169 lines
2.9 KiB
Markdown
169 lines
2.9 KiB
Markdown
# YOLOR usage
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**NOTE**: Select the correct branch of the YOLOR repo before the conversion.
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**NOTE**: The cfg file is required for the main branch.
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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_yolor file](#edit-the-config_infer_primary_yolor-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 YOLOR repo and install the requirements
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```
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git clone https://github.com/WongKinYiu/yolor.git
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cd yolor
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pip3 install -r requirements.txt
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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_yolor.py` file from `DeepStream-Yolo/utils` directory to the `yolor` folder.
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#### 3. Download the model
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Download the `pt` file from [YOLOR](https://github.com/WongKinYiu/yolor) repo.
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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
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- Main branch
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Example for YOLOR-CSP
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```
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python3 export_yolor.py -w yolor_csp.pt -c cfg/yolor_csp.cfg --simplify
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```
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- Paper branch
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Example for YOLOR-P6
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```
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python3 export_yolor.py -w yolor-p6.pt --simplify
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```
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**NOTE**: To convert a P6 model
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```
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--p6
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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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#### 5. Copy generated files
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Copy the generated ONNX model file to the `DeepStream-Yolo` folder
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##
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### Compile the lib
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Open the `DeepStream-Yolo` folder and compile the lib
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* DeepStream 6.2 on x86 platform
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```
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CUDA_VER=11.8 make -C nvdsinfer_custom_impl_Yolo
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```
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* DeepStream 6.1.1 on x86 platform
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```
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CUDA_VER=11.7 make -C nvdsinfer_custom_impl_Yolo
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```
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* DeepStream 6.1 on x86 platform
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```
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CUDA_VER=11.6 make -C nvdsinfer_custom_impl_Yolo
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```
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* DeepStream 6.0.1 / 6.0 on x86 platform
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```
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CUDA_VER=11.4 make -C nvdsinfer_custom_impl_Yolo
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```
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* DeepStream 6.2 / 6.1.1 / 6.1 on Jetson platform
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```
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CUDA_VER=11.4 make -C nvdsinfer_custom_impl_Yolo
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```
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* DeepStream 6.0.1 / 6.0 on Jetson platform
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```
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CUDA_VER=10.2 make -C nvdsinfer_custom_impl_Yolo
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```
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##
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### Edit the config_infer_primary_yolor file
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Edit the `config_infer_primary_yolor.txt` file according to your model (example for YOLOR-CSP with 80 classes)
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```
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[property]
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...
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onnx-file=yolor_csp.onnx
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model-engine-file=yolor_csp.onnx_b1_gpu0_fp32.engine
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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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##
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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_yolor.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**: 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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