Edge-aware Graph Representation Learning and Reasoning for Face Parsing

The official repository of Edge-aware Graph Representation Learning and Reasoning for Face Parsing (ECCV 2020) and AGRNet: Adaptive Graph Representation Learning and Reasoning for Face Parsing (TIP 2021).

Installation

Our model is based on Pytorch 1.4.0 with Python 3.6.8. Also, we use In-Place Activated BatchNorm. First, you need to clone and compile inplace_abn.

git clone https://github.com/mapillary/inplace_abn.git
cd inplace_abn
python setup.py install
cd scripts
pip install -r requirements.txt

Data

You can download original datasets without alignment:

• Helen : https://www.sifeiliu.net/face-parsing
• LaPa : https://github.com/JDAI-CV/lapa-dataset
• CelebAMask-HQ : https://github.com/switchablenorms/CelebAMask-HQ

and put them in ./dataset folder. If you need imagenet pretrained resent-101, please download from baidu drive or Google drive, and put it into snapshot folder. We do not provide the registration code for the moment, and you need to organize input data as follows:

dataset/
    images/
    labels/
    edges/
    train_list.txt
    test_list.txt
        each line: 'images/100032540_1.jpg labels/100032540_1.png'

Besides, we provide the edge genearation code in the generate_edge.py.

Usage

We support single-gpu and multi-gpu training. Inplace-abn requires pytorch distributed data parallel. And you can switch between the model between EAGRNet and AGRNet in train.py.

Single gpu training

python train.py --data-dir ./dataset/Helen/ --random-mirror --random-scale --gpu 0 --learning-rate 1e-3 --weight-decay 5e-4 --batch-size 7 --input-size 473,473 --snapshot-dir ./snapshots/ --dataset train --num-classes 11 --epochs 200

Distributed(multi-gpu) training

CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 train.py --data-dir ./dataset/Helen/ --random-mirror --random-scale --gpu 0,1,2,3 --learning-rate 1e-3 --weight-decay 5e-4 --batch-size 7 --input-size 473,473 --snapshot-dir ./snapshots/ --dataset train --num-classes 11 --epochs 99

Validation

python evaluate.py --data-dir ./dataset/Helen/ --restore-from ./snapshots/helen/best.pth --gpu 0 --batch-size 7 --input-size 473,473 --dataset test --num-classes 11

Reference

If you consider use our code, please cite our paper:

@inproceedings{te2020edge,
  title={Edge-aware Graph Representation Learning and Reasoning for Face Parsing},
  author={Te, Gusi and Liu, Yinglu and Hu, Wei and Shi, Hailin and Mei, Tao},
  booktitle={European Conference on Computer Vision},
  pages={258--274},
  year={2020},
  organization={Springer}
}

@article{te2021agrnet,
  title={Agrnet: Adaptive graph representation learning and reasoning for face parsing},
  author={Te, Gusi and Hu, Wei and Liu, Yinglu and Shi, Hailin and Mei, Tao},
  journal={IEEE Transactions on Image Processing},
  volume={30},
  pages={8236--8250},
  year={2021},
  publisher={IEEE}
}

Acknowledgement

Thanks @lucia123 and her work A New Dataset and Boundary-Attention Semantic Segmentation for Face Parsing in AAAI, 2020.