init

.gitignore

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+# 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

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+## Getting Started with Detectron2
+
+
+
+### 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).
+
+3. Train DiffusionDet
+```
+python train_net.py --num-gpus 8 \
+    --config-file configs/diffdet.coco.res50.yaml
+```
+
+4. 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.