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45 lines
1.8 KiB
Markdown
45 lines
1.8 KiB
Markdown
# PaddleClas 昇腾 Python部署示例
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本目录下提供`infer.py`快速完成PaddleClas在昇腾AI处理器上部署的示例.
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## 1. 部署环境准备
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在部署前，需自行编译基于昇腾AI处理器的FastDeploy python wheel包并安装，参考文档，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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```bash
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# 安装FastDpeloy 昇腾预测库 python包（详细文档请参考`部署环境准备`）
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# 下载部署示例代码
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd FastDeploy/examples/vision/classification/paddleclas/ascend/python
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# 如果您希望从PaddleClas下载示例代码，请运行
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git clone https://github.com/PaddlePaddle/PaddleClas.git
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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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cd PaddleClas/deploy/fastdeploy/ascend/python
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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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# 在Ascend AI 处理器上推理
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python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --topk 1
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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 Python API查阅](https://www.paddlepaddle.org.cn/fastdeploy-api-doc/python/html/image_classification.html)
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- [FastDeploy部署PaddleClas模型概览](../../)
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- [PaddleClas C++ 部署](../cpp)
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