init

.gitignore

+# output dir
+output
+output*
+instant_test_output
+inference_test_output
+
+
+*.json
+*.diff
+
+# compilation and distribution
+__pycache__
+_ext
+*.pyc
+*.pyd
+*.so
+detectron2.egg-info/
+build/
+dist/
+wheels/
+
+# pytorch/python/numpy formats
+*.pth
+*.pkl
+*.npy
+
+# ipython/jupyter notebooks
+*.ipynb
+**/.ipynb_checkpoints/
+
+# Editor temporaries
+*.swn
+*.swo
+*.swp
+*~
+
+# editor settings
+.idea
+.vscode
+_darcs
+
+# project dirs
+/detectron2/model_zoo/configs
+/datasets/*
+!/datasets/*.*
+/projects/*/datasets
+

GETTING_STARTED.md

+## Getting Started with DiffusionDet
+
+
+
+### Installation
+
+The codebases are built on top of [Detectron2](https://github.com/facebookresearch/detectron2), [Sparse R-CNN](https://github.com/PeizeSun/SparseR-CNN), and [denoising-diffusion-pytorch](https://github.com/lucidrains/denoising-diffusion-pytorch).
+Thanks very much.
+
+#### Requirements
+- Linux or macOS with Python ≥ 3.6
+- PyTorch ≥ 1.9.0 and [torchvision](https://github.com/pytorch/vision/) that matches the PyTorch installation.
+  You can install them together at [pytorch.org](https://pytorch.org) to make sure of this
+- OpenCV is optional and needed by demo and visualization
+
+#### Steps
+1. Install Detectron2 following https://github.com/facebookresearch/detectron2/blob/main/INSTALL.md#installation.
+
+2. Prepare datasets
+```
+mkdir -p datasets/coco
+mkdir -p datasets/lvis
+
+ln -s /path_to_coco_dataset/annotations datasets/coco/annotations
+ln -s /path_to_coco_dataset/train2017 datasets/coco/train2017
+ln -s /path_to_coco_dataset/val2017 datasets/coco/val2017
+
+ln -s /path_to_lvis_dataset/lvis_v1_train.json datasets/lvis/lvis_v1_train.json
+ln -s /path_to_lvis_dataset/lvis_v1_val.json datasets/lvis/lvis_v1_val.json
+```
+
+3. Prepare pretrain models
+
+DiffusionDet uses three backbones including ResNet-50, ResNet-101 and Swin-Base. The pretrained ResNet-50 model can be
+downloaded automatically by Detectron2. We also provide pretrained
+[ResNet-101](https://github.com/ShoufaChen/DiffusionDet/releases/download/v0.1/torchvision-R-101.pkl) and
+[Swin-Base](https://github.com/ShoufaChen/DiffusionDet/releases/download/v0.1/swin_base_patch4_window7_224_22k.pkl) which are compatible with
+Detectron2. Please download them to `DiffusionDet_ROOT/models/` before training:
+
+```bash
+mkdir models
+cd models
+# ResNet-101
+wget https://github.com/ShoufaChen/DiffusionDet/releases/download/v0.1/torchvision-R-101.pkl
+
+# Swin-Base
+wget https://github.com/ShoufaChen/DiffusionDet/releases/download/v0.1/swin_base_patch4_window7_224_22k.pkl
+
+cd ..
+```
+
+Thanks for model conversion scripts of [ResNet-101](https://github.com/PeizeSun/SparseR-CNN/blob/main/tools/convert-torchvision-to-d2.py)
+and [Swin-Base](https://github.com/facebookresearch/Detic/blob/main/tools/convert-thirdparty-pretrained-model-to-d2.py).
+
+4. Train DiffusionDet
+```
+python train_net.py --num-gpus 8 \
+    --config-file configs/diffdet.coco.res50.yaml
+```
+
+5. Evaluate DiffusionDet
+```
+python train_net.py --num-gpus 8 \
+    --config-file configs/diffdet.coco.res50.yaml \
+    --eval-only MODEL.WEIGHTS path/to/model.pth
+```
+
+* Evaluate with arbitrary number (e.g 300) of boxes by setting `MODEL.DiffusionDet.NUM_PROPOSALS 300`.
+* Evaluate with 4 refinement steps by setting `MODEL.DiffusionDet.SAMPLE_STEP 4`.
+
+
+We also provide the [pretrained model](https://github.com/ShoufaChen/DiffusionDet/releases/download/v0.1/diffdet_coco_res50_300boxes.pth)
+of [DiffusionDet-300boxes](configs/diffdet.coco.res50.300boxes.yaml) that is used for ablation study.
+
+
+### Inference Demo with Pre-trained Models
+We provide a command line tool to run a simple demo following [Detectron2](https://github.com/facebookresearch/detectron2/tree/main/demo#detectron2-demo).
+
+```bash
+python demo.py --config-file configs/diffdet.coco.res50.yaml \
+    --input image.jpg --opts MODEL.WEIGHTS diffdet_coco_res50.pth
+```
+
+We need to specify `MODEL.WEIGHTS` to a model from model zoo for evaluation.
+This command will run the inference and show visualizations in an OpenCV window.
+
+For details of the command line arguments, see `demo.py -h` or look at its source code
+to understand its behavior. Some common arguments are:
+* To run __on your webcam__, replace `--input files` with `--webcam`.
+* To run __on a video__, replace `--input files` with `--video-input video.mp4`.
+* To run __on cpu__, add `MODEL.DEVICE cpu` after `--opts`.
+* To save outputs to a directory (for images) or a file (for webcam or video), use `--output`.

LICENSE

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README.md

+# DiffusionDet
+## 论文
+`DiffusionDet: Diffusion Model for Object Detection`
+- https://arxiv.org/abs/2211.09788
+## 模型结构
+ 扩散模型在许多生成任务中取得了巨大成功，开始在感知任务如图像分割中进行探索。然而，据作者所知，尚无成功将其应用于目标检测的先例。
+ DiffusionDet是一种新框架，它将目标检测表述为从噪声框到目标框的去噪扩散过程。
+
+<div align=center>
+    <img src="./assets/teaser.png"/>
+</div>
+
+## 算法原理
+DiffusionDet框架如下图。(a) 图像编码器从输入图像中提取特征表示。检测解码器以带噪声的框为输入，预测类别分类和框坐标。
+(b) 检测解码器在一个检测头部有 6 个阶段，遵循了 DETR 和 Sparse R-CNN 的设计。此外，DiffusionDet 可以多次重用这个检测头部（包含 6 个阶段），这被称为“迭代评估”。
+<div align=center>
+    <img src="./assets/framework.png"/>
+</div>
+
+## 环境配置
+### Docker（方法一）
+此处提供[光源](https://www.sourcefind.cn/#/service-details)拉取docker镜像的地址与使用步骤，以及[光合](https://developer.hpccube.com/tool/)开发者社区深度学习库下载地址
+```
+docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.1.0-centos7.6-dtk24.04-py310 
+docker run -it --shm-size=128G -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal:/opt/hyhal:ro --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name diffusiondet_pytorch  <your IMAGE ID> bash # <your IMAGE ID>为以上拉取的docker的镜像ID替换，本镜像为：c85ed27005f2
+cd /path/your_code_data/diffusiondet_pytorch
+pip install mmcv-2.0.1_das1.0+gitc0ccf15.abi0.dtk2404.torch2.1.-cp310-cp310-manylinux2014_x86_64.whl
+pip install wheel -i https://mirrors.aliyun.com/pypi/simple/  --trusted-host mirrors.aliyun.com --no-deps
+pip install timm -i https://mirrors.aliyun.com/pypi/simple/  --trusted-host mirrors.aliyun.com
+git clone https://github.com/facebookresearch/detectron2.git
+cd detectron2
+pip install e . --no-build-isolation -i https://mirrors.aliyun.com/pypi/simple/  --trusted-host mirrors.aliyun.com
+
+```
+### Dockerfile（方法二）
+此处提供dockerfile的使用方法
+```
+docker build --no-cache -t diffusiondet:latest .
+docker run -it --shm-size=128G -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal:/opt/hyhal:ro --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name diffusiondet_pytorch  diffusiondet  bash
+cd /path/your_code_data/diffusiondet_pytorch
+pip install mmcv-2.0.1_das1.0+gitc0ccf15.abi0.dtk2404.torch2.1.-cp310-cp310-manylinux2014_x86_64.whl
+pip install wheel -i https://mirrors.aliyun.com/pypi/simple/  --trusted-host mirrors.aliyun.com --no-deps
+pip install timm -i https://mirrors.aliyun.com/pypi/simple/  --trusted-host mirrors.aliyun.com
+git clone https://github.com/facebookresearch/detectron2.git
+cd detectron2
+pip install e . --no-build-isolation -i https://mirrors.aliyun.com/pypi/simple/  --trusted-host mirrors.aliyun.com
+```
+### Anaconda（方法三）
+此处提供本地配置、编译的详细步骤，例如：
+
+关于本项目DCU显卡所需的特殊深度学习库可从[光合](https://developer.hpccube.com/tool/)开发者社区下载安装。
+```
+#DTK驱动：dtk24.04
+# python：python3.10
+# torch: 2.1.0
+# torchvision: 0.16.0
+conda create -n diffusiondet python=3.10
+conda activate diffusiondet
+pip install torch-2.1.0+das1.0+git00661e0.abi0.dtk2404-cp310-cp310-manylinux2014_x86_64.whl
+pip install torchvision-0.16.0+das1.0+gitc9e7141.abi0.dtk2404.torch2.1-cp310-cp310-manylinux2014_x86_64.whl
+pip install mmcv-2.0.1_das1.0+gitc0ccf15.abi0.dtk2404.torch2.1.-cp310-cp310-manylinux2014_x86_64.whl
+
+```
+`Tips：以上dtk驱动、python、torch等DCU相关工具版本需要严格一一对应`
+
+其它依赖环境安装如下：
+```
+cd /path/your_code_data/sed
+git clone https://github.com/facebookresearch/detectron2.git
+cd detectron2
+pip install e . --no-build-isolation -i https://mirrors.aliyun.com/pypi/simple/  --trusted-host mirrors.aliyun.com
+pip install timm -i https://mirrors.aliyun.com/pypi/simple/  --trusted-host mirrors.aliyun.com
+```
+## 数据集
+
+dataset数据结构如下:
+数据集SCNet快速下载链接
+
+[coco](http://113.200.138.88:18080/aidatasets/coco2017)
+
+[lvis](http://113.200.138.88:18080/aidatasets/lvis)
+
+```
+ ── dataset
+│   ├── coco
+│   │  ├── annotations
+│   │  ├── train2017
+│   │  └── val2017
+│   ├── lvis
+│   │  ├── lvis_v1_train.json
+│   │  └── lvis_v1_val.json
+```
+数据准备详情查看dataset/readme.md。
+
+## 训练
+首先下载模型文件:
+
+模型文件SCNet快速下载链接[pkl文件](http://113.200.138.88:18080/aimodels/diffusiondet_models)
+下载后放于/path/your_code_data/diffusiondet_pytorch/文件夹下
+```
+mkdir models
+cd models
+# ResNet-101
+wget https://github.com/ShoufaChen/DiffusionDet/releases/download/v0.1/torchvision-R-101.pkl
+
+# Swin-Base
+wget https://github.com/ShoufaChen/DiffusionDet/releases/download/v0.1/swin_base_patch4_window7_224_22k.pkl
+
+cd ..
+
+
+```
+
+### 单机单卡
+```
+python train_net.py --config-file configs/diffdet.coco.res50.yaml
+```
+### 单机多卡
+```
+python train_net.py --num-gpus 4 --config-file configs/diffdet.coco.res50.yaml
+```
+
+## 推理
+模型权重文件下载表格如下，放到weights文件夹下：
+
+注意：模型配置文件、clip文件与权重文件应一一对应
+
+### 单卡推理
+
+Inference Demo
+
+To save outputs to a directory , use --output
+```
+python demo.py --config-file configs/diffdet.coco.res50.yaml \
+    --input demo.jpg --opts MODEL.WEIGHTS diffdet_coco_res50.pth 
+```
+
+Evaluate DiffusionDet
+```
+python train_net.py \
+    --config-file configs/diffdet.coco.res50.yaml \
+    --eval-only MODEL.WEIGHTS path/to/model.pth
+```
+
+
+### 多卡推理
+
+```
+python train_net.py --num-gpus 4 \
+    --config-file configs/diffdet.coco.res50.yaml \
+    --eval-only MODEL.WEIGHTS path/to/model.pth
+    
+#Evaluate with arbitrary number (e.g 300) of boxes by setting MODEL.DiffusionDet.NUM_PROPOSALS 300.
+#Evaluate with 4 refinement steps by setting MODEL.DiffusionDet.SAMPLE_STEP 4.
+```
+
+## result
+Inference Demo
+
+<div align=center>
+    <img src="./assets/demo.jpg"/>
+</div>
+
+
+
+
+### 精度
+使用四张DCU-K100 AI卡推理
+
+|                                        Method                                        | Box AP (1 step) | Box AP (4 step) | 
+|:------------------------------------------------------------------------------------:|:---------------:|------|
+|                    [COCO-Res50](configs/diffdet.coco.res50.yaml)                     |      45.7       | 46.1 |
+|                   [COCO-Res101](configs/diffdet.coco.res101.yaml)                    |      46.6       | 46.9 |
+|                 [COCO-SwinBase](configs/diffdet.coco.swinbase.yaml)                  |      52.3       | 52.7 |
+|                    [LVIS-Res50](configs/diffdet.lvis.res50.yaml)                     |      30.4       | 31.8 |
+|                   [LVIS-Res101](configs/diffdet.lvis.res101.yaml)                    |      31.9       | 32.9 |
+|                  [LVIS-SwinBase](configs/diffdet.lvis.swinbase.yaml)                 |      40.6       | 41.9|
+
+
+
+## 应用场景
+### 算法类别
+`目标检测`
+### 热点应用行业
+`科研,制造,医疗,家居,教育`
+## 源码仓库及问题反馈
+- https://developer.hpccube.com/codes/modelzoo/diffusiondet-pytorch
+## 参考资料
+- https://github.com/ShoufaChen/DiffusionDet
+

README_ori.md

+## DiffusionDet: Diffusion Model for Object Detection
+
+**DiffusionDet is the first work of diffusion model for object detection.**
+
+![](teaser.png)
+
+
+> [**DiffusionDet: Diffusion Model for Object Detection**](https://arxiv.org/abs/2211.09788)               
+> [Shoufa Chen](https://www.shoufachen.com/), [Peize Sun](https://peizesun.github.io/), [Yibing Song](https://ybsong00.github.io/), [Ping Luo](http://luoping.me/)                 
+> *[arXiv 2211.09788](https://arxiv.org/abs/2211.09788)* 
+
+## Updates
+- (11/2022) Code is released.
+
+## Models
+Method | Box AP (1 step) | Box AP (4 step) | Download
+--- |:---:|:---:|:---:
+[COCO-Res50](configs/diffdet.coco.res50.yaml) | 45.5 | 46.1 | [model](https://github.com/ShoufaChen/DiffusionDet/releases/download/v0.1/diffdet_coco_res50.pth)
+[COCO-Res101](configs/diffdet.coco.res101.yaml) | 46.6 | 46.9 | [model](https://github.com/ShoufaChen/DiffusionDet/releases/download/v0.1/diffdet_coco_res101.pth)
+[COCO-SwinBase](configs/diffdet.coco.swinbase.yaml) | 52.3 | 52.7 | [model](https://github.com/ShoufaChen/DiffusionDet/releases/download/v0.1/diffdet_coco_swinbase.pth)
+[LVIS-Res50](configs/diffdet.lvis.res50.yaml) | 30.4 | 31.8 | [model](https://github.com/ShoufaChen/DiffusionDet/releases/download/v0.1/diffdet_lvis_res50.pth)
+[LVIS-Res101](configs/diffdet.lvis.res101.yaml) | 31.9 | 32.9 | [model](https://github.com/ShoufaChen/DiffusionDet/releases/download/v0.1/diffdet_lvis_res101.pth)
+[LVIS-SwinBase](configs/diffdet.lvis.swinbase.yaml) | 40.6 | 41.9 | [model](https://github.com/ShoufaChen/DiffusionDet/releases/download/v0.1/diffdet_lvis_swinbase.pth)
+
+
+## Getting Started
+
+The installation instruction and usage are in [Getting Started with DiffusionDet](GETTING_STARTED.md).
+
+
+## License
+
+This project is under the CC-BY-NC 4.0 license. See [LICENSE](LICENSE) for details.
+
+
+## Citing DiffusionDet
+
+If you use DiffusionDet in your research or wish to refer to the baseline results published here, please use 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}
+}
+```
\ No newline at end of file

configs/Base-DiffusionDet.yaml

+MODEL:
+  META_ARCHITECTURE: "DiffusionDet"
+  WEIGHTS: "detectron2://ImageNetPretrained/torchvision/R-50.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
+  BACKBONE:
+    NAME: "build_resnet_fpn_backbone"
+  RESNETS:
+    OUT_FEATURES: ["res2", "res3", "res4", "res5"]
+  FPN:
+    IN_FEATURES: ["res2", "res3", "res4", "res5"]
+  ROI_HEADS:
+    IN_FEATURES: ["p2", "p3", "p4", "p5"]
+  ROI_BOX_HEAD:
+    POOLER_TYPE: "ROIAlignV2"
+    POOLER_RESOLUTION: 7
+    POOLER_SAMPLING_RATIO: 2
+SOLVER:
+  IMS_PER_BATCH: 16
+  BASE_LR: 0.000025
+  STEPS: (210000, 250000)
+  MAX_ITER: 270000
+  WARMUP_FACTOR: 0.01
+  WARMUP_ITERS: 1000
+  WEIGHT_DECAY: 0.0001
+  OPTIMIZER: "ADAMW"
+  BACKBONE_MULTIPLIER: 1.0  # keep same with BASE_LR.
+  CLIP_GRADIENTS:
+    ENABLED: True
+    CLIP_TYPE: "full_model"
+    CLIP_VALUE: 1.0
+    NORM_TYPE: 2.0
+SEED: 40244023
+INPUT:
+  MIN_SIZE_TRAIN: (480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800)
+  CROP:
+    ENABLED: False
+    TYPE: "absolute_range"
+    SIZE: (384, 600)
+  FORMAT: "RGB"
+TEST:
+  EVAL_PERIOD: 7330
+DATALOADER:
+  FILTER_EMPTY_ANNOTATIONS: False
+  NUM_WORKERS: 4
+VERSION: 2

configs/diffdet.coco.res101.yaml

+_BASE_: "Base-DiffusionDet.yaml"
+MODEL:
+  WEIGHTS: "models/torchvision-R-101.pkl"
+  RESNETS:
+    DEPTH: 101
+    STRIDE_IN_1X1: False
+  DiffusionDet:
+    NUM_PROPOSALS: 500
+    NUM_CLASSES: 80
+DATASETS:
+  TRAIN: ("coco_2017_train",)
+  TEST:  ("coco_2017_val",)
+SOLVER:
+  STEPS: (350000, 420000)
+  MAX_ITER: 450000
+INPUT:
+  CROP:
+    ENABLED: True
+  FORMAT: "RGB"

configs/diffdet.coco.res50.300boxes.yaml

+_BASE_: "Base-DiffusionDet.yaml"
+MODEL:
+  WEIGHTS: "detectron2://ImageNetPretrained/torchvision/R-50.pkl"
+  RESNETS:
+    DEPTH: 50
+    STRIDE_IN_1X1: False
+  DiffusionDet:
+    NUM_PROPOSALS: 300
+    NUM_CLASSES: 80
+DATASETS:
+  TRAIN: ("coco_2017_train",)
+  TEST:  ("coco_2017_val",)
+SOLVER:
+  STEPS: (350000, 420000)
+  MAX_ITER: 450000
+INPUT:
+  CROP:
+    ENABLED: True
+  FORMAT: "RGB"

configs/diffdet.coco.res50.yaml

+_BASE_: "Base-DiffusionDet.yaml"
+MODEL:
+  WEIGHTS: "detectron2://ImageNetPretrained/torchvision/R-50.pkl"
+  RESNETS:
+    DEPTH: 50
+    STRIDE_IN_1X1: False
+  DiffusionDet:
+    NUM_PROPOSALS: 500
+    NUM_CLASSES: 80
+DATASETS:
+  TRAIN: ("coco_2017_train",)
+  TEST:  ("coco_2017_val",)
+SOLVER:
+  STEPS: (350000, 420000)
+  MAX_ITER: 450000
+INPUT:
+  CROP:
+    ENABLED: True
+  FORMAT: "RGB"

configs/diffdet.coco.swinbase.yaml

+_BASE_: "Base-DiffusionDet.yaml"
+MODEL:
+  WEIGHTS: "models/swin_base_patch4_window7_224_22k.pkl"
+  BACKBONE:
+    NAME: build_swintransformer_fpn_backbone
+  SWIN:
+    SIZE: B-22k
+  FPN:
+    IN_FEATURES: ["swin0", "swin1", "swin2", "swin3" ]
+  DiffusionDet:
+    NUM_PROPOSALS: 500
+    NUM_CLASSES: 80
+DATASETS:
+  TRAIN: ("coco_2017_train",)
+  TEST:  ("coco_2017_val",)
+SOLVER:
+  STEPS: (350000, 420000)
+  MAX_ITER: 450000
+INPUT:
+  CROP:
+    ENABLED: True
+  FORMAT: "RGB"

configs/diffdet.lvis.res101.yaml

+_BASE_: "Base-DiffusionDet.yaml"
+MODEL:
+  WEIGHTS: "models/torchvision-R-101.pkl"
+  RESNETS:
+    DEPTH: 101
+    STRIDE_IN_1X1: False
+  ROI_HEADS:
+    NUM_CLASSES: 1203  # LVIS
+  DiffusionDet:
+    NUM_PROPOSALS: 500
+    NUM_CLASSES: 1203  # LVIS
+    USE_FED_LOSS: True  # LVIS
+DATASETS:  # LVIS
+  TRAIN: ("lvis_v1_train",)
+  TEST: ("lvis_v1_val",)
+DATALOADER:
+  SAMPLER_TRAIN: "RepeatFactorTrainingSampler"
+  REPEAT_THRESHOLD: 0.001
+SOLVER:
+  STEPS: (210000, 250000)
+  MAX_ITER: 270000
+INPUT:
+  CROP:
+    ENABLED: True
+  FORMAT: "RGB"
+TEST:  # LVIS
+  EVAL_PERIOD: 0  # disable eval during train since long time

configs/diffdet.lvis.res50.yaml

+_BASE_: "Base-DiffusionDet.yaml"
+MODEL:
+  WEIGHTS: "detectron2://ImageNetPretrained/torchvision/R-50.pkl"
+  RESNETS:
+    DEPTH: 50
+    STRIDE_IN_1X1: False
+  ROI_HEADS:
+    NUM_CLASSES: 1203  # LVIS
+  DiffusionDet:
+    NUM_PROPOSALS: 500
+    NUM_CLASSES: 1203  # LVIS
+    USE_FED_LOSS: True  # LVIS
+DATASETS:  # LVIS
+  TRAIN: ("lvis_v1_train",)
+  TEST: ("lvis_v1_val",)
+DATALOADER:
+  SAMPLER_TRAIN: "RepeatFactorTrainingSampler"
+  REPEAT_THRESHOLD: 0.001
+SOLVER:
+  STEPS: (210000, 250000)
+  MAX_ITER: 270000
+INPUT:
+  CROP:
+    ENABLED: True
+  FORMAT: "RGB"
+TEST:  # LVIS
+  EVAL_PERIOD: 0  # disable eval during train since long time

configs/diffdet.lvis.swinbase.yaml

+_BASE_: "Base-DiffusionDet.yaml"
+MODEL:
+  WEIGHTS: "models/swin_base_patch4_window7_224_22k.pkl"
+  BACKBONE:
+    NAME: build_swintransformer_fpn_backbone
+  SWIN:
+    SIZE: B-22k
+  FPN:
+    IN_FEATURES: [ "swin0", "swin1", "swin2", "swin3" ]
+  ROI_HEADS:
+    NUM_CLASSES: 1203  # LVIS
+  DiffusionDet:
+    NUM_PROPOSALS: 500
+    NUM_CLASSES: 1203  # LVIS
+    USE_FED_LOSS: True  # LVIS
+DATASETS:  # LVIS
+  TRAIN: ("lvis_v1_train",)
+  TEST: ("lvis_v1_val",)
+DATALOADER:
+  SAMPLER_TRAIN: "RepeatFactorTrainingSampler"
+  REPEAT_THRESHOLD: 0.001
+SOLVER:
+  STEPS: (210000, 250000)
+  MAX_ITER: 270000
+INPUT:
+  CROP:
+    ENABLED: True
+  FORMAT: "RGB"
+TEST:  # LVIS
+  EVAL_PERIOD: 0  # disable eval during train since long time

demo.py

+# Copyright (c) Facebook, Inc. and its affiliates.
+import argparse
+import glob
+import multiprocessing as mp
+import numpy as np
+import os
+import tempfile
+import time
+import warnings
+import cv2
+import tqdm
+
+from detectron2.config import get_cfg
+from detectron2.data.detection_utils import read_image
+from detectron2.utils.logger import setup_logger
+
+from diffusiondet.predictor import VisualizationDemo
+from diffusiondet import DiffusionDetDatasetMapper, add_diffusiondet_config, DiffusionDetWithTTA
+from diffusiondet.util.model_ema import add_model_ema_configs, may_build_model_ema, may_get_ema_checkpointer, EMAHook, \
+    apply_model_ema_and_restore, EMADetectionCheckpointer
+
+# constants
+WINDOW_NAME = "COCO detections"
+
+
+def setup_cfg(args):
+    # load config from file and command-line arguments
+    cfg = get_cfg()
+    # To use demo for Panoptic-DeepLab, please uncomment the following two lines.
+    # from detectron2.projects.panoptic_deeplab import add_panoptic_deeplab_config  # noqa
+    # add_panoptic_deeplab_config(cfg)
+    add_diffusiondet_config(cfg)
+    add_model_ema_configs(cfg)
+    cfg.merge_from_file(args.config_file)
+    cfg.merge_from_list(args.opts)
+    # Set score_threshold for builtin models
+    cfg.MODEL.RETINANET.SCORE_THRESH_TEST = args.confidence_threshold
+    cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = args.confidence_threshold
+    cfg.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH = args.confidence_threshold
+    cfg.freeze()
+    return cfg
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(description="Detectron2 demo for builtin configs")
+    parser.add_argument(
+        "--config-file",
+        default="configs/quick_schedules/mask_rcnn_R_50_FPN_inference_acc_test.yaml",
+        metavar="FILE",
+        help="path to config file",
+    )
+    parser.add_argument("--webcam", action="store_true", help="Take inputs from webcam.")
+    parser.add_argument("--video-input", help="Path to video file.")
+    parser.add_argument(
+        "--input",
+        nargs="+",
+        help="A list of space separated input images; "
+        "or a single glob pattern such as 'directory/*.jpg'",
+    )
+    parser.add_argument(
+        "--output",
+        help="A file or directory to save output visualizations. "
+        "If not given, will show output in an OpenCV window.",
+    )
+
+    parser.add_argument(
+        "--confidence-threshold",
+        type=float,
+        default=0.5,
+        help="Minimum score for instance predictions to be shown",
+    )
+    parser.add_argument(
+        "--opts",
+        help="Modify config options using the command-line 'KEY VALUE' pairs",
+        default=[],
+        nargs=argparse.REMAINDER,
+    )
+    return parser
+
+
+def test_opencv_video_format(codec, file_ext):
+    with tempfile.TemporaryDirectory(prefix="video_format_test") as dir:
+        filename = os.path.join(dir, "test_file" + file_ext)
+        writer = cv2.VideoWriter(
+            filename=filename,
+            fourcc=cv2.VideoWriter_fourcc(*codec),
+            fps=float(30),
+            frameSize=(10, 10),
+            isColor=True,
+        )
+        [writer.write(np.zeros((10, 10, 3), np.uint8)) for _ in range(30)]
+        writer.release()
+        if os.path.isfile(filename):
+            return True
+        return False
+
+
+if __name__ == "__main__":
+    mp.set_start_method("spawn", force=True)
+    args = get_parser().parse_args()
+    setup_logger(name="fvcore")
+    logger = setup_logger()
+    logger.info("Arguments: " + str(args))
+
+    cfg = setup_cfg(args)
+
+    demo = VisualizationDemo(cfg)
+
+    if args.input:
+        if len(args.input) == 1:
+            args.input = glob.glob(os.path.expanduser(args.input[0]))
+            assert args.input, "The input path(s) was not found"
+        for path in tqdm.tqdm(args.input, disable=not args.output):
+            # use PIL, to be consistent with evaluation
+            img = read_image(path, format="BGR")
+            start_time = time.time()
+            predictions, visualized_output = demo.run_on_image(img)
+            logger.info(
+                "{}: {} in {:.2f}s".format(
+                    path,
+                    "detected {} instances".format(len(predictions["instances"]))
+                    if "instances" in predictions
+                    else "finished",
+                    time.time() - start_time,
+                )
+            )
+
+            if args.output:
+                if os.path.isdir(args.output):
+                    assert os.path.isdir(args.output), args.output
+                    out_filename = os.path.join(args.output, os.path.basename(path))
+                else:
+                    assert len(args.input) == 1, "Please specify a directory with args.output"
+                    out_filename = args.output
+                visualized_output.save(out_filename)
+            else:
+                cv2.namedWindow(WINDOW_NAME, cv2.WINDOW_NORMAL)
+                cv2.imshow(WINDOW_NAME, visualized_output.get_image()[:, :, ::-1])
+                if cv2.waitKey(0) == 27:
+                    break  # esc to quit
+    elif args.webcam:
+        assert args.input is None, "Cannot have both --input and --webcam!"
+        assert args.output is None, "output not yet supported with --webcam!"
+        cam = cv2.VideoCapture(0)
+        for vis in tqdm.tqdm(demo.run_on_video(cam)):
+            cv2.namedWindow(WINDOW_NAME, cv2.WINDOW_NORMAL)
+            cv2.imshow(WINDOW_NAME, vis)
+            if cv2.waitKey(1) == 27:
+                break  # esc to quit
+        cam.release()
+        cv2.destroyAllWindows()
+    elif args.video_input:
+        video = cv2.VideoCapture(args.video_input)
+        width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))
+        height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
+        frames_per_second = video.get(cv2.CAP_PROP_FPS)
+        num_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
+        basename = os.path.basename(args.video_input)
+        codec, file_ext = (
+            ("x264", ".mkv") if test_opencv_video_format("x264", ".mkv") else ("mp4v", ".mp4")
+        )
+        if codec == ".mp4v":
+            warnings.warn("x264 codec not available, switching to mp4v")
+        if args.output:
+            if os.path.isdir(args.output):
+                output_fname = os.path.join(args.output, basename)
+                output_fname = os.path.splitext(output_fname)[0] + file_ext
+            else:
+                output_fname = args.output
+            assert not os.path.isfile(output_fname), output_fname
+            output_file = cv2.VideoWriter(
+                filename=output_fname,
+                # some installation of opencv may not support x264 (due to its license),
+                # you can try other format (e.g. MPEG)
+                fourcc=cv2.VideoWriter_fourcc(*codec),
+                fps=float(frames_per_second),
+                frameSize=(width, height),
+                isColor=True,
+            )
+        assert os.path.isfile(args.video_input)
+        for vis_frame in tqdm.tqdm(demo.run_on_video(video), total=num_frames):
+            if args.output:
+                output_file.write(vis_frame)
+            else:
+                cv2.namedWindow(basename, cv2.WINDOW_NORMAL)
+                cv2.imshow(basename, vis_frame)
+                if cv2.waitKey(1) == 27:
+                    break  # esc to quit
+        video.release()
+        if args.output:
+            output_file.release()
+        else:
+            cv2.destroyAllWindows()

diffusiondet/__init__.py

+# ========================================
+# Modified by Shoufa Chen
+# ========================================
+# Modified by Peize Sun, Rufeng Zhang
+# Contact: {sunpeize, cxrfzhang}@foxmail.com
+#
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+from .config import add_diffusiondet_config
+from .detector import DiffusionDet
+from .dataset_mapper import DiffusionDetDatasetMapper
+from .test_time_augmentation import DiffusionDetWithTTA
+from .swintransformer import build_swintransformer_fpn_backbone

diffusiondet/config.py

+# ========================================
+# Modified by Shoufa Chen
+# ========================================
+# Modified by Peize Sun, Rufeng Zhang
+# Contact: {sunpeize, cxrfzhang}@foxmail.com
+#
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+from detectron2.config import CfgNode as CN
+
+
+def add_diffusiondet_config(cfg):
+    """
+    Add config for DiffusionDet
+    """
+    cfg.MODEL.DiffusionDet = CN()
+    cfg.MODEL.DiffusionDet.NUM_CLASSES = 80
+    cfg.MODEL.DiffusionDet.NUM_PROPOSALS = 300
+
+    # RCNN Head.
+    cfg.MODEL.DiffusionDet.NHEADS = 8
+    cfg.MODEL.DiffusionDet.DROPOUT = 0.0
+    cfg.MODEL.DiffusionDet.DIM_FEEDFORWARD = 2048
+    cfg.MODEL.DiffusionDet.ACTIVATION = 'relu'
+    cfg.MODEL.DiffusionDet.HIDDEN_DIM = 256
+    cfg.MODEL.DiffusionDet.NUM_CLS = 1
+    cfg.MODEL.DiffusionDet.NUM_REG = 3
+    cfg.MODEL.DiffusionDet.NUM_HEADS = 6
+
+    # Dynamic Conv.
+    cfg.MODEL.DiffusionDet.NUM_DYNAMIC = 2
+    cfg.MODEL.DiffusionDet.DIM_DYNAMIC = 64
+
+    # Loss.
+    cfg.MODEL.DiffusionDet.CLASS_WEIGHT = 2.0
+    cfg.MODEL.DiffusionDet.GIOU_WEIGHT = 2.0
+    cfg.MODEL.DiffusionDet.L1_WEIGHT = 5.0
+    cfg.MODEL.DiffusionDet.DEEP_SUPERVISION = True
+    cfg.MODEL.DiffusionDet.NO_OBJECT_WEIGHT = 0.1
+
+    # Focal Loss.
+    cfg.MODEL.DiffusionDet.USE_FOCAL = True
+    cfg.MODEL.DiffusionDet.USE_FED_LOSS = False
+    cfg.MODEL.DiffusionDet.ALPHA = 0.25
+    cfg.MODEL.DiffusionDet.GAMMA = 2.0
+    cfg.MODEL.DiffusionDet.PRIOR_PROB = 0.01
+
+    # Dynamic K
+    cfg.MODEL.DiffusionDet.OTA_K = 5
+
+    # Diffusion
+    cfg.MODEL.DiffusionDet.SNR_SCALE = 2.0
+    cfg.MODEL.DiffusionDet.SAMPLE_STEP = 1
+
+    # Inference
+    cfg.MODEL.DiffusionDet.USE_NMS = True
+
+    # Swin Backbones
+    cfg.MODEL.SWIN = CN()
+    cfg.MODEL.SWIN.SIZE = 'B'  # 'T', 'S', 'B'
+    cfg.MODEL.SWIN.USE_CHECKPOINT = False
+    cfg.MODEL.SWIN.OUT_FEATURES = (0, 1, 2, 3)  # modify
+
+    # Optimizer.
+    cfg.SOLVER.OPTIMIZER = "ADAMW"
+    cfg.SOLVER.BACKBONE_MULTIPLIER = 1.0
+
+    # TTA.
+    cfg.TEST.AUG.MIN_SIZES = (400, 500, 600, 640, 700, 900, 1000, 1100, 1200, 1300, 1400, 1800, 800)
+    cfg.TEST.AUG.CVPODS_TTA = True
+    cfg.TEST.AUG.SCALE_FILTER = True
+    cfg.TEST.AUG.SCALE_RANGES = ([96, 10000], [96, 10000], 
+                                 [64, 10000], [64, 10000],
+                                 [64, 10000], [0, 10000],
+                                 [0, 10000], [0, 256],
+                                 [0, 256], [0, 192],
+                                 [0, 192], [0, 96],
+                                 [0, 10000])