💀 HAMLET

To Adapt or Not to Adapt? Real-Time Adaptation for Semantic Segmentation (ICCV23)

Marc Botet Colomer^1,2*Pier Luigi Dovesi^3*†Theodoros Panagiotakopoulos⁴Joao Frederico Carvalho¹Linus Härenstam-Nielsen^5,6Hossein Azizpour²Hedvig Kjellström^2,3Daniel Cremers^5,6,7Matteo Poggi⁸

¹Univrses²KTH³Silo AI⁴King⁵Technical University of Munich⁶Munich Center of Machine Learning⁷University of Oxford⁸University of Bologna

^* Joint first authorship. ^† Part of the work carried out while at Univrses.

📜 arxiv 💀 project page 📽️ video

Citation

If you find this repo useful for your work, please cite our paper:

@inproceedings{colomer2023toadapt,
title = {To Adapt or Not to Adapt? Real-Time Adaptation for Semantic Segmentation},
author = {Botet Colomer, Marc and Dovesi, Pier Luigi and Panagiotakopoulos, Theodoros and Carvalho, Joao Frederico and H{\"a}renstam-Nielsen, Linus and Azizpour, Hossein and Kjellstr{\"o}m, Hedvig and Cremers, Daniel and
Poggi, Matteo},
booktitle = {IEEE International Conference on Computer Vision},
note = {ICCV},
year = {2023}
}

Setup Environment

For this project, we used Python 3.9.13. We recommend setting up a new virtual environment:

python -m venv ~/venv/hamlet
source~/venv/hamlet/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

All experiments were executed on a NVIDIA RTX 3090

Setup Datasets

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

Rainy Cityscapes: Please follow the steps as shown here: https://team.inria.fr/rits/computer-vision/weather-augment/

If you have troubles creating the rainy dataset, please contact us in domain-adaptation-group@googlegroups.com to obtain the Rainy Cityscapes dataset

We refer to MMSegmentation for further instructions about the dataset structure.

Prepare the source dataset:

python tools/convert_datasets/cityscapes.py /data/datasets/Cityscapes --out-dir data/Cityscapes --nproc 8

Training

For convenience, it is possible to run the configuration by selecting experiment -1. If wandb is configurated, it can be activated by setting the wandb argument to 1

python run_experiments.py --exp -1 --wandb 1

All assets to run a training can be found here.

Make sure to place the pretrained model mitb1_uda.pth in pretrained/.

We provide a config.py file that can be easily modified to run multiple experiments by changing parameters. Make sure to place the random modules to random_modules/.

Code structure

This code is based on MMSegmentation project. The most relevant files are:

• online_src/domain_indicator_orchestrator.py: Implementation of the Adaptive Domain Detection.
• online_src/online_runner.py: Runner for Hamlet.
• online_src/buffer.py: Buffer sampling methods
• mmseg/models/segmentors/modular_encoder_decoder.py: Implementation of HAMT
• mmseg/models/decode_heads/incremental_decode_head.py: Handle the lightweight decoder
• mmseg/models/decode_heads/segformer_head.py: Implementation of the lightweight decoder on SegFormer
• mmseg/models/backbones/mix_transformer.py: Implementationf of modular freezing for HAMT
• mmseg/models/uda/dacs.py: UDA method using hamlet strategies
• mmseg/core/evaluation/eval_hooks.py: Evaluation methods

Acknowledgements

This project is based on the following open-source projects.

• MMSegmentation
• DAFormer
• SegFormer