184 lines
3.5 KiB
Markdown
184 lines
3.5 KiB
Markdown
# RT-DETR Ultralytics usage
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**NOTE**: Ultralytics (https://docs.ultralytics.com/models/rtdetr) version.
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* [Convert model](#convert-model)
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* [Compile the lib](#compile-the-lib)
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* [Edit the config_infer_primary_rtdetr file](#edit-the-config_infer_primary_rtdetr-file)
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* [Edit the deepstream_app_config file](#edit-the-deepstream_app_config-file)
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* [Testing the model](#testing-the-model)
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##
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### Convert model
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#### 1. Download the Ultralytics repo and install the requirements
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```
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git clone https://github.com/ultralytics/ultralytics.git
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cd ultralytics
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pip3 install -r requirements.txt
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python3 setup.py install
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pip3 install onnx onnxsim onnxruntime
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```
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**NOTE**: It is recommended to use Python virtualenv.
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#### 2. Copy conversor
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Copy the `export_rtdetr_ultralytics.py` file from `DeepStream-Yolo/utils` directory to the `ultralytics` folder.
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#### 3. Download the model
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Download the `pt` file from [Ultralytics](https://github.com/ultralytics/assets/releases/) releases (example for RT-DETR-l)
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```
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wget https://github.com/ultralytics/assets/releases/download/v0.0.0/rtdetr-l.pt
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```
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**NOTE**: You can use your custom model.
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#### 4. Convert model
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Generate the ONNX model file (example for RT-DETR-l)
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```
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python3 export_rtdetr_ultralytics.py -w rtdetr-l.pt --dynamic
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```
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**NOTE**: To change the inference size (defaut: 640)
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```
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-s SIZE
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--size SIZE
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-s HEIGHT WIDTH
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--size HEIGHT WIDTH
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```
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Example for 1280
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```
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-s 1280
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```
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or
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```
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-s 1280 1280
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```
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**NOTE**: To simplify the ONNX model (DeepStream >= 6.0)
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```
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--simplify
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```
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**NOTE**: To use dynamic batch-size (DeepStream >= 6.1)
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```
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--dynamic
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```
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**NOTE**: To use static batch-size (example for batch-size = 4)
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```
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--batch 4
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```
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**NOTE**: If you are using the DeepStream 5.1, remove the `--dynamic` arg and use opset 12 or lower. The default opset is 16.
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```
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--opset 12
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```
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#### 5. Copy generated files
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Copy the generated ONNX model file and labels.txt file (if generated) to the `DeepStream-Yolo` folder.
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##
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### Compile the lib
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1. Open the `DeepStream-Yolo` folder and compile the lib
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2. Set the `CUDA_VER` according to your DeepStream version
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```
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export CUDA_VER=XY.Z
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```
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* x86 platform
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```
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DeepStream 7.0 / 6.4 = 12.2
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DeepStream 6.3 = 12.1
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DeepStream 6.2 = 11.8
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DeepStream 6.1.1 = 11.7
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DeepStream 6.1 = 11.6
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DeepStream 6.0.1 / 6.0 = 11.4
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DeepStream 5.1 = 11.1
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```
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* Jetson platform
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```
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DeepStream 7.0 / 6.4 = 12.2
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DeepStream 6.3 / 6.2 / 6.1.1 / 6.1 = 11.4
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DeepStream 6.0.1 / 6.0 / 5.1 = 10.2
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```
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3. Make the lib
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```
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make -C nvdsinfer_custom_impl_Yolo clean && make -C nvdsinfer_custom_impl_Yolo
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```
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##
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### Edit the config_infer_primary_rtdetr file
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Edit the `config_infer_primary_rtdetr.txt` file according to your model (example for RT-DETR-l with 80 classes)
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```
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[property]
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...
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onnx-file=rtdetr-l.onnx
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...
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num-detected-classes=80
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...
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parse-bbox-func-name=NvDsInferParseYolo
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...
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```
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**NOTE**: The **RT-DETR Ultralytics** do not resize the input with padding. To get better accuracy, use
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```
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[property]
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...
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maintain-aspect-ratio=0
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...
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```
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##
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### Edit the deepstream_app_config file
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```
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...
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[primary-gie]
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...
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config-file=config_infer_primary_rtdetr.txt
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```
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##
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### Testing the model
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```
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deepstream-app -c deepstream_app_config.txt
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```
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**NOTE**: The TensorRT engine file may take a very long time to generate (sometimes more than 10 minutes).
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**NOTE**: For more information about custom models configuration (`batch-size`, `network-mode`, etc), please check the [`docs/customModels.md`](customModels.md) file.
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