214 lines
4.4 KiB
Markdown
214 lines
4.4 KiB
Markdown
# YOLOX usage
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**NOTE**: You can use the main branch of the YOLOX repo to convert all model versions.
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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_yolox file](#edit-the-config_infer_primary_yolox-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 YOLOX repo and install the requirements
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```
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git clone https://github.com/Megvii-BaseDetection/YOLOX.git
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cd YOLOX
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pip3 install -r requirements.txt
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python3 setup.py develop
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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_yolox.py` file from `DeepStream-Yolo/utils` directory to the `YOLOX` folder.
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#### 3. Download the model
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Download the `pth` file from [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX/releases/) releases (example for YOLOX-s)
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```
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wget https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_s.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 YOLOX-s)
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```
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python3 export_yolox.py -w yolox_s.pth -c exps/default/yolox_s.py --dynamic
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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 implicit 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 11.
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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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Open the `DeepStream-Yolo` folder and compile the lib
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* DeepStream 6.3 on x86 platform
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```
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CUDA_VER=12.1 make -C nvdsinfer_custom_impl_Yolo
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```
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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 5.1 on x86 platform
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```
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CUDA_VER=11.1 make -C nvdsinfer_custom_impl_Yolo
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```
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* DeepStream 6.3 / 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 / 5.1 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_yolox file
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Edit the `config_infer_primary_yolox.txt` file according to your model (example for YOLOX-s with 80 classes)
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```
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[property]
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...
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onnx-file=yolox_s.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 the **legacy** model, you should edit the `config_infer_primary_yolox_legacy.txt` file.
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**NOTE**: The **YOLOX and YOLOX legacy** resize 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 **YOLOX** 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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**NOTE**: The **YOLOX legacy** uses normalization on the image preprocess. It is important to change the `net-scale-factor` and `offsets` according to the trained values.
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Default: `mean = 0.485, 0.456, 0.406` and `std = 0.229, 0.224, 0.225`
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```
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[property]
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...
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net-scale-factor=0.0173520735727919486
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offsets=123.675;116.28;103.53
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...
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```
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**NOTE**: By default, the dynamic batch-size is set. To use implicit batch-size, uncomment the line
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```
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[property]
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...
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force-implicit-batch-dim=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_yolox.txt
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```
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**NOTE**: If you are using the **legacy** model, you should edit it to `config_infer_primary_yolox_legacy.txt`.
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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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