DTP

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[ICCV 2023] Official implement of <Disentangle then Parse: Night-time Semantic Segmentation with Illumination Disentanglement>

DTP 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# DTP (Disentangle then Parse)

This repository implements a PyTorch re-implementation of the research paper: ["Disentangle then Parse: Night-time Semantic Segmentation with Illumination Disentanglement"](https://arxiv.org/abs/).

![overview](https://github.com/w1oves/DTP/assets/54713447/d9725a14-7495-4740-ac0c-ed5597d45d20)

## Dataset

Nightcity-fine: Access the dataset via [Google Drive](https://drive.google.com/file/d/1Ilj99NMAmkZIPQcVOd6cJebnKXjJ-Sit/view?usp=drive_link).

Cityscapes: Access the dataset via [cityscapes-dataset.com](https://www.cityscapes-dataset.com/downloads/).

## Environment Setup

Set up your environment with these steps:

```bash
conda create -n dtp python=3.10
conda activate dtp
conda install pytorch torchvision torchaudio pytorch-cuda=11.6 -c pytorch -c nvidia
# Alternatively: pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu116
pip install tensorboard
pip install -U openmim
mim install mmcv-full
pip install -v -e .
# Alternatively: python setup.py develop
```

## Preparation

### Download and Organize

1. Decompress the `nightcity-fine.zip` dataset and relocate it to `./data/nightcity-fine`.

2. Download Cityscapes

1. [gtFine_trainvaltest.zip (241MB)](https://www.cityscapes-dataset.com/file-handling/?packageID=1) [[md5\]](https://www.cityscapes-dataset.com/md5-sum/?packageID=1)

2. [gtCoarse.zip (1.3GB)](https://www.cityscapes-dataset.com/file-handling/?packageID=2) [[md5\]](https://www.cityscapes-dataset.com/md5-sum/?packageID=2)

3. [leftImg8bit_trainvaltest.zip (11GB)](https://www.cityscapes-dataset.com/file-handling/?packageID=3) [[md5\]](https://www.cityscapes-dataset.com/md5-sum/?packageID=3)

4. ```shell
      git clone https://github.com/mcordts/cityscapesScripts.git
      
      pip install cityscapesscripts
      ```

5. Extract all the above zip files and git repo into the `./data` folder

6. ```shell
      vim cityscapesScripts/cityscapesscripts/preparation/createTrainIdLabelImgs.py 
      
      Add the next line of code after `import os`
      os.envieron['CITYSCAPES_DATASET'] = "../../../"
      
      python cityscapesScripts/cityscapesscripts/preparation/createTrainIdLabelImgs.py
      
      mkdir cityscapes
      
      mv gtFine cityscapes && mv leftImg8bit cityscapes
      ```

3. Download the checkpoint from [Google Drive](https://drive.google.com/file/d/1g-32y3N3RGOOiCe7hfjJKmB2D70w0Nrp/view?usp=drive_link) and place it in `./checkpoints`.

Your directory structure should resemble:

```plaintext
.
├── checkpoints
│   ├── night
│   ├── night+day
|   └── simmim_pretrain__swin_base__img192_window6__800ep.pth
├── custom
├── custom-tools
│   ├── dist_test.sh
│   ├── dist_train.sh
│   ├── test.py
│   └── train.py
├── data
│   ├── cityscapes
│   │   ├── gtFine
│   │   └── leftImg8bit
│   └── nightcity-fine
│       ├── train
│       └── val
├── mmseg
├── readme.md
├── requirements.txt
├── setup.cfg
└── setup.py
```

## Testing

Execute tests using:

```bash
python custom-tools/test.py checkpoints/night/cfg.py checkpoints/night/night.pth --eval mIoU --aug-test
```

## Training
1. Download pre-training weight from [Google Drive](https://drive.google.com/file/d/15zENvGjHlM71uKQ3d2FbljWPubtrPtjl/view).
2. Convert it to MMSeg format using:
    ```shell
    python custom-tools/swin2mmseg.py </path/to/pretrain> checkpoints/simmim_pretrain__swin_base__img192_window6__800ep.pth
    ```
3. Start training with:
    ```shell
    python custom-tools/train.py </path/to/your/config>
    # </path/to/your/config>：our config: checkpoints/night/cfg.py or checkpoints/night+day/cfg.py
    ```

## Results

The table below summarizes our findings:

| logs                                            | train dataset                  | validation dataset | mIoU |
|-------------------------------------------------|--------------------------------|--------------------|------|
| checkpoints/night/eval_multi_scale_20230801_162237.json | nightcity-fine                 | nightcity-fine     | 64.2 |
| checkpoints/night+day/eval_multi_scale_20230809_170141.json | nightcity-fine + cityscapes    | nightcity-fine     | 64.9 |

# Acknowledgements
This dataset is refined based on the dataset of [NightCity](https://dmcv.sjtu.edu.cn/people/phd/tanxin/NightCity/index.html) by Xin Tan *et al.* and [NightLab](https://github.com/xdeng7/NightLab) by Xueqing Deng *et al.*.

This project is based on the [mmsegmentation](https://github.com/open-mmlab/mmsegmentation.git).

Pretraining checkpoint comes from the [SimMIM](https://github.com/microsoft/SimMIM).

The annotation process was completed using [LabelMe](https://github.com/wkentaro/labelme.git).

# Citation
If you find this code or data useful, please cite our paper
```
@InProceedings{Wei_2023_ICCV,
    author    = {Wei, Zhixiang and Chen, Lin and Tu, Tao and Ling, Pengyang and Chen, Huaian and Jin, Yi},
    title     = {Disentangle then Parse: Night-time Semantic Segmentation with Illumination Disentanglement},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2023},
    pages     = {21593-21603}
}
```

DTP

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