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61 lines
3.8 KiB
Markdown
Executable File
61 lines
3.8 KiB
Markdown
Executable File
English | [简体中文](README_CN.md)
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# PP-YOLOE Quantitative Model C++ Deployment Example
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`infer.cc` in this directory can help you quickly complete the inference acceleration of PP-YOLOE quantization model deployment on A311D.
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## Deployment Preparations
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### FastDeploy Cross-compile Environment Preparations
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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)
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### Model Preparations
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1. You can directly use the quantized model provided by FastDeploy for deployment.
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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).
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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.)
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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.
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For more information, please refer to [Model Quantization](../../quantize/README.md)
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## Deploying the Quantized PP-YOLOE Detection model on A311D
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Please follow these steps to complete the deployment of the PP-YOLOE quantization model on A311D.
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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)
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2. Copy the compiled library to the current directory. You can run this line:
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```bash
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cp -r FastDeploy/build/fastdeploy-timvx/ FastDeploy/examples/vision/detection/paddledetection/a311d/cpp
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```
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3. Download the model and example images required for deployment in current path.
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```bash
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cd FastDeploy/examples/vision/detection/paddledetection/a311d/cpp
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mkdir models && mkdir images
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wget https://bj.bcebos.com/fastdeploy/models/ppyoloe_noshare_qat.tar.gz
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tar -xvf ppyoloe_noshare_qat.tar.gz
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cp -r ppyoloe_noshare_qat models
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wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
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cp -r 000000014439.jpg images
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```
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4. Compile the deployment example. You can run the following lines:
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```bash
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cd FastDeploy/examples/vision/detection/paddledetection/a311d/cpp
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mkdir build && cd build
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cmake -DCMAKE_TOOLCHAIN_FILE=${PWD}/../fastdeploy-timvx/toolchain.cmake -DFASTDEPLOY_INSTALL_DIR=${PWD}/../fastdeploy-timvx -DTARGET_ABI=arm64 ..
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make -j8
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make install
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# After success, an install folder will be created with a running demo and libraries required for deployment.
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```
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5. Deploy the PP-YOLOE detection model to A311D based on adb.
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```bash
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# Go to the install directory.
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cd FastDeploy/examples/vision/detection/paddledetection/a311d/cpp/build/install/
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# The following line represents: bash run_with_adb.sh, demo needed to run, model path, image path, DEVICE ID.
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bash run_with_adb.sh infer_demo ppyoloe_noshare_qat 000000014439.jpg $DEVICE_ID
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```
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The output is:
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<img width="640" src="https://user-images.githubusercontent.com/30516196/203708564-43c49485-9b48-4eb2-8fe7-0fa517979fff.png">
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Please note that the model deployed on A311D needs to be quantized. You can refer to [Model Quantization](../../../../../../docs/en/quantize.md)
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