Requirements

• Python 3.7
• torchvision 0.9.0
• PyTorch 1.8.0

Train:

• Unsupervised DA on Office31,OfficeHome, and VisDA2017 datasets:

  sh runUDA.sh

• Unsupervised DA on ImageNet-scale dataset:

  python main.py --dataset_root ./data/ --src IN --tgt INR --contrast_dim 256 --module domain_loss --cw 1 --lr 0.003 --batch_size 32 --max_key_size 20 --max_iterations 50000
  

• Semi-supervised DA on COVID-19 dataset:

  sh runSSDA.sh

Log:

• The training log will be generated in the folder with --log_dir. We can visualize the training process through tensorboard as follows.

  tensorboard --logdir=/log_dir/ --host= `host address`

Usage

• We uploaded the file PythonGraphPers_withCompInfo.so for computing the connected components. If you need to generate it, you can compile the C++ code in folder ref, run ./compile_pers_lib.sh (by default it requires Python 3.7. If you are using other Python versions, modify the command inside compile_pers_lib.sh).

• pybind11 is a lightweight header-only library that exposes C++ types in Python and vice versa, mainly to create Python bindings of existing C++ code. Our code will be further improved to make it cleaner and easier to use.

Note: Place the datasets in the corresponding data path.