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).
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.txtData
You can download the Helen dataset on https://www.sifeiliu.net/face-parsing and imagenet pretrained resent-101 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.
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:
@article{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},
journal={arXiv preprint arXiv:2007.11240},
year={2020}
}
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