AFN

PyTorch implementation for Unsupervised Domain Adaptation: An Adaptive Feature Norm Approach.

Requirements

• Platform : Linux

• Hardware : Nvidia GPU

• Others:

  • CUDA 9.0.176
  • PyTorch 0.4.1
  • tqdm

Datasets

Please follow the README.md in subfolder Data to organize the datasets

Trainning and Evaluation

• Make sure you have organized datasets and satisfied the requirements.

• According to the hierarchy in following block, enter corresponding setting ,dataset and method folder.

• Modify parameters: data_root, result and snapshot in main.sh，and can switch model through changing model.

• If you want to run the IAFN+ENT mothod on Office-31 or ImageCLEF-DA, you have to modify the command CUDA_VISIBLE_DEVICES=${gpu_id} python train.py \ to CUDA_VISIBLE_DEVICES=${gpu_id} python train_ent.py \

• run bash main.sh in your terminal

.
├── README.md
├── data
│   ├── ImageCLEF
│   ├── Office31
│   ├── OfficeHome
│   ├── README.md
│   └── Visda2017
├── partial
│   ├── OfficeHome
│   │   ├── HAFN
│   │   └── IAFN
│   ├── README.md
│   └── Visda2017
│       ├── HAFN
│       └── IAFN
├── resources
└── vanilla
    ├── ImageCLEF
    │   ├── HAFN
    │   └── IAFN
    ├── Office31
    │   ├── HAFN
    │   └── IAFN
    ├── README.md
    └── Visda2017
        ├── HAFN
        └── IAFN

Here are some description of parameters :

• data_root : the directory of data.
• snapshot : the directory to store and load state dicts.
• result : the directory that store evaluating results.
• post : distinguish each experiment.
• repeat : distinguish each repeated result in a experiment.
• gpu_id : the GPU ID to run experiments.
• model : switch model between resnet101 and resnet50