mmdetection/projects/DiffusionDet/README.md

## Description

This is an implementation of [DiffusionDet](https://github.com/ShoufaChen/DiffusionDet) based on [MMDetection](https://github.com/open-mmlab/mmdetection/tree/main), [MMCV](https://github.com/open-mmlab/mmcv), and [MMEngine](https://github.com/open-mmlab/mmengine).

<center>
<img src="https://user-images.githubusercontent.com/48282753/211472911-c84d658a-952b-4608-8b91-9ac932cbf2e2.png">
</center>

## Usage

<!-- For a typical model, this section should contain the commands for training and testing. You are also suggested to dump your environment specification to env.yml by `conda env export > env.yml`. -->

### Comparison of results

1. Download the [DiffusionDet released model](https://github.com/ShoufaChen/DiffusionDet#models).

2. Convert model from DiffusionDet version to MMDetection version. We give a [sample script](model_converters/diffusiondet_resnet_to_mmdet.py)
   to convert `DiffusionDet-resnet50` model. Users can download the corresponding models from [here](https://github.com/ShoufaChen/DiffusionDet/releases/download/v0.1/diffdet_coco_res50.pth).

   ```shell
   python projects/DiffusionDet/model_converters/diffusiondet_resnet_to_mmdet.py ${DiffusionDet ckpt path} ${MMDetectron ckpt path}
   ```

3. Testing the model in MMDetection.

   ```shell
   python tools/test.py projects/DiffusionDet/configs/diffusiondet_r50_fpn_500-proposals_1-step_crop-ms-480-800-450k_coco.py ${CHECKPOINT_PATH}
   ```

**Note:** During inference time, DiffusionDet will randomly generate noisy boxes,
which may affect the AP results. If users want to get the same result every inference time, setting seed is a good way.
We give a table to compare the inference results on `ResNet50-500-proposals` between DiffusionDet and MMDetection.

|                                                         Config                                                          | Step |    AP     |
| :---------------------------------------------------------------------------------------------------------------------: | :--: | :-------: |
| [DiffusionDet](https://github.com/ShoufaChen/DiffusionDet/blob/main/configs/diffdet.coco.res50.yaml) (released results) |  1   |   45.5    |
|      [DiffusionDet](https://github.com/ShoufaChen/DiffusionDet/blob/main/configs/diffdet.coco.res50.yaml) (seed=0)      |  1   |   45.66   |
|         [MMDetection](configs/diffusiondet_r50_fpn_500-proposals_1-step_crop-ms-480-800-450k_coco.py) (seed=0)          |  1   |   45.7    |
|       [MMDetection](configs/diffusiondet_r50_fpn_500-proposals_1-step_crop-ms-480-800-450k_coco.py) (random seed)       |  1   | 45.6~45.8 |
| [DiffusionDet](https://github.com/ShoufaChen/DiffusionDet/blob/main/configs/diffdet.coco.res50.yaml) (released results) |  4   |   46.1    |
|      [DiffusionDet](https://github.com/ShoufaChen/DiffusionDet/blob/main/configs/diffdet.coco.res50.yaml) (seed=0)      |  4   |   46.38   |
|         [MMDetection](configs/diffusiondet_r50_fpn_500-proposals_1-step_crop-ms-480-800-450k_coco.py) (seed=0)          |  4   |   46.4    |
|       [MMDetection](configs/diffusiondet_r50_fpn_500-proposals_1-step_crop-ms-480-800-450k_coco.py) (random seed)       |  4   | 46.2~46.4 |

- `seed=0` means hard set seed before generating random boxes.
  ```python
  # hard set seed=0 before generating random boxes
  seed = 0
  random.seed(seed)
  torch.manual_seed(seed)
  # torch.cuda.manual_seed(seed)
  torch.cuda.manual_seed_all(seed)
  ...
  noise_bboxes_raw = torch.randn(
      (self.num_proposals, 4),
      device=device)
  ...
  ```
- `random seed` means do not hard set seed before generating random boxes.

### Training commands

In MMDetection's root directory, run the following command to train the model:

```bash
python tools/train.py projects/DiffusionDet/configs/diffusiondet_r50_fpn_500-proposals_1-step_crop-ms-480-800-450k_coco.py
```

For multi-gpu training, run:

```bash
python -m torch.distributed.launch --nnodes=1 --node_rank=0 --nproc_per_node=${NUM_GPUS} --master_port=29506 --master_addr="127.0.0.1" tools/train.py projects/DiffusionDet/configs/diffusiondet_r50_fpn_500-proposals_1-step_crop-ms-480-800-450k_coco.py
```

### Testing commands

In MMDetection's root directory, run the following command to test the model:

```bash
# for 1 step inference
# test command
python tools/test.py projects/DiffusionDet/configs/diffusiondet_r50_fpn_500-proposals_1-step_crop-ms-480-800-450k_coco.py ${CHECKPOINT_PATH}

# for 4 steps inference

# test command
python tools/test.py projects/DiffusionDet/configs/diffusiondet_r50_fpn_500-proposals_1-step_crop-ms-480-800-450k_coco.py ${CHECKPOINT_PATH} --cfg-options model.bbox_head.sampling_timesteps=4
```

**Note:** There is no difference between 1 step or 4 steps (or other multi-step) during training. Users can set different steps during inference through `--cfg-options model.bbox_head.sampling_timesteps=${STEPS}`, but larger `sampling_timesteps` will affect the inference time.

## Results

Here we provide the baseline version of DiffusionDet with ResNet50 backbone.

To find more variants, please visit the [official model zoo](https://github.com/ShoufaChen/DiffusionDet#models).

| Backbone |  Style  | Lr schd | AP (Step=1) | AP (Step=4) |                                           Config                                           |                                                                                                                                                                                                                                      Download                                                                                                                                                                                                                                      |
| :------: | :-----: | :-----: | :---------: | :---------: | :----------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
|   R-50   | PyTorch |  450k   |    44.5     |    46.2     | [config](./configs/diffusiondet_r50_fpn_500-proposals_1-step_crop-ms-480-800-450k_coco.py) | [model](https://download.openmmlab.com/mmdetection/v3.0/diffusiondet/diffusiondet_r50_fpn_500-proposals_1-step_crop-ms-480-800-450k_coco/diffusiondet_r50_fpn_500-proposals_1-step_crop-ms-480-800-450k_coco_20230215_090925-7d6ed504.pth) \| [log](https://download.openmmlab.com/mmdetection/v3.0/diffusiondet/diffusiondet_r50_fpn_500-proposals_1-step_crop-ms-480-800-450k_coco/diffusiondet_r50_fpn_500-proposals_1-step_crop-ms-480-800-450k_coco_20230215_090925.log.json) |

## License

DiffusionDet is under the [CC-BY-NC 4.0 license](https://github.com/ShoufaChen/DiffusionDet/blob/main/LICENSE). Users should be careful about adopting these features in any commercial matters.

## Citation

If you find DiffusionDet is useful in your research or applications, please consider giving a star 🌟 to the [official repository](https://github.com/ShoufaChen/DiffusionDet) and citing DiffusionDet by the following BibTeX entry.

```BibTeX
@article{chen2022diffusiondet,
      title={DiffusionDet: Diffusion Model for Object Detection},
      author={Chen, Shoufa and Sun, Peize and Song, Yibing and Luo, Ping},
      journal={arXiv preprint arXiv:2211.09788},
      year={2022}
}
```

## Checklist

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- [x] Milestone 1: PR-ready, and acceptable to be one of the `projects/`.

  - [x] Finish the code

    <!-- The code's design shall follow existing interfaces and convention. For example, each model component should be registered into `mmdet.registry.MODELS` and configurable via a config file. -->

  - [x] Basic docstrings & proper citation

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  - [x] A full README

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- [ ] Milestone 3: Good to be a part of our core package!

  - [ ] Type hints and docstrings

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  - [ ] Code polishing

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  - [ ] Metafile.yml

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