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CO-DETR (MMDetection) usage

Convert model

1. Download the CO-DETR (MMDetection) repo and install the requirements

git clone https://github.com/open-mmlab/mmdetection.git
cd mmdetection
pip3 install openmim
mim install mmengine
mim install mmdeploy
mim install "mmcv>=2.0.0rc4,<2.2.0"
pip3 install -v -e .
pip3 install onnx onnxslim onnxruntime

NOTE: It is recommended to use Python virtualenv.

2. Copy conversor

Copy the export_codetr.py file from DeepStream-Yolo/utils directory to the mmdetection folder.

3. Download the model

Download the pth file from CO-DETR (MMDetection) releases (example for Co-DINO R50 DETR*)

wget https://download.openmmlab.com/mmdetection/v3.0/codetr/co_dino_5scale_r50_1x_coco-7481f903.pth

NOTE: You can use your custom model.

4. Convert model

Generate the ONNX model file (example for Co-DINO R50 DETR)

python3 export_codetr.py -w co_dino_5scale_r50_1x_coco-7481f903.pth -c projects/CO-DETR/configs/codino/co_dino_5scale_r50_8xb2_1x_coco.py --dynamic

NOTE: To change the inference size (defaut: 640)

-s SIZE
--size SIZE
-s HEIGHT WIDTH
--size HEIGHT WIDTH

Example for 1280

-s 1280

or

-s 1280 1280

NOTE: To simplify the ONNX model (DeepStream >= 6.0)

--simplify

NOTE: To use dynamic batch-size (DeepStream >= 6.1)

--dynamic

NOTE: To use static batch-size (example for batch-size = 4)

--batch 4

NOTE: If you are using the DeepStream 5.1, remove the --dynamic arg and use opset 12 or lower. The default opset is 11.

--opset 12

5. Copy generated files

Copy the generated ONNX model file and labels.txt file (if generated) to the DeepStream-Yolo folder.

Compile the lib

  1. Open the DeepStream-Yolo folder and compile the lib

  2. Set the CUDA_VER according to your DeepStream version

export CUDA_VER=XY.Z

  • x86 platform

    DeepStream 7.1 = 12.6
DeepStream 7.0 / 6.4 = 12.2
DeepStream 6.3 = 12.1
DeepStream 6.2 = 11.8
DeepStream 6.1.1 = 11.7
DeepStream 6.1 = 11.6
DeepStream 6.0.1 / 6.0 = 11.4
DeepStream 5.1 = 11.1

  • Jetson platform

    DeepStream 7.1 = 12.6
DeepStream 7.0 / 6.4 = 12.2
DeepStream 6.3 / 6.2 / 6.1.1 / 6.1 = 11.4
DeepStream 6.0.1 / 6.0 / 5.1 = 10.2
  1. Make the lib
make -C nvdsinfer_custom_impl_Yolo clean && make -C nvdsinfer_custom_impl_Yolo

Edit the config_infer_primary_codetr file

Edit the config_infer_primary_codetr.txt file according to your model (example for Co-DINO R50 DETR with 80 classes)

[property]
...
onnx-file=co_dino_5scale_r50_1x_coco-7481f903.pth.onnx
...
num-detected-classes=80
...
parse-bbox-func-name=NvDsInferParseYolo
...

NOTE: The CO-DETR (MMDetection) resizes the input with left/top padding. To get better accuracy, use

[property]
...
maintain-aspect-ratio=1
symmetric-padding=0
...

Edit the deepstream_app_config file

...
[primary-gie]
...
config-file=config_infer_primary_codetr.txt

Testing the model

deepstream-app -c deepstream_app_config.txt

NOTE: The TensorRT engine file may take a very long time to generate (sometimes more than 10 minutes).

NOTE: For more information about custom models configuration (batch-size, network-mode, etc), please check the docs/customModels.md file.