Diffusion-Domain-Teacher

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Diffusion-Domain-Teacher

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[MM2024] Diffusion Domain Teacher: Diffusion Guided Domain Adaptive Object Detector

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<div align="center">
    <h2>
        Diffusion Domain Teacher: Diffusion Guided Domain Adaptive Object Detector
    </h2>
</div>
<br>

This repository is the code implementation of the paper **Diffusion Domain Teacher: Diffusion Guided Domain Adaptive Object Detector** , which is based on the [MMDetection](https://github.com/open-mmlab/mmdetection) project.

## Installation
### Requirements
- Linux system, Windows is not tested
- Python 3.8+, recommended 3.11
- PyTorch 2.0 or higher, recommended 2.0.0
- CUDA 11.7 or higher, recommended 11.8
- MMCV 2.0 or higher, recommended 2.0.0
- MMDetection 3.0 or higher, recommended 3.3.0
- diffusers 0.20.0 or higher, recommended 0.20.2
### Environment Installation

It is recommended to use conda for installation. The following commands will create a virtual environment named `DDT` and install PyTorch and MMCV. In the following installation steps, the default installed CUDA version is **11.8**. 
If your CUDA version is not 11.8, please modify it according to the actual situation.
Note: If you are experienced with PyTorch and have already installed it, you can skip to the next section. Otherwise, you can follow the steps below.

**Step 1**: Create a virtual environment named `DDT` and activate it.

```shell
conda create -n DDT python=3.11 -y
conda activate DDT
```

**Step 2**: Install [PyTorch2.x](https://pytorch.org/get-started/locally/).

Linux/Windows:
```shell
conda install pytorch==2.0.0 torchvision==0.15.0 torchaudio==2.0.0 pytorch-cuda=11.8 -c pytorch -c nvidia
```

**Step 3**: Install [MMDetection-3.x](https://mmdetection.readthedocs.io/en/latest/get_started.html).

```shell
pip install -U openmim
mim install mmengine
mim install "mmcv>=2.0.0"
mim install mmdet=3.3.0
```

**Step 4**: Prepare for [Stable-diffusion-1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5) with diffusers

```shell
git lfs install
git clone https://huggingface.co/runwayml/stable-diffusion-v1-5
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/runwayml/stable-diffusion-v1-5
```
And you should move the **Stable-diffusion-1.5** to the same dir as our **DDT**. Then:

```shell
pip install diffusers==0.20.2
```

## Dataset Preparation

### Cross-domian detection datasets

We provide the cross-domain detection dataset used in the paper. To facilitate the fast implementation of code, we provide Coco-style json files for all datasets [google drive](https://drive.google.com/drive/folders/1D1UMj79BdUBW1Jppl0R9OxUFKrgi3Eei?usp=drive_link).

- Image and annotation download link: [Cityscapes](https://www.cityscapes-dataset.com).
- Image and annotation download link: [BDD 100k](https://bdd-data.berkeley.edu/).
- Image and annotation download link: [SIM10k](https://fcav.engin.umich.edu/projects/driving-in-the-matrix).
- Image and annotation download link: [VOC 07+12](http://host.robots.ox.ac.uk/pascal/VOC/).
- Image and annotation download link: [Clipart, Comic, Watercolor](https://github.com/naoto0804/cross-domain-detection/tree/master/datasets).
## Code of DDT

**Important code directories**：

- `DA`：The root directory of our config file for DDT.
- `DA/_base_/da_setting`：Training and lr config of DDT.
- `DA/_base_/datasets`：Six cross-domain datasets config of DDT.
- `DA/Ours`：Detector config on six cross-domian datastes of DDT.
- [`mmdet/models/backbones/dift_encoder.py`](mmdet/models/backbones/dift_encoder.py)：Code and setting of  diffusion backbone.
- [`mmdet/models/detectors/Z_dift_semi_base.py`](mmdet/models/detectors/Z_dift_semi_base.py)：Code of diffusion teacher for self-training.
- [`mmdet/models/detectors/Z_domain_adaptation_detector.py`](mmdet/models/detectors/Z_domain_adaptation_detector.py)：Main code of DDT.
- [`mmdet/engine/hooks/adaptive_teacher_hook.py`](mmdet/engine/hooks/adaptive_teacher_hook.py)：Training and testing hooks of DDT.

## Model Training

### Diffusion detector training
The models are trained for 20,000 steps on two 3090 GPUs, with a batch size of 16. 
If your settings are different from ours, please modify the training steps and default learning rate settings in [training config](DA/_base_/da_setting/da_20k_0.1backbone.py).
Or You can use the trained models that we provide [google drive](https://drive.google.com/drive/folders/11YnyZLGXqJmgwptBzZRoRO3lvdHBudPw?usp=drive_link).
#### Multi-gpu Training
```shell
sh ./tools/dist_train.sh ${CHECKPOINT_FILE} ${GPU_NUM}  # CHECKPOINT_FILE is the configuration file you want to use, GPU_NUM is the number of GPUs used
```
For example:
```shell
sh ./tools/dist_train.sh DA/Ours/city_to_bdd100k/diffusion_faster-rcnn_fpn_city_to_bdd100k_source.py  2  
```

### Diffusion Domain Teacher
The models are trained for 20,000 steps on two 3090 GPUs, with a batch size of 16.
We train exclusively on the source domain for the first 12000 steps and then perform joint training on both the source and target domain for the remaining 8000 steps.

If your settings are different from ours, please modify the training steps and default learning rate settings in [training config](DA/_base_/da_setting/semi_e2e_20k_0.1backbone.py).
You should first modify the config and weights paths [here](DA/Ours/city_to_bdd100k/DDT_r101_fpn_city_to_bdd100k.py), ensuring that the differential detector being used matches your location.

For example:
```shell
sh ./tools/dist_train.sh DA/Ours/city_to_bdd100k/DDT_r101_fpn_city_to_bdd100k.py 2 
```
## Model Testing
#### Multi-gpu Testing：

Note: Please change the code [here](DA/Ours/sim10k_to_bdd100k/DDT_r101_fpn_sim10k_to_bdd100k.py) ***detector.dift_model.config*** and ***detector.dift_model.pretrained_model*** as None before test.

```shell
sh ./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM}  # CONFIG_FILE is the configuration file you want to use, CHECKPOINT_FILE is the checkpoint file you want to use, GPU_NUM is the number of GPUs used
```

## Trained models

- **Cityscapes to BDD100k:** [google drive](https://drive.google.com/drive/folders/11YnyZLGXqJmgwptBzZRoRO3lvdHBudPw?usp=drive_link)
- **Sim10k to BDD 100k:** [google drive](https://drive.google.com/drive/folders/11YnyZLGXqJmgwptBzZRoRO3lvdHBudPw?usp=drive_link)
- **Sim10k to Cityscapes:** [google drive](https://drive.google.com/drive/folders/11YnyZLGXqJmgwptBzZRoRO3lvdHBudPw?usp=drive_link)
- **VOC to Clipart:** [google drive](https://drive.google.com/drive/folders/11YnyZLGXqJmgwptBzZRoRO3lvdHBudPw?usp=drive_link)
- **VOC to Comic:** [google drive](https://drive.google.com/drive/folders/11YnyZLGXqJmgwptBzZRoRO3lvdHBudPw?usp=drive_link)
- **VOC to Watercolor:** [google drive](https://drive.google.com/drive/folders/11YnyZLGXqJmgwptBzZRoRO3lvdHBudPw?usp=drive_link)

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