universal Human Parsing via Graph Transfer Learning

This repository contains the code for the paper:

Graphonomy: Universal Human Parsing via Graph Transfer Learning ,Ke Gong, Yiming Gao, Xiaodan Liang, Xiaohui Shen, Meng Wang, Liang Lin.

Environment and installation

• Pytorch = 0.4.0

• torchvision

• scipy

• tensorboardX

• numpy

• opencv-python

• matplotlib

• networkx

  you can install above package by using pip install -r requirements.txt

Getting Started

Data Preparation

• You need to download the human parsing dataset, prepare the images and store in /data/datasets/dataset_name/. We recommend to symlink the path to the dataets to /data/dataset/ as follows

# symlink the Pascal-Person-Part dataset for example
ln -s /path_to_Pascal_Person_Part/* data/datasets/pascal/

• The file structure should look like:

/Graphonomy
  /data
    /datasets
      /pascal
        /JPEGImages
        /list
        /SegmentationPart
      /CIHP_4w
        /Images
        /lists
        ...  

• The datasets (CIHP & ATR) are available at google drive and baidu drive. And you also need to download the label with flipped. Download cihp_flipped, unzip and store in data/datasets/CIHP_4w/. Download atr_flip, unzip and store in data/datasets/ATR/.

Inference

We provide a simply script to get the visualization result on the CIHP dataset using trained models as follows :

# Example of inference
python exp/inference/inference.py  \
--loadmodel /path_to_inference_model \
--img_path ./img/messi.jpg \
--output_path ./img/ \
--output_name /output_file_name

Training

Transfer learning

1. Download the Pascal pretrained model(available soon).
2. Run the sh train_transfer_cihp.sh.
3. The results and models are saved in exp/transfer/run/.
4. Evaluation and visualization script is eval_cihp.sh. You only need to change the attribute of --loadmodel before you run it.

Universal training

1. Download the pretrained model and store in /data/pretrained_model/.
2. Run the sh train_universal.sh.
3. The results and models are saved in exp/universal/run/.

Testing

If you want to evaluate the performance of a pre-trained model on PASCAL-Person-Part or CIHP val/test set, simply run the script: sh eval_cihp/pascal.sh. Specify the specific model. And we provide the final model that you can download and store it in /data/pretrained_model/.

Models

Pascal-Person-Part trained model

|Model|Google Cloud|Baidu Yun| |--------|--------------|-----------| |Graphonomy(CIHP)| Download| Available soon|

CIHP trained model

|Model|Google Cloud|Baidu Yun| |--------|--------------|-----------| |Graphonomy(PASCAL)| Download| Available soon|

Universal trained model

|Model|Google Cloud|Baidu Yun| |--------|--------------|-----------| |Universal| Download|Available soon|

Todo:

• [ ] release pretrained and trained models
• [ ] update universal eval code&script

Citation

@inproceedings{Gong2019Graphonomy,
author = {Ke Gong and Yiming Gao and Xiaodan Liang and Xiaohui Shen and Meng Wang and Liang Lin},
title = {Graphonomy: Universal Human Parsing via Graph Transfer Learning},
booktitle = {CVPR},
year = {2019},
}

Contact

if you have any questions about this repo, please feel free to contact gaoym9@mail2.sysu.edu.cn.

Related work

• Self-supervised Structure-sensitive Learning SSL
• Joint Body Parsing & Pose Estimation Network JPPNet
• Instance-level Human Parsing via Part Grouping Network PGN
• Graphonomy: Universal Image Parsing via Graph Reasoning and Transfer paper code