[Doc]Add English version of documents in examples (#1070)

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This commit is contained in:
Hu Chuqi
2023-01-06 09:34:28 +08:00
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# ResNet C++部署示例
English | [简体中文](README_CN.md)
# ResNet C++ Deployment Example
本目录下提供`infer.cc`快速完成ResNet系列模型在CPU/GPU以及GPU上通过TensorRT加速部署的示例。
This directory provides examples that `infer.cc` fast finishes the deployment of ResNet models on CPU/GPU and GPU accelerated by TensorRT.
在部署前,需确认以下两个步骤
Before deployment, two steps require confirmation.
- 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)
- 2. 根据开发环境下载预编译部署库和samples代码参考[FastDeploy预编译库](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
- 2. Download the precompiled deployment library and samples code according to your development environment. Refer to [FastDeploy Precompiled Library](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
以Linux上 ResNet50 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0.7.0以上(x.x.x>=0.7.0)
Taking ResNet50 inference on Linux as an example, the compilation test can be completed by executing the following command in this directory. FastDeploy version 0.7.0 or above (x.x.x>=0.7.0) is required to support this model.
```bash
mkdir build
cd build
# 下载FastDeploy预编译库用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
# Download the FastDeploy precompiled library. Users can choose your appropriate version in the `FastDeploy Precompiled Library` mentioned above
wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
tar xvf fastdeploy-linux-x64-x.x.x.tgz
cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
make -j
# 下载ResNet模型文件和测试图片
# Download the ResNet50 model file and test images
wget https://bj.bcebos.com/paddlehub/fastdeploy/resnet50.onnx
wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
# CPU推理
# CPU inference
./infer_demo resnet50.onnx ILSVRC2012_val_00000010.jpeg 0
# GPU推理
# GPU inference
./infer_demo resnet50.onnx ILSVRC2012_val_00000010.jpeg 1
# GPU上TensorRT推理
# TensorRT Inference on GPU
./infer_demo resnet50.onnx ILSVRC2012_val_00000010.jpeg 2
```
以上命令只适用于LinuxMacOS, Windows下SDK的使用方式请参考:
- [如何在Windows中使用FastDeploy C++ SDK](../../../../../docs/cn/faq/use_sdk_on_windows.md)
The above command works for Linux or MacOS. Refer to:
- [How to use FastDeploy C++ SDK in Windows](../../../../../docs/cn/faq/use_sdk_on_windows.md) for SDK use-pattern in Windows
## ResNet C++接口
## ResNet C++ Interface
### ResNet
### ResNet Class
```c++
@@ -48,29 +49,29 @@ fastdeploy::vision::classification::ResNet(
```
**参数**
**Parameter**
> * **model_file**(str): 模型文件路径
> * **params_file**(str): 参数文件路径
> * **runtime_option**(RuntimeOption): 后端推理配置默认为None即采用默认配置
> * **model_format**(ModelFormat): 模型格式默认为ONNX格式
> * **model_file**(str): Model file path
> * **params_file**(str): Parameter file path
> * **runtime_option**(RuntimeOption): Backend inference configuration. None by default. (use the default configuration)
> * **model_format**(ModelFormat): Model format. ONNX format by default
#### Predict函数
#### Predict Function
> ```c++
> ResNet::Predict(cv::Mat* im, ClassifyResult* result, int topk = 1)
> ```
>
> 模型预测接口,输入图像直接输出检测结果。
> Model prediction interface. Input images and output results directly.
>
> **参数**
> **Parameter**
>
> > * **im**: 输入图像注意需为HWCBGR格式
> > * **result**: 分类结果包括label_id以及相应的置信度, ClassifyResult说明参考[视觉模型预测结果](../../../../../docs/api/vision_results/)
> > * **topk**(int):返回预测概率最高的topk个分类结果默认为1
> > * **im**: Input images in HWC or BGR format
> > * **result**: The classification result, including label_id, and the corresponding confidence. Refer to [Visual Model Prediction Results](../../../../../docs/api/vision_results/) for the description of ClassifyResult
> > * **topk**(int): Return the topk classification results with the highest prediction probability. Default 1
- [模型介绍](../../)
- [Python部署](../python)
- [视觉模型预测结果](../../../../../docs/api/vision_results/)
- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)
- [Model Description](../../)
- [Python Deployment](../python)
- [Vision Model prediction results](../../../../../docs/api/vision_results/)
- [How to switch the model inference backend engine](../../../../../docs/cn/faq/how_to_change_backend.md)

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[English](README.md) | 简体中文
# ResNet C++部署示例
本目录下提供`infer.cc`快速完成ResNet系列模型在CPU/GPU以及GPU上通过TensorRT加速部署的示例。
在部署前,需确认以下两个步骤
- 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)
- 2. 根据开发环境下载预编译部署库和samples代码参考[FastDeploy预编译库](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
以Linux上 ResNet50 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0.7.0以上(x.x.x>=0.7.0)
```bash
mkdir build
cd build
# 下载FastDeploy预编译库用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
tar xvf fastdeploy-linux-x64-x.x.x.tgz
cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
make -j
# 下载ResNet模型文件和测试图片
wget https://bj.bcebos.com/paddlehub/fastdeploy/resnet50.onnx
wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
# CPU推理
./infer_demo resnet50.onnx ILSVRC2012_val_00000010.jpeg 0
# GPU推理
./infer_demo resnet50.onnx ILSVRC2012_val_00000010.jpeg 1
# GPU上TensorRT推理
./infer_demo resnet50.onnx ILSVRC2012_val_00000010.jpeg 2
```
以上命令只适用于Linux或MacOS, Windows下SDK的使用方式请参考:
- [如何在Windows中使用FastDeploy C++ SDK](../../../../../docs/cn/faq/use_sdk_on_windows.md)
## ResNet C++接口
### ResNet类
```c++
fastdeploy::vision::classification::ResNet(
const std::string& model_file,
const std::string& params_file = "",
const RuntimeOption& custom_option = RuntimeOption(),
const ModelFormat& model_format = ModelFormat::ONNX)
```
**参数**
> * **model_file**(str): 模型文件路径
> * **params_file**(str): 参数文件路径
> * **runtime_option**(RuntimeOption): 后端推理配置默认为None即采用默认配置
> * **model_format**(ModelFormat): 模型格式默认为ONNX格式
#### Predict函数
> ```c++
> ResNet::Predict(cv::Mat* im, ClassifyResult* result, int topk = 1)
> ```
>
> 模型预测接口,输入图像直接输出检测结果。
>
> **参数**
>
> > * **im**: 输入图像注意需为HWCBGR格式
> > * **result**: 分类结果包括label_id以及相应的置信度, ClassifyResult说明参考[视觉模型预测结果](../../../../../docs/api/vision_results/)
> > * **topk**(int):返回预测概率最高的topk个分类结果默认为1
- [模型介绍](../../)
- [Python部署](../python)
- [视觉模型预测结果](../../../../../docs/api/vision_results/)
- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)