2023-03-15 21:12:51 +08:00
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# PaddleClas 昇腾 C++部署示例
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本目录下提供`infer.cc`, 供用户完成PaddleClas模型在昇腾AI处理器上的部署.
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## 1. 部署环境准备
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在部署前，需确认以下两个步骤
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- 1. 在部署前，需自行编译基于昇腾AI处理器的预测库，参考文档[昇腾AI处理器部署环境编译](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install#自行编译安装)
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- 2. 部署时需要环境初始化, 请参考[如何使用C++在昇腾AI处理器部署](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/faq/use_sdk_on_ascend.md)
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## 2. 部署模型准备
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在部署前, 请准备好您所需要运行的推理模型, 您可以在[FastDeploy支持的PaddleClas模型列表](../README.md)中下载所需模型.
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## 3. 运行部署示例
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以Linux上推理为例，在本目录执行如下命令即可完成编译测试，支持此模型需保证FastDeploy版本1.0.0以上(x.x.x>=1.0.0)
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```bash
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# 下载部署示例代码
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2023-03-20 15:05:44 +08:00
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd FastDeploy/examples/vision/classification/paddleclas/ascend/cpp
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# 如果您希望从PaddleClas下载示例代码，请运行
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2023-03-15 21:12:51 +08:00
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git clone https://github.com/PaddlePaddle/PaddleClas.git
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2023-03-20 15:05:44 +08:00
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# 注意：如果当前分支找不到下面的fastdeploy测试代码，请切换到develop分支
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git checkout develop
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2023-03-15 21:12:51 +08:00
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cd PaddleClas/deploy/fastdeploy/ascend/cpp
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mkdir build
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cd build
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# 使用编译完成的FastDeploy库编译infer_demo
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cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-ascend
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make -j
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# 下载ResNet50_vd模型文件和测试图片
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wget https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz
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tar -xvf ResNet50_vd_infer.tgz
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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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# 使用昇腾部署
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./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg
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```
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运行完成后返回结果如下所示
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```bash
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ClassifyResult(
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label_ids: 153,
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scores: 0.686229,
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)
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```
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## 4. 更多指南
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- [PaddleClas系列 C++ API查阅](https://www.paddlepaddle.org.cn/fastdeploy-api-doc/cpp/html/namespacefastdeploy_1_1vision_1_1classification.html)
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- [FastDeploy部署PaddleClas模型概览](../../)
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- [PaddleClas Python部署](../python)
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