by Lukas Hoyer, Dengxin Dai, and Luc Van Gool

[Arxiv] [Paper]

Unsupervised domain adaptation (UDA) aims to adapt a model trained on synthetic data to real-world data without requiring expensive annotations of real-world images. As UDA methods for semantic segmentation are usually GPU memory intensive, most previous methods operate only on downscaled images. We question this design as low-resolution predictions often fail to preserve fine details. The alternative of training with random crops of high-resolution images alleviates this problem but falls short in capturing long-range, domain-robust context information.

Therefore, we propose HRDA, a multi-resolution training approach for UDA, that combines the strengths of small high-resolution crops to preserve fine segmentation details and large low-resolution crops to capture long-range context dependencies with a learned scale attention, while maintaining a manageable GPU memory footprint.

HRDA enables adapting small objects and preserving fine segmentation details. It significantly improves the state-of-the-art performance by 5.5 mIoU for GTA→Cityscapes and by 4.9 mIoU for Synthia→Cityscapes, resulting in an unprecedented performance of 73.8 and 65.8 mIoU, respectively.

The more detailed domain-adaptive semantic segmentation of HRDA, compared to the previous state-of-the-art UDA method DAFormer, can also be observed in example predictions from the Cityscapes validation set.

For more information on HRDA, please check our [Paper].

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

@Article{hoyer2022hrda,
  title={{HRDA}: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation},
  author={Hoyer, Lukas and Dai, Dengxin and Van Gool, Luc},
  journal={arXiv preprint arXiv:2204.13132},
  year={2022}
}

For this project, we used python 3.8.5. We recommend setting up a new virtual environment:

python -m venv ~/venv/hrda
source ~/venv/hrda/bin/activate

In that environment, the requirements can be installed with:

pip install -r requirements.txt -f https://download.pytorch.org/whl/torch_stable.html
pip install mmcv-full==1.3.7  # requires the other packages to be installed first

Further, please download the MiT weights from SegFormer using the following script. If problems occur with the automatic download, please follow the instructions for a manual download within the script.

sh tools/download_checkpoints.sh

Cityscapes: Please, download leftImg8bit_trainvaltest.zip and gt_trainvaltest.zip from here and extract them to data/cityscapes.

GTA: Please, download all image and label packages from here and extract them to data/gta.

Synthia: Please, download SYNTHIA-RAND-CITYSCAPES from here and extract it to data/synthia.

The final folder structure should look like this:

DAFormer
├── ...
├── data
│   ├── cityscapes
│   │   ├── leftImg8bit
│   │   │   ├── train
│   │   │   ├── val
│   │   ├── gtFine
│   │   │   ├── train
│   │   │   ├── val
│   ├── gta
│   │   ├── images
│   │   ├── labels
│   ├── synthia
│   │   ├── RGB
│   │   ├── GT
│   │   │   ├── LABELS
├── ...

Data Preprocessing: Finally, please run the following scripts to convert the label IDs to the train IDs and to generate the class index for RCS:

python tools/convert_datasets/gta.py data/gta --nproc 8
python tools/convert_datasets/cityscapes.py data/cityscapes --nproc 8
python tools/convert_datasets/synthia.py data/synthia/ --nproc 8

The provided HRDA checkpoint trained on GTA->Cityscapes (already downloaded by tools/download_checkpoints.sh) can be tested on the Cityscapes validation set using:

sh test.sh work_dirs/gtaHR2csHR_hrda_246ef

The predictions are saved for inspection to work_dirs/gtaHR2csHR_hrda_246ef/preds and the mIoU of the model is printed to the console. The provided checkpoint should achieve 73.79 mIoU. Refer to the end of work_dirs/gtaHR2csHR_hrda_246ef/20220215_002056.log for more information such as the class-wise IoU.

If you want to visualize the LR predictions, HR predictions, or scale attentions of HRDA on the validation set, please refer to test.sh for further instructions.

For convenience, we provide an annotated config file of the final HRDA. A training job can be launched using:

python run_experiments.py --config configs/hrda/gtaHR2csHR_hrda.py

The logs and checkpoints are stored in work_dirs/.

For the other experiments in our paper, we use a script to automatically generate and train the configs:

python run_experiments.py --exp <ID>

More information about the available experiments and their assigned IDs, can be found in experiments.py. The generated configs will be stored in configs/generated/.

When training a model on Synthia->Cityscapes, please note that the evaluation script calculates the mIoU for all 19 Cityscapes classes. However, Synthia contains only labels for 16 of these classes. Therefore, it is a common practice in UDA to report the mIoU for Synthia->Cityscapes only on these 16 classes. As the Iou for the 3 missing classes is 0, you can do the conversion mIoU16 = mIoU19 * 19 / 16.

This project is based on mmsegmentation version 0.16.0. For more information about the framework structure and the config system, please refer to the mmsegmentation documentation and the mmcv documentation.

The most relevant files for HRDA are:

• configs/hrda/gtaHR2csHR_hrda.py: Annotated config file for the final HRDA.
• mmseg/models/segmentors/hrda_encoder_decoder.py: Implementation of the HRDA multi-resolution encoding with context and detail crop.
• mmseg/models/decode_heads/hrda_head.py: Implementation of the HRDA decoding with multi-resolution fusion and scale attention.
• mmseg/models/uda/dacs.py: Implementation of the DAFormer self-training.

HRDA is based on the following open-source projects. We thank their authors for making the source code publicly available.

• DAFormer
• MMSegmentation
• SegFormer
• DACS