237 lines
4.2 KiB
Markdown
237 lines
4.2 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 onnxslim 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 --dynamic
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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**: Number of classes (example for 80 classes)
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```
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-n 80
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```
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or
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```
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--classes 80
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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 14.
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```
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--opset 12
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```
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#### 5. Copy generated file
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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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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 7.1 = 12.6
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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 7.1 = 12.6
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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_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.pth.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**: If you are using a **custom** model, you should edit the `config_infer_primary_yolonas_custom.txt` file.
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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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[property]
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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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**NOTE**: The **pre-trained YOLO-NAS** uses zero mean normalization on the image preprocess. It is important to change the `net-scale-factor` according to the trained values.
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```
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[property]
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...
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net-scale-factor=0.0039215697906911373
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...
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```
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**NOTE**: The **custom YOLO-NAS** uses no normalization on the image preprocess. It is important to change the `net-scale-factor` according to the trained values.
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```
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[property]
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...
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net-scale-factor=1
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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_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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