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# PP-YOLOE Quantitative Model C++ Deployment Example

 `infer.cc` in this directory can help you quickly complete the inference acceleration of PP-YOLOE quantization model deployment on A311D.

## Deployment Preparations
### FastDeploy Cross-compile Environment Preparations
1. For the software and hardware environment, and the cross-compile environment, please refer to [FastDeploy Cross-compile environment](../../../../../../docs/en/build_and_install/a311d.md#Cross-compilation-environment-construction)  

### Model Preparations
1. You can directly use the quantized model provided by FastDeploy for deployment.
2. You can use PaddleDetection to export Float32 models, note that you need to set the parameter when exporting model: use_shared_conv=False. For more information: [PP-YOLOE](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyoloe).
3. You can use [one-click automatical compression tool](../../../../../../tools/common_tools/auto_compression/) provided by FastDeploy to quantize model by yourself, and use the generated quantized model for deployment.(Note: The quantized classification model still needs the infer_cfg.yml file in the FP32 model folder. Self-quantized model folder does not contain this yaml file, you can copy it from the FP32 model folder to the quantized model folder.)
4. The model requires heterogeneous computation. Please refer to: [Heterogeneous Computation](./../../../../../../docs/en/faq/heterogeneous_computing_on_timvx_npu.md). Since the model is already provided, you can test the heterogeneous file we provide first to verify whether the accuracy meets the requirements.

For more information, please refer to [Model Quantization](../../quantize/README.md)

## Deploying the Quantized PP-YOLOE Detection model on A311D
Please follow these steps to complete the deployment of the PP-YOLOE quantization model on A311D.
1. Cross-compile the FastDeploy library as described in [Cross-compile  FastDeploy](../../../../../../docs/en/build_and_install/a311d.md#FastDeploy-cross-compilation-library-compilation-based-on-Paddle-Lite)

2. Copy the compiled library to the current directory. You can run this line:
```bash
cp -r FastDeploy/build/fastdeploy-timvx/ FastDeploy/examples/vision/detection/paddledetection/a311d/cpp
```

3. Download the model and example images required for deployment in current path.
```bash
cd FastDeploy/examples/vision/detection/paddledetection/a311d/cpp
mkdir models && mkdir images
wget https://bj.bcebos.com/fastdeploy/models/ppyoloe_noshare_qat.tar.gz
tar -xvf ppyoloe_noshare_qat.tar.gz
cp -r ppyoloe_noshare_qat models
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
cp -r 000000014439.jpg images
```

4. Compile the deployment example. You can run the following lines:
```bash
cd FastDeploy/examples/vision/detection/paddledetection/a311d/cpp
mkdir build && cd build
cmake -DCMAKE_TOOLCHAIN_FILE=${PWD}/../fastdeploy-timvx/toolchain.cmake -DFASTDEPLOY_INSTALL_DIR=${PWD}/../fastdeploy-timvx -DTARGET_ABI=arm64 ..
make -j8
make install
# After success, an install folder will be created with a running demo and libraries required for deployment.
```

5. Deploy the PP-YOLOE detection model to A311D based on adb.
```bash
# Go to the install directory.
cd FastDeploy/examples/vision/detection/paddledetection/a311d/cpp/build/install/
# The following line represents: bash run_with_adb.sh, demo needed to run, model path, image path, DEVICE ID.
bash run_with_adb.sh infer_demo ppyoloe_noshare_qat 000000014439.jpg $DEVICE_ID
```

The output is:

<img width="640" src="https://user-images.githubusercontent.com/30516196/203708564-43c49485-9b48-4eb2-8fe7-0fa517979fff.png">

Please note that the model deployed on A311D needs to be quantized. You can refer to [Model Quantization](../../../../../../docs/en/quantize.md)