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examples/vision/classification/paddleclas/a311d/cpp/README_CN.md

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+[English](README.md) | 简体中文
+# PaddleClas A311D 开发板 C++ 部署示例
+本目录下提供的 `infer.cc`，可以帮助用户快速完成 PaddleClas 量化模型在 A311D 上的部署推理加速。
+
+## 部署准备
+### FastDeploy 交叉编译环境准备
+1. 软硬件环境满足要求，以及交叉编译环境的准备，请参考：[FastDeploy 交叉编译环境准备](../../../../../../docs/cn/build_and_install/a311d.md#交叉编译环境搭建)  
+
+### 量化模型准备
+1. 用户可以直接使用由 FastDeploy 提供的量化模型进行部署。
+2. 用户可以使用 FastDeploy 提供的[一键模型自动化压缩工具](../../../../../../tools/common_tools/auto_compression/)，自行进行模型量化, 并使用产出的量化模型进行部署。(注意: 推理量化后的分类模型仍然需要FP32模型文件夹下的inference_cls.yaml文件, 自行量化的模型文件夹内不包含此 yaml 文件, 用户从 FP32 模型文件夹下复制此 yaml 文件到量化后的模型文件夹内即可.)
+
+更多量化相关相关信息可查阅[模型量化](../../quantize/README.md)
+
+## 在 A311D 上部署量化后的 ResNet50_Vd 分类模型
+请按照以下步骤完成在 A311D 上部署 ResNet50_Vd 量化模型：
+1. 交叉编译编译 FastDeploy 库，具体请参考：[交叉编译 FastDeploy](../../../../../../docs/cn/build_and_install/a311d.md#基于-paddlelite-的-fastdeploy-交叉编译库编译)
+
+2. 将编译后的库拷贝到当前目录，可使用如下命令：
+```bash
+cp -r FastDeploy/build/fastdeploy-timvx/ FastDeploy/examples/vision/classification/paddleclas/a311d/cpp/
+```
+
+3. 在当前路径下载部署所需的模型和示例图片：
+```bash
+cd FastDeploy/examples/vision/classification/paddleclas/a311d/cpp/
+mkdir models && mkdir images
+wget https://bj.bcebos.com/paddlehub/fastdeploy/resnet50_vd_ptq.tar
+tar -xvf resnet50_vd_ptq.tar
+cp -r resnet50_vd_ptq models
+wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
+cp -r ILSVRC2012_val_00000010.jpeg images
+```
+
+4. 编译部署示例，可使入如下命令：
+```bash
+cd FastDeploy/examples/vision/classification/paddleclas/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
+# 成功编译之后，会生成 install 文件夹，里面有一个运行 demo 和部署所需的库
+```
+
+5. 基于 adb 工具部署 ResNet50 分类模型到晶晨 A311D，可使用如下命令：
+```bash
+# 进入 install 目录
+cd FastDeploy/examples/vision/classification/paddleclas/a311d/cpp/build/install/
+# 如下命令表示：bash run_with_adb.sh 需要运行的demo 模型路径 图片路径 设备的DEVICE_ID
+bash run_with_adb.sh infer_demo resnet50_vd_ptq ILSVRC2012_val_00000010.jpeg $DEVICE_ID
+```
+
+部署成功后运行结果如下：
+
+<img width="640" src="https://user-images.githubusercontent.com/30516196/200767389-26519e50-9e4f-4fe1-8d52-260718f73476.png">
+
+需要特别注意的是，在 A311D 上部署的模型需要是量化后的模型，模型的量化请参考：[模型量化](../../../../../../docs/cn/quantize.md)