172 lines
3.5 KiB
Markdown
172 lines
3.5 KiB
Markdown
# YOLOv7 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_yoloV7 file](#edit-the-config_infer_primary_yolov7-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 YOLOv7 repo and install the requirements
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```
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git clone https://github.com/WongKinYiu/yolov7.git
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cd yolov7
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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_yoloV7.py` file from `DeepStream-Yolo/utils` directory to the `yolov7` folder.
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#### 3. Download the model
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Download the `pt` file from [YOLOv7](https://github.com/WongKinYiu/yolov7/releases/) releases (example for YOLOv7)
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```
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wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt
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```
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**NOTE**: You can use your custom model.
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#### 4. Reparameterize your model
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[YOLOv7](https://github.com/WongKinYiu/yolov7/releases/) and its variants cannot be directly converted to engine file. Therefore, you will have to reparameterize your model using the code [here](https://github.com/WongKinYiu/yolov7/blob/main/tools/reparameterization.ipynb). Make sure to convert your custom checkpoints in yolov7 repository, and then save your reparmeterized checkpoints for conversion in the next step.
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#### 5. Convert model
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Generate the ONNX model file (example for YOLOv7)
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```
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python3 export_yoloV7.py -w yolov7.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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#### 6. 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_yoloV7 file
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Edit the `config_infer_primary_yoloV7.txt` file according to your model (example for YOLOv7 with 80 classes)
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```
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[property]
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...
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onnx-file=yolov7.onnx
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model-engine-file=yolov7.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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**NOTE**: The **YOLOv7** resizes the input with center padding. To get better accuracy, use
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```
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maintain-aspect-ratio=1
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symmetric-padding=1
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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_yoloV7.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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