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[Doc] Update multi_thread docs in tutorials (#886)

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* Refactor PaddleSeg with preprocessor && postprocessor

* Fix bugs

* Delete redundancy code

* Modify by comments

* Refactor according to comments

* Add batch evaluation

* Add single test script

* Add ppliteseg single test script && fix eval(raise) error

* fix bug

* Fix evaluation segmentation.py batch predict

* Fix segmentation evaluation bug

* Fix evaluation segmentation bugs

* Update segmentation result docs

* Update old predict api and DisableNormalizeAndPermute

* Update resize segmentation label map with cv::INTER_NEAREST

* Add Model Clone function for PaddleClas && PaddleDet && PaddleSeg

* Add multi thread demo

* Add python model clone function

* Add multi thread python && C++ example

* Fix bug

* Update python && cpp multi_thread examples

* Add cpp && python directory

* Add README.md for examples

* Delete redundant code

* Create README_CN.md

* Rename README_CN.md to README.md

* Update README.md

* Update README.md

Co-authored-by: Jason <jiangjiajun@baidu.com>
This commit is contained in:
huangjianhui
2022-12-15 14:53:44 +08:00
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commit e4b1581593
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tutorials/multi_thread/cpp/README.md
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@@ -1,11 +1,11 @@
# PaddleClas C++部署示例
# PaddleClas C++多线程部署示例
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。
本目录下提供`multi_thread.cc`快速完成PaddleClas系列模型在CPU/GPU，以及GPU上通过TensorRT加速多线程部署的示例。
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)
- 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_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)
@@ -24,56 +24,14 @@ 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
./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 0
# GPU推理
./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 1
# GPU上TensorRT推理
./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 2
# CPU多线程推理
./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 0 1
# GPU多线程推理
./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 1 1
# GPU上TensorRT多线程推理
./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 2 1
```
>> **注意**: 最后一位数字表示线程数
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)
## PaddleClas C++接口
### PaddleClas类
```c++
fastdeploy::vision::classification::PaddleClasModel(
const string& model_file,
const string& params_file,
const string& config_file,
const RuntimeOption& runtime_option = RuntimeOption(),
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)
**参数**
> * **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
#### Predict函数
> ```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。
>
> **参数**
>
> > * **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
- [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)
- [如何在Windows中使用FastDeploy C++ SDK](../../../docs/cn/faq/use_sdk_on_windows.md)