TVConv: Efficient Translation Variant Convolution for Layout-aware Visual Processing [CVPR 2022]

This is the official Pytorch/PytorchLightning implementation of our paper:

TVConv: Efficient Translation Variant Convolution for Layout-aware Visual Processing
Jierun Chen, Tianlang He, Weipeng Zhuo, Li Ma, Sangtae Ha, S.-H. Gary Chan
In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022

Requirements

• Create a conda virtual environment and activate it:

conda create -n TVConv python=3.7.1 -y
conda activate TVConv

• Install CUDA==9.2 following the official installation instructions
• Install pytorch==1.7.1:

conda install pytorch==1.7.1 torchvision==0.8.2 cudatoolkit=9.2 -c pytorch

• Further install the following packages for the face recognition task:

pip install opencv-python pytorch-lightning==1.4.6 pytorch-lightning-bolts==0.3.0 wandb==0.12.1 torchmetrics==0.5.1

• Or further install the following packages for the optic disc/cup segmentation task:

conda install -c anaconda scikit-image
pip install tensorboardX==2.0 pyyaml MedPy opencv-python pytz==2020.1

Dataset preparation

For face recognition

Download datasets from here. Unzip and put the datasets within the directory ./data. The datasets include:

• Training dataset
  CASIA-Webface-frontal (10K ids/329K images) adapted from CASIA-Webface (10K ids/490K images) [1]
• Validation datasets
  AgeDB-30 (570 ids/12,240 images/6K pairs)[3]
  LFW (5749 ids/13233 images/6K pairs)[2]
  CALFW (5749 ids/13233 images/6K pairs)[4]
  CFP-FF (500 ids/14K images/7K pairs)[5].
  

Original version in .bin format before preprocessing can be found here.

For optic disc/cup segmentation

Download the dataset into your own folder and change --data-dir correspondingly.
They include:

• Domian1: Drishti-GS dataset [6] with 101 samples, including 50 training and 51 testing samples;
  
• Domain2: RIM-ONE_r3 dataset [7] with 159 samples, including 99 training and 60 testing samples;
  
• Domain3: REFUGE training [8] with 400 samples, including 320 training and 80 testing samples;
  
• Domian4: REFUGE val [8] with 400 samples, including 320 training and 80 testing samples.
  

Train and evaluation

For face recognition

To train a model, for example mobilenet_v2_x0_1 with TVConv, from scratch with 2 RTX 2080Ti, run:

cd face_recognition/
python train_test.py -m mobilenet_v2_x0_1 -a TVConv -g 0,1 

The results will be saved and uploaded to the wandb server. Sign in your account to check out.

For optic disc/cup segmentation

Train and test the model:

cd od_oc_segmentation/
python train_test.py -g 0 --datasetTrain 1 2 3 --datasetTest 4 --batch-size 16

The results will be saved within the ./od_oc_segmentation/result folder.

Citation

If you find our work useful in your research, please consider citing:

@inproceedings{chen2022tvconv,
  title={TVConv: Efficient Translation Variant Convolution for Layout-aware Visual Processing},
  author={Chen, Jierun and He, Tianlang and Zhuo, Weipeng and Ma, Li and Ha, Sangtae and Chan, S-H Gary},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2022}
}

Acknowledgement

Part of the codes are modified from torchvision.models, FaceX-Zoo, DoFE.

Reference

[1] Dong Yi, Zhen Lei, Shengcai Liao, Stan Z. Li. Learning Face Representation from Scratch. arXiv:1411.7923, 2014.
[2] Gary B. Huang, Manu Ramesh, Tamara Berg, and Erik Learned-Miller. Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments, 2007.
[3] Moschoglou, Stylianos and Papaioannou, Athanasios and Sagonas, Christos and Deng, Jiankang and Kotsia, Irene and Zafeiriou, Stefanos, Agedb: the first manually collected, in-the-wild age database, CVPRW, 2017.
[4] Zheng Tianyue, Deng Weihong, Hu Jiani, Cross-age lfw: A database for studying cross-age face recognition in unconstrained environments, arXiv:1708.08197, 2017.
[5] Sengupta Soumyadip, Chen Jun-Cheng, Castillo Carlos, Patel Vishal M, Chellappa Rama, Jacobs David W, Frontal to profile face verification in the wild, WACV, 2016.
[6] Jayanthi Sivaswamy, S Krishnadas, Arunava Chakravarty, GJoshi, A Syed Tabish, et al. A comprehensive retinal image dataset for the assessment of glaucoma from the optic nerve head analysis. JSM Biomedical Imaging Data Papers, 2(1):1004, 2015.
[7] Francisco Fumero, Silvia Alayón, José L Sanchez, Jose Sigut, and M Gonzalez-Hernandez. Rim-one: An open retinal image database for optic nerve evaluation. In 2011 24th international symposium on computer-based medical systems (CBMS), pages 1–6. IEEE, 2011.
[8] José Ignacio Orlando, Huazhu Fu, João Barbosa Breda, Karel van Keer, Deepti R Bathula, Andrés Diaz-Pinto, Ruogu Fang, Pheng-Ann Heng, Jeyoung Kim, JoonHo Lee, et al. Refuge challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs. Medical image analysis, 59:101570, 2020.