181 lines
3.2 KiB
Markdown
181 lines
3.2 KiB
Markdown
# YOLO-NAS usage
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**NOTE**: The yaml file is not required.
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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_yolonas file](#edit-the-config_infer_primary_yolonas-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 YOLO-NAS repo and install the requirements
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```
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git clone https://github.com/Deci-AI/super-gradients.git
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cd super-gradients
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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_yolonas.py` file from `DeepStream-Yolo/utils` directory to the `super-gradients` folder.
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#### 3. Download the model
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Download the `pth` file from [YOLO-NAS](https://sghub.deci.ai/) releases (example for YOLO-NAS S)
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```
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wget https://sghub.deci.ai/models/yolo_nas_s_coco.pth
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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 YOLO-NAS S)
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```
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python3 export_yolonas.py -m yolo_nas_s -w yolo_nas_s_coco.pth --simplify
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```
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**NOTE**: Model names
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```
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-m yolo_nas_s
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```
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or
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```
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-m yolo_nas_m
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```
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or
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```
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-m yolo_nas_l
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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_yolonas file
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Edit the `config_infer_primary_yolonas.txt` file according to your model (example for YOLO-NAS S with 80 classes)
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```
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[property]
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...
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onnx-file=yolo_nas_s_coco.onnx
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model-engine-file=yolo_nas_s_coco.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=NvDsInferParseYoloE
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...
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```
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**NOTE**: The **YOLO-NAS** resizes the input with left/top padding. To get better accuracy, use
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```
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maintain-aspect-ratio=1
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symmetric-padding=0
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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_yolonas.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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