NOTE: https://github.com/lyuwenyu/RT-DETR/tree/main/rtdetr_paddle version.
- Convert model
- Compile the lib
- Edit the config_infer_primary_rtdetr file
- Edit the deepstream_app_config file
- Testing the model
https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.7/docs/tutorials/INSTALL.md
git clone https://github.com/lyuwenyu/RT-DETR.git
cd RT-DETR/rtdetr_paddle
pip3 install -r requirements.txt
pip3 install onnx onnxsim onnxruntime paddle2onnx
NOTE: It is recommended to use Python virtualenv.
Copy the export_rtdetr_paddle.py
file from DeepStream-Yolo/utils
directory to the RT-DETR/rtdetr_paddle
folder.
Download the pdparams
file from RT-DETR Paddle releases (example for RT-DETR-R50)
wget https://bj.bcebos.com/v1/paddledet/models/rtdetr_r50vd_6x_coco.pdparams
NOTE: You can use your custom model.
Generate the ONNX model file (example for RT-DETR-R50)
python3 export_rtdetr_paddle.py -w rtdetr_r50vd_6x_coco.pdparams -c configs/rtdetr/rtdetr_r50vd_6x_coco.yml --dynamic
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 16.
--opset 12
Copy the generated ONNX model file and labels.txt file (if generated) to the DeepStream-Yolo
folder.
-
Open the
DeepStream-Yolo
folder and compile the lib -
Set the
CUDA_VER
according to your DeepStream version
export CUDA_VER=XY.Z
-
x86 platform
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 7.0 / 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
- Make the lib
make -C nvdsinfer_custom_impl_Yolo clean && make -C nvdsinfer_custom_impl_Yolo
Edit the config_infer_primary_rtdetr.txt
file according to your model (example for RT-DETR-R50 with 80 classes)
[property]
...
onnx-file=rtdetr_r50vd_6x_coco.onnx
...
num-detected-classes=80
...
parse-bbox-func-name=NvDsInferParseYolo
...
NOTE: The RT-DETR do not resize the input with padding. To get better accuracy, use
[property]
...
maintain-aspect-ratio=0
...
...
[primary-gie]
...
config-file=config_infer_primary_rtdetr.txt
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.