173 lines
4.2 KiB
Markdown
173 lines
4.2 KiB
Markdown
# PP-YOLOE / PP-YOLOE+ usage
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**NOTE**: You can use the release/2.6 branch of the PPYOLOE 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_ppyoloe_plus file](#edit-the-config_infer_primary_ppyoloe_plus-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 PaddleDetection repo and install the requirements
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https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.6/docs/tutorials/INSTALL.md
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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_ppyoloe.py` file from `DeepStream-Yolo/utils` directory to the `PaddleDetection` folder.
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#### 3. Download the model
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Download the `pdparams` file from [PP-YOLOE](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.6/configs/ppyoloe) releases (example for PP-YOLOE+_s)
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```
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wget https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_s_80e_coco.pdparams
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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 PP-YOLOE+_s)
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```
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pip3 install onnx onnxsim onnxruntime
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python3 export_ppyoloe.py -w ppyoloe_plus_crn_s_80e_coco.pdparams -c configs/ppyoloe/ppyoloe_plus_crn_s_80e_coco.yml --simplify
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```
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**NOTE**: If you are using DeepStream 5.1, 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 files
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Copy the generated ONNX model file and labels.txt file (if generated) 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 5.1 on x86 platform
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```
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CUDA_VER=11.1 LEGACY=1 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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* DeepStream 5.1 on Jetson platform
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```
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CUDA_VER=10.2 LEGACY=1 make -C nvdsinfer_custom_impl_Yolo
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```
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##
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### Edit the config_infer_primary_ppyoloe_plus file
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Edit the `config_infer_primary_ppyoloe_plus.txt` file according to your model (example for PP-YOLOE+_s with 80 classes)
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```
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[property]
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...
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onnx-file=ppyoloe_plus_crn_s_80e_coco.onnx
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model-engine-file=ppyoloe_plus_crn_s_80e_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**: If you use the **legacy** model, you should edit the `config_infer_primary_ppyoloe.txt` file.
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**NOTE**: The **PP-YOLOE+ and PP-YOLOE legacy** do not resize the input with padding. To get better accuracy, use
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```
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maintain-aspect-ratio=0
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```
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**NOTE**: The **PP-YOLOE+** 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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net-scale-factor=0.0039215697906911373
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```
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**NOTE**: The **PP-YOLOE 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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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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### 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_ppyoloe_plus.txt
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```
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**NOTE**: If you use the **legacy** model, you should edit it to `config_infer_primary_ppyoloe.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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