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# PyTorch-JAANet This repository is the PyTorch implementation of [JAA-Net](http://openaccess.thecvf.com/content_ECCV_2018/papers/Zhiwen_Shao_Deep_Adaptive_Attention_ECCV_2018_paper.pdf), as well as [its extended journal version](https://arxiv.org/pdf/2003.08834.pdf). "*v1.py" is for the ECCV version, and "*v2.py" is for the IJCV version. The trained models can be downloaded [here](https://sjtueducn-my.sharepoint.com/:f:/g/personal/shaozhiwen_sjtu_edu_cn/Eu3SDFcZYG9Ah5RdRYqfYxoBWDyWici_FdWJP8TnYFqaZw?e=ogNmZv). The original Caffe implementation can be found [here](https://github.com/ZhiwenShao/JAANet) # Getting Started ## Installation - This code was tested with PyTorch 1.1.0 and Python 3.5 - Clone this repo: ``` git clone https://github.com/ZhiwenShao/PyTorch-JAANet cd PyTorch-JAANet ``` ## Datasets [BP4D](http://www.cs.binghamton.edu/~lijun/Research/3DFE/3DFE_Analysis.html) and [DISFA](http://www.engr.du.edu/mmahoor/DISFA.htm) Put these datasets into the folder "dataset" following the paths shown in the list files of the folder "data/list". You can refer to the example images for BP4D and DISFA ## Preprocessing - Put the landmark annotation files into the folder "dataset". Two example files "BP4D_combine_1_2_land.txt" and "DISFA_combine_1_2_66land.txt" are also provided - For DISFA dataset, we need to select the annotations of 49 landmarks from original 66 landmarks: ``` cd dataset python read_disfa_49land.py ``` - Conduct similarity transformation for face images: ``` cd dataset python face_transform.py ``` - Compute the inter-ocular distance of each face image: ``` cd dataset python write_biocular.py ``` - Compute the weight of the loss of each AU for the training set: - The AU annoatation files should be in the folder "data/list" ``` cd dataset python write_AU_weight.py ``` ## Training - Train on BP4D with the first two folds for training and the third fold for testing: ``` python train_JAAv1.py --run_name='JAAv1' --gpu_id=0 --train_batch_size=16 --eval_batch_size=28 --train_path_prefix='data/list/BP4D_combine_1_2' --test_path_prefix='data/list/BP4D_part3' --au_num=12 ``` - Train on DISFA with the first two folds for training and the third fold for testing, using the the well-trained BP4D model for initialization: ``` python train_JAAv1_disfa.py --run_name='JAAv1_DISFA' --gpu_id=0 --train_batch_size=16 --eval_batch_size=32 --train_path_prefix='data/list/DISFA_combine_1_2' --test_path_prefix='data/list/DISFA_part3' --au_num=8 --pretrain_path='JAAv1_combine_1_3' --pretrain_epoch=5 ``` ## Testing - Test the models saved in different epochs: ``` python test_JAAv1.py --run_name='JAAv1' --gpu_id=0 --start_epoch=1 --n_epochs=12 --eval_batch_size=28 --test_path_prefix='data/list/BP4D_part3' --au_num=12 ``` - Visualize attention maps ``` python test_JAAv1.py --run_name='JAAv1' --gpu_id=0 --pred_AU=False --vis_attention=True --start_epoch=5 --n_epochs=5 --test_path_prefix='data/list/BP4D_part3' --au_num=12 ``` ## Supplement - The PyTorch implementation for the ECCV version conducts two minor revisions to make the proposed method more general: - The redundant cropping of attention maps is removed - The assembling of local feature maps uses element-wise average instead of element-wise sum - The differences in the extended journal version are detailed [here](https://arxiv.org/pdf/2003.08834.pdf) ## Citation - If you use this code for your research, please cite our papers ``` @inproceedings{shao2018deep, title={Deep Adaptive Attention for Joint Facial Action Unit Detection and Face Alignment}, author={Shao, Zhiwen and Liu, Zhilei and Cai, Jianfei and Ma, Lizhuang}, booktitle={European Conference on Computer Vision}, year={2018}, pages={725--740}, organization={Springer} } @article{shao2020jaa, title={J{\^A}A-Net: Joint Facial Action Unit Detection and Face Alignment via Adaptive Attention}, author={Shao, Zhiwen and Liu, Zhilei and Cai, Jianfei and Ma, Lizhuang}, journal={International Journal of Computer Vision}, year={2020}, publisher={Springer} } ```