First, please install cuda version 11.0.3 available at https://developer.nvidia.com/cuda-11-0-3-download-archive. It is required to build mmcv-full later.

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

python -m venv ~/venv/mic-seg
source ~/venv/mic-seg/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
sh install_tsne.sh
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 (Optional): Please, download SYNTHIA-RAND-CITYSCAPES from here and extract it to data/synthia.

ACDC (Optional): Please, download rgb_anon_trainvaltest.zip and gt_trainval.zip from here and extract them to data/acdc. Further, please restructure the folders from condition/split/sequence/ to split/ using the following commands:

rsync -a data/acdc/rgb_anon/*/train/*/* data/acdc/rgb_anon/train/
rsync -a data/acdc/rgb_anon/*/val/*/* data/acdc/rgb_anon/val/
rsync -a data/acdc/gt/*/train/*/*_labelTrainIds.png data/acdc/gt/train/
rsync -a data/acdc/gt/*/val/*/*_labelTrainIds.png data/acdc/gt/val/

Dark Zurich (Optional): Please, download the Dark_Zurich_train_anon.zip and Dark_Zurich_val_anon.zip from here and extract it to data/dark_zurich.

The final folder structure should look like this:

AADA
├── ...
├── data
│   ├── acdc (optional)
│   │   ├── gt
│   │   │   ├── train
│   │   │   ├── val
│   │   ├── rgb_anon
│   │   │   ├── train
│   │   │   ├── val
│   ├── cityscapes
│   │   ├── leftImg8bit
│   │   │   ├── train
│   │   │   ├── val
│   │   ├── gtFine
│   │   │   ├── train
│   │   │   ├── val
│   ├── dark_zurich (optional)
│   │   ├── gt
│   │   │   ├── val
│   │   ├── rgb_anon
│   │   │   ├── train
│   │   │   ├── val
│   ├── gta
│   │   ├── images
│   │   ├── labels
│   ├── synthia (optional)
│   │   ├── 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

For convenience, we provide an annotated config file of the final MIC(HRDA) on GTA→Cityscapes. A training job can be launched using:

CUDA_VISIBLE_DEVICES=0 python run_experiments.py --config configs/mic/gtaHR2csHR_aada_mic_hrda.py
CUDA_VISIBLE_DEVICES=0 python run_experiments.py --config configs/mic/synthiaHR2csHR_aada_mic_hrda.py
CUDA_VISIBLE_DEVICES=0 python run_experiments.py --config configs/mic/csHR2darkHR_aada_mic_hrda.py
CUDA_VISIBLE_DEVICES=0 python run_experiments.py --config configs/mic/csHR2acdcHR_aada_mic_hrda.py

The logs and checkpoints are stored in work_dirs/.

A trained model can be evaluated using:

sh test.sh work_dirs/run_name/

The predictions are saved for inspection to work_dirs/run_name/preds and the mIoU of the model is printed to the console.

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.

The results for Cityscapes→ACDC and Cityscapes→DarkZurich are reported on the test split of the target dataset. To generate the predictions for the test set, please run:

python -m tools.test path/to/config_file path/to/checkpoint_file --test-set --format-only --eval-option imgfile_prefix=labelTrainIds to_label_id=False

t-SNE --SelClass [all, easy, hard, mix]

python -m tools.test_tSNE path/to/config_file path/to/checkpoint_file --sample 1000 --show-dir dir_name --SelClass easy

The predictions can be submitted to the public evaluation server of the respective dataset to obtain the test score.

Below, we provide checkpoints of MIC(HRDA) for the different benchmarks. As the results in the paper are provided as the mean over three random seeds, we provide the checkpoint with the median validation performance here.

• AADA for GTA→Cityscapes
• AADA for Synthia→Cityscapes
• AADA for Cityscapes→ACDC
• AADA for Cityscapes→DarkZurich

The checkpoints come with the training logs. Please note that:

• The logs provide the mIoU for 19 classes. For Synthia→Cityscapes, it is necessary to convert the mIoU to the 16 valid classes. Please, read the section above for converting the mIoU.
• The logs provide the mIoU on the validation set. For Cityscapes→ACDC and Cityscapes→DarkZurich the results reported in the paper are calculated on the test split. For DarkZurich, the performance significantly differs between validation and test split. Please, read the section above on how to obtain the test mIoU.

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 MIC are:

• configs/mic/gtaHR2csHR_aada_mic_hrda.py: Annotated config file for AADA on GTA→Cityscapes.
• mmseg/models/uda/aada.py: Implementation of AADA.
• mmseg/augmentation/*: Implementation of the Auto-augmentation

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

• TeachAugment
• MIC
• HRDA
• DAFormer
• MMSegmentation
• SegFormer
• DACS