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Hu Chuqi
2023-01-06 09:34:28 +08:00
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74
examples/vision/classification/paddleclas/cpp/README.md
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# PaddleClas C++部署示例
English | [简体中文](README_CN.md)
# PaddleClas C++ Deployment Example
本目录下提供`infer.cc`快速完成PaddleClas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 PaddleClas 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_vd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_vd 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 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ResNet50_vd模型文件和测试图片
# Download ResNet50_vd model file and test images
wget https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz
tar -xvf ResNet50_vd_infer.tgz
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_vd_infer ILSVRC2012_val_00000010.jpeg 0
# GPU推理
# GPU inference
./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 1
# GPU上TensorRT推理
# TensorRT inference on GPU
./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 2
# IPU推理
# IPU inference
./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 3
# KunlunXin XPU推理
# KunlunXin XPU inference
./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 4
# Huawei Ascend NPU推理
./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 5
```
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)
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
如果用户使用华为昇腾NPU部署, 请参考以下方式在部署前初始化部署环境:
- [如何使用华为昇腾NPU部署](../../../../../docs/cn/faq/use_sdk_on_ascend.md)
## PaddleClas C++ Interface
## PaddleClas C++接口
### PaddleClas类
### PaddleClas Class
```c++
fastdeploy::vision::classification::PaddleClasModel(
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const ModelFormat& model_format = ModelFormat::PADDLE)
```
PaddleClas模型加载和初始化，其中model_file, params_file为训练模型导出的Paddle inference文件，具体请参考其文档说明[模型导出](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/inference_deployment/export_model.md#2-%E5%88%86%E7%B1%BB%E6%A8%A1%E5%9E%8B%E5%AF%BC%E5%87%BA)
PaddleClas model loading and initialization, where model_file and params_file are the Paddle inference files exported from the training model. Refer to [Model Export](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/inference_deployment/export_model.md#2-%E5%88%86%E7%B1%BB%E6%A8%A1%E5%9E%8B%E5%AF%BC%E5%87%BA) for more information
**参数**
**Parameter**
> * **model_file**(str): 模型文件路径
> * **params_file**(str): 参数文件路径
> * **config_file**(str): 推理部署配置文件
> * **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Paddle格式
> * **model_file**(str): Model file path
> * **params_file**(str): Parameter file path
> * **config_file**(str): Inference deployment configuration file
> * **runtime_option**(RuntimeOption): Backend inference configuration. None by default. (use the default configuration)
> * **model_format**(ModelFormat): Model format. Paddle format by default
#### Predict函数
#### Predict function
> ```c++
> PaddleClasModel::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HWC，BGR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)
- [Visual Model prediction results](../../../../../docs/api/vision_results/)
- [How to switch the model inference backend engine](../../../../../docs/cn/faq/how_to_change_backend.md)