panoptic-segmentation-experiments

This repository contains experiments with panoptic segmentation models.

Setup python environment

1. Clone the repository using git clone command.
2. Open the terminal and go to the project directory using cd command.
3. Create virtual environment using python -m venv venv or conda create -n venv python=3.10 command.
4. Activate virtual environment using source venv/bin/activate or conda activate venv command.
5. Install poetry using instructions from here. Use with the official installer section.
6. Set the following option to disable new virtualenv creation:

   poetry config virtualenvs.create false

7. Install dependencies using make install_deps command.
8. Setup pre-commit hooks using pre-commit install command. More information about pre-commit you can find here.
9. Run the test to check the correctness of the project work using following command:

   python -m unittest -b

10. After successful passing of the tests, you can work with the project!
11. If you want to add new dependencies, use poetry add <package_name> command. More information about poetry you can find here.
12. If you want to add new tests, use unittest library. More information about unittest you can find here. All tests should be placed in the tests directory.
13. All commits should be checked by pre-commit hooks. If you want to skip this check, use git commit --no-verify command. But it is not recommended to do this.
14. Also, you can run pre-commit hooks manually using pre-commit run --all-files command.
15. More useful commands you can find in Makefile.

Examples

How to start?

1. Setup environment and install dependencies (previous section).
2. Download Cityscapes dataset and extract it to the data directory.

    make download_cityscapes

3. You should have the following structure:

   data
   └── cityscapes
       ├── gtFine
       │   ├── test
       │   ├── train
       │   └── val
       └── leftImg8bit
           ├── test
           ├── train
           └── val

4. Run the following command to convert Cityscapes dataset to COCO format:

   make convert_cityscapes_to_coco_3

5. After that, you should have the following structure:

   data
   └── cityscapes
       ├── gtFine
       │   ├── test
       │   ├── train
       │   ├── val
       │   ├── cityscapes_panoptic_val
       │   ├── cityscapes_panoptic_train
       │   └── cityscapes_panoptic_test
       ├── leftImg8bit
       │   ├── test
       │   ├── train
       │   ├── val
       │   ├── cityscapes_panoptic_val
       │   ├── cityscapes_panoptic_train
       │   └── cityscapes_panoptic_test
       └── annotations
           ├── cityscapes_panoptic_test.json
           ├── cityscapes_panoptic_train.json
           └── cityscapes_panoptic_val.json

6. Copy src/cityscapes_panoptic_dataset.py to the mmdetection/mmdet/datasets directory. Also, add an import of the CityscapesPanopticDataset class to the mmdetection/mmdet/datasets/__init__.py file. Finally, add the following class to __all__ list.
7. Run the following command to start training:

   make run_train

8. Run the following command to start testing:

   make run_test