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[Doc]Add English version of documents in examples/ (#1042)
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charl-u
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English | [简体中文](README_CN.md)
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# Python推理
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在运行demo前，需确认以下两个步骤
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Before running demo, the following two steps need to be confirmed:
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- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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- 1. Hardware and software environment meets the requirements. Please refer to [Environment requirements for FastDeploy](../../../docs/en/build_and_install/download_prebuilt_libraries.md).
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- 2. Install FastDeploy Python whl package, please refer to [FastDeploy Python Installation](../../../docs/cn/build_and_install/download_prebuilt_libraries.md).
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本文档以 PaddleClas 分类模型 MobileNetV2 为例展示 CPU 上的推理示例
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This document shows an inference example on the CPU using the PaddleClas classification model MobileNetV2 as an example.
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## 1. 获取模型
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## 1. Obtaining the model
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``` python
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import fastdeploy as fd
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@@ -16,7 +17,7 @@ model_url = "https://bj.bcebos.com/fastdeploy/models/mobilenetv2.tgz"
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fd.download_and_decompress(model_url, path=".")
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```
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## 2. 配置后端
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## 2. Backend Configuration
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``` python
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option = fd.RuntimeOption()
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@@ -24,30 +25,30 @@ option = fd.RuntimeOption()
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option.set_model_path("mobilenetv2/inference.pdmodel",
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"mobilenetv2/inference.pdiparams")
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# **** CPU 配置 ****
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# **** CPU Configuration ****
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option.use_cpu()
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option.use_ort_backend()
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option.set_cpu_thread_num(12)
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# 初始化构造runtime
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# Initialise runtime
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runtime = fd.Runtime(option)
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# 获取模型输入名
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# Get model input name
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input_name = runtime.get_input_info(0).name
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# 构造随机数据进行推理
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# Constructing random data for inference
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results = runtime.infer({
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input_name: np.random.rand(1, 3, 224, 224).astype("float32")
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})
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print(results[0].shape)
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```
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加载完成，会输出提示如下，说明初始化的后端，以及运行的硬件设备
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When loading is complete, you will get the following output information indicating the initialized backend and the hardware devices.
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```
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[INFO] fastdeploy/fastdeploy_runtime.cc(283)::Init Runtime initialized with Backend::OrtBackend in device Device::CPU.
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```
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## 其它文档
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## Other Documents
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- [Runtime C++ 示例](../cpp)
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- [切换模型推理的硬件和后端](../../../docs/cn/faq/how_to_change_backend.md)
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- [A C++ example for Runtime C++](../cpp)
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- [Switching hardware and backend for model inference](../../../docs/en/faq/how_to_change_backend.md)
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54
examples/runtime/python/README_CN.md
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54
examples/runtime/python/README_CN.md
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@@ -0,0 +1,54 @@
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简体中文 ｜ [English](README.md)
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# Python推理
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在运行demo前，需确认以下两个步骤
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- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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本文档以 PaddleClas 分类模型 MobileNetV2 为例展示 CPU 上的推理示例
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## 1. 获取模型
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``` python
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import fastdeploy as fd
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model_url = "https://bj.bcebos.com/fastdeploy/models/mobilenetv2.tgz"
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fd.download_and_decompress(model_url, path=".")
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```
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## 2. 配置后端
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``` python
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option = fd.RuntimeOption()
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option.set_model_path("mobilenetv2/inference.pdmodel",
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"mobilenetv2/inference.pdiparams")
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# **** CPU 配置 ****
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option.use_cpu()
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option.use_ort_backend()
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option.set_cpu_thread_num(12)
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# 初始化构造runtime
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runtime = fd.Runtime(option)
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# 获取模型输入名
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input_name = runtime.get_input_info(0).name
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# 构造随机数据进行推理
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results = runtime.infer({
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input_name: np.random.rand(1, 3, 224, 224).astype("float32")
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})
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print(results[0].shape)
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```
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加载完成，会输出提示如下，说明初始化的后端，以及运行的硬件设备
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```
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[INFO] fastdeploy/fastdeploy_runtime.cc(283)::Init Runtime initialized with Backend::OrtBackend in device Device::CPU.
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```
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## 其它文档
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- [Runtime C++ 示例](../cpp)
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- [切换模型推理的硬件和后端](../../../docs/cn/faq/how_to_change_backend.md)
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