About

This is the source code of the paper Assessing Domain Gap for Continual Domain Adaptation in Object Detection

If you find our code is useful, please cite:

@article{Doan23DGECDA,
    author    = {Anh-Dzung Doan and Bach Long Nguyen and Surabhi Gupta and Ian Reid and Markus Wagner and Tat-Jun Chin},
    title     = {Assessing Domain Gap for Continual Domain Adaptation in Object Detection},
    journal   = {Computer Vision and Image Understanding},
    year      = {2024}
}

Installation

Our installation is mainly based on Detectron2's installation with few modifications

• Clone this repo, suppose the source code is saved in [ROOT_DIR]/DGE-CDA/

• Install Python >= 3.7 (tested on Python 3.9)

• Install Pytorch >= 1.8 and torchvision that matches the Pytorch installation (tested on Pytorch 1.11, torchvision 0.12, CUDA 11.3)

• Install OpenCV

  pip install opencv-python
  

  (tested on opencv-python 4.5.5.64)

• Install detectron2

  cd [ROOT_DIR] 
  python -m pip install -e DGE-CDA 
  cd [ROOT_DIR]/DGE-CDA
  

• Install POT library

  pip install POT
  

• Install pyJoules

  pip install pyJoules[nvidia]
  

• Install tqdm

  pip install tqdm
  

Dataset preparation

• Download the dataset DGTA_SeaDronesSee_jpg from DeepGTAV (suppose save it to [ROOT_DIR]/DGTA_SeaDronesSee_jpg)

• Create folder DGTA_SeaDronesSee_merged/ inside [ROOT_DIR]/DGE-CDA/datasets/

• Create folder images/ inside [ROOT_DIR]/DGE-CDA/datasets/DGTA_SeaDronesSee_merged/

• Copy all images from [ROOT_DIR]/DGTA_SeaDronesSee_jpg/train/images/ to [ROOT_DIR]/DGE-CDA/datasets/DGTA_SeaDronesSee_merged/images/

• Copy all images from [ROOT_DIR]/DGTA_SeaDronesSee_jpg/val/images/ to [ROOT_DIR]/DGE-CDA/datasets/DGTA_SeaDronesSee_merged/images/

• Create folder experiments/ inside [ROOT_DIR]/DGE-CDA/datasets/DGTA_SeaDronesSee_merged/

• Download all json files from HERE and put them in [ROOT_DIR]/DGE-CDA/datasets/DGTA_SeaDronesSee_merged/experiments/

Finally, we should obtain

• all images located in [ROOT_DIR]/DGE-CDA/datasets/DGTA_SeaDronesSee_merged/images/

• and all json files located in [ROOT_DIR]/DGE-CDA/datasets/DGTA_SeaDronesSee_merged/experiments/

DEMO

• Pretrain RetinaNet on source domain

  python cda/pretrain_source.py --config-file configs/dgta.yaml
  

  After the training finishes, we should find model_final.pth located in work_dir/DeepGTAV/CLEAR_9-15/

• Randomly generate random projections

  python cda/generate_random_projections.py --save-dir work_dir/DeepGTAV/projections
  

  We should find 3 projections in the folder work_dir/DeepGTAV/projections

• To plot the histograms of AP discrepancy and domain gaps (Fig.5 in the paper), run

  python cda/plot_correlation.py --config-file configs/dgta.yaml --projs-dir work_dir/DeepGTAV/projections
  

  This script will take about 4h in Intel-Core-i7 with 16GB RAM and NVIDIA GeForce GTX 1080Ti

• To reproduce the results in Table 5,

  • For SDA with DGE

  python cda/cont_dgta.py --config-file configs/dgta_cont.yaml --projs-dir work_dir/DeepGTAV/projections --gap-thr 0.02 --dge
  

  The trained models can be found in work_dir/DeepGTAV/adapt_with_dge/. The energy consumption record can be found in its subfolders, e.g., work_dir/DeepGTAV/adapt_with_dge/CLEAR_9-15_and_OVERCAST_0-1/energy.csv

  • For SDA w/o DGE

  python cda/cont_dgta.py --config-file configs/dgta_cont.yaml
  

  The trained models can be found in work_dir/DeepGTAV/adapt_wo_dge/. The energy consumption record can be found in its subfolders.

  • It should be noted that
    • The energy unit is Joule
    • there is a common error Permission denied: '/sys/class/powercap/intel-rapl/intel-rapl:0/energy_uj'. To resolve it, simply grant the permission

    sudo chmod -R 7777 /sys/class/powercap/intel-rapl/intel-rapl:0