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58 lines
3.4 KiB
Markdown
Executable File
58 lines
3.4 KiB
Markdown
Executable File
English | [简体中文](README_CN.md)
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# PaddleClas A311D Development Board C++ Deployment Example
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`infer.cc` in this directory can help you quickly complete the inference acceleration of PaddleClas 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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### Quantization 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 [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 inference_cls.yaml 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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For more information, please refer to [Model Quantization](../../quantize/README.md).
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## Deploying the Quantized ResNet50_Vd Segmentation model on A311D
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Please follow these steps to complete the deployment of the ResNet50_Vd 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/classification/paddleclas/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/classification/paddleclas/a311d/cpp/
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mkdir models && mkdir images
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wget https://bj.bcebos.com/paddlehub/fastdeploy/resnet50_vd_ptq.tar
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tar -xvf resnet50_vd_ptq.tar
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cp -r resnet50_vd_ptq models
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wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
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cp -r ILSVRC2012_val_00000010.jpeg 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/classification/paddleclas/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 ResNet50 segmentation model to A311D based on adb. You can run the following lines:
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```bash
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# Go to the install directory.
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cd FastDeploy/examples/vision/classification/paddleclas/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 resnet50_vd_ptq ILSVRC2012_val_00000010.jpeg $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/30516196/200767389-26519e50-9e4f-4fe1-8d52-260718f73476.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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