This is implementation for the paper: Class Relationship Embedded Learning for Source-Free Unsupervised Domain Adaptation.

Project structure

The project structure is presented as follows

├──basicda  
├──configs
| ├──_base_
| ├────cls_datasets
| ├────cls_models
| ├──sfda_class_relation
├──data
├──runs
├──CRCo
| ├──hooks
| ├──loaders
| ├──models
| ├──trainers
├──experiments
| ├──sfda_class_relation_train.sh
| ├──sfda_source_only_train.sh
├──train.py

basicda: basic framework for domain adaptation tasks

configs: training configs files for different experiments

data: contain dataset images and labels

runs: automatically created which stores checkpoints, tensorboard and text logging files

CRCo: source code of our method, contains hooks (evaluation hooks), loaders (train and test loaders), models ( definition of models), trainers (training and testing process)

experiments: training scripts

train.py: entrance to training process

Below are the structure under data.

│officehome/
├──Art/
│  ├── Alarm_Clock
│  │   ├── 00001.jpg
│  │   ├── 00002.jpg
│  │   ├── ......
│  ├── Backpack
│  │   ├── 00001.jpg
│  │   ├── 00002.jpg
│  │   ├── ......
│  ├── ......
├──Clipart/
│  ├── Alarm_Clock
│  │   ├── 00001.jpg
│  │   ├── 00002.jpg
│  │   ├── ......
│  ├── Backpack
│  │   ├── 00001.jpg
│  │   ├── 00002.jpg
│  │   ├── ......
│  ├── ......
│txt/
├──officehome/
│  ├── labeled_source_images_Art.txt

Core files

1. Model definition:

   ./CRCo/models/sfda_simplified_contrastive_model.py

2. Training process:

   ./CRCo/trainers/trainer_sfda_class_relation.py

Steps to reproduce the results

Here we take Office-Home A->C under the close-set source-free unsupervised Domain adaptation setting as an example.

1. Some preparations.
   • Make a folder '/home/username/PycharmProjects', replace the username by your name under /home.
   • Download the "code.zip" here. Unzip it under '/home/username/PycharmProjects', and rename this folder by 'class-relation-learning' (needed in sfda_source_only_train.sh and sfda_class_relation_train.sh)
   • Prepare dataset following the data folder structure.
2. Go to the project home

cd /home/username/PycharmProjects/class-relation-learning

3. Training source domain model using the following script. Here single GPU is enough. In the following scripts, "my_exp" is an experiment name specified by yourself. The .py file stores the configurations for training and testing.

CUDA_VISIBLE_DEVICES=0 bash ./experiments/sfda_source_only_train.sh my_exp ./configs/sfda_class_relation/sfda_officehome_source_A.py

After trained done, you can find the source pre-trained model in the runs folder. You should keep the path of model in mind for the target domain training. Or you can skip the step by using our provided models.

4. Configure the source model path (absolute path) in configs/sfda_class_relation/class_relation_officehome_AaD_AC.py configuration file. Specifically, append the absolute path in "pretrained_model" key which looks as follows:

control = dict(
  save_interval=500,
  max_save_num=1,
  seed=2023,
  pretrained_model =  /home/***/source_model_Art.pth,
)

5. Train target domain model using the following script. Since we have three views for each image (one weak and two strong views), we need Two GPUs with 11G memory for training. For larger models like ResNet101 or ViT-B, larger GPU memory is necessary.

CUDA_VISIBLE_DEVICES=0,1 bash ./experiments/sfda_class_relation_train.sh my_exp ./configs/sfda_class_relation/class_relation_officehome_AaD_AC.py

The results of all training metrics and testing accuracy is stored in the runs folder. You can view them from the txt log file or through tensorboard.