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EvPSNet
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EvPSNet
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# EvPSNet: Evidential Panoptic Segmentation Network for Uncertainy-aware Panoptic Segmentation EvPSNet is the first approach to tackle the task of uncertainty-aware panoptic segmentation, with an aim to provide per-pixel semantic and instance label together with per-pixel panoptic uncertainty estimation.  This repository contains the **PyTorch implementation** of our RAL'2023 paper [Uncertainty-aware Panoptic Segmentation](https://arxiv.org/abs/2206.14554). The repository builds on [EfficientPS](https://github.com/DeepSceneSeg/EfficientPS), [mmdetection](https://github.com/open-mmlab/mmdetection) and [gen-efficientnet-pytorch](https://github.com/rwightman/gen-efficientnet-pytorch) codebases. If you find the code useful for your research, please consider citing our paper: ``` @article{sirohi2023uncertainty, title={Uncertainty-aware panoptic segmentation}, author={Sirohi, Kshitij and Marvi, Sajad and B{\"u}scher, Daniel and Burgard, Wolfram}, journal={IEEE Robotics and Automation Letters}, volume={8}, number={5}, pages={2629--2636}, year={2023}, publisher={IEEE} } ``` ## System Requirements * Linux * Python 3.7 * PyTorch 1.7 * CUDA 10.2 * GCC 7 or 8 **IMPORTANT NOTE**: These requirements are not necessarily mandatory. However, we have only tested the code under the above settings and cannot provide support for other setups. ## Installation a. Create a conda virtual environment from the provided environment.yml and activate it. ```shell git clone https://github.com/kshitij3112/EvPSNet.git cd EvPSNet conda env create -n EvPSnet_env --file=environment.yml conda activate EvPSnet_env ``` b. Install all other dependencies using pip: ```bash pip install -r requirements.txt ``` c. Install EfficientNet implementation ```bash cd efficientNet python setup.py develop ``` d. Install EvPSNet implementation ```bash cd .. python setup.py develop ``` ## Prepare datasets It is recommended to symlink the dataset root to `$EvPSNet/data`. If your folder structure is different, you may need to change the corresponding paths in config files. ``` EvPSNet ├── mmdet ├── tools ├── configs └── data └── cityscapes ├── annotations ├── train ├── val ├── stuffthingmaps ├── cityscapes_panoptic_val.json └── cityscapes_panoptic_val ``` The cityscapes annotations have to be converted into the aforementioned format using `tools/convert_datasets/cityscapes.py`: ```shell python tools/convert_cityscapes.py ROOT_DIRECTORY_OF_CITYSCAPES ./data/cityscapes/ cd .. git clone https://github.com/mcordts/cityscapesScripts.git cd cityscapesScripts/cityscapesscripts/preparation python createPanopticImgs.py --dataset-folder path_to_cityscapes_gtFine_folder --output-folder ../../../EvPSNet/data/cityscapes --set-names val ``` ## Training and Evaluation ### Training Procedure Edit the config file appropriately in configs folder. Train with a single GPU: ``` python tools/train.py configs/EvPSNet_unc_singlegpu.py --work_dir work_dirs/checkpoints --validate ``` Train with multiple GPUS: ``` ./tools/dist_train.sh configs/EvPSNet_unc_mutigpu.py ${GPU_NUM} --work_dir work_dirs/checkpoints --validate ``` * --resume_from ${CHECKPOINT_FILE}: Resume from a previous checkpoint file. ### Evaluation Procedure Test with a single GPU: ``` python tools/test.py configs/EvPSNet_unc_singlegpu.py ${CHECKPOINT_FILE} --eval panoptic ``` Test with multiple GPUS: ``` ./tools/dist_test.sh configs/EvPSNet_unc_mutigpu.py ${CHECKPOINT_FILE} ${GPU_NUM} --eval panoptic ``` ## Additional Notes: * tool/cityscapes_inference.py: saves predictions in the official cityscapes panoptic format. * tool/cityscapes_save_predictions.py: saves color visualizations. * This is an impmentation of EvPSNet in PyTorch. Please refer to the metrics reported in [Uncertainty-aware Panoptic Segmentation](https://arxiv.org/abs/2206.14554) when making comparisons. ## Acknowledgements We have used utility functions from other open-source projects. We especially thank the authors of: - [EfficientPS](https://github.com/DeepSceneSeg/EfficientPS) - [mmdetection](https://github.com/open-mmlab/mmdetection) - [gen-efficientnet-pytorch](https://github.com/rwightman/gen-efficientnet-pytorch) - [seamseg](https://github.com/mapillary/seamseg.git) ## Contacts * [Kshitij Sirohi](http://www2.informatik.uni-freiburg.de/~sirohik/) * [Sajad Marvi](http://www2.informatik.uni-freiburg.de/~sirohik/) * [Daniel Büscher](http://www2.informatik.uni-freiburg.de/~buescher/) ## License For academic usage, the code is released under the [GPLv3](https://www.gnu.org/licenses/gpl-3.0.en.html) license. For any commercial purpose, please contact the authors.
