mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-06 00:57:33 +08:00
625845c7d6
* Update ppseg backend support type * Update ppseg preprocess if condition * Update README.md * Update README.md * Update README.md * Update ppseg with eigen functions * Delete old argmax function * Update README.md * Delete apply_softmax in ppseg example demo * Update ppseg code with createFromTensor function * Delete FDTensor2CVMat function * Update README.md * Update README.md * Update README.md * Update README.md * Update ppseg model.cc with transpose&&softmax in place convert * Update segmentation_result.md * Update model.cc * Update README.md * Update README.md Co-authored-by: Jason <jiangjiajun@baidu.com>
92 lines
3.6 KiB
Markdown
92 lines
3.6 KiB
Markdown
# PaddleSeg C++部署示例
|
||
|
||
本目录下提供`infer.cc`快速完成Unet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/environment.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/quick_start)
|
||
|
||
以Linux上推理为例，在本目录执行如下命令即可完成编译测试
|
||
|
||
```bash
|
||
wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-gpu-0.2.1.tgz
|
||
tar xvf fastdeploy-linux-x64-gpu-0.2.1.tgz
|
||
cd fastdeploy-linux-x64-gpu-0.2.1/examples/vision/segmentation/paddleseg/cpp/
|
||
mkdir build
|
||
cd build
|
||
cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/../../../../../../../fastdeploy-linux-x64-gpu-0.2.1
|
||
make -j
|
||
|
||
# 下载Unet模型文件和测试图片
|
||
wget https://bj.bcebos.com/paddlehub/fastdeploy/Unet_cityscapes_without_argmax_infer.tgz
|
||
tar -xvf Unet_cityscapes_without_argmax_infer.tgz
|
||
wget https://paddleseg.bj.bcebos.com/dygraph/demo/cityscapes_demo.png
|
||
|
||
|
||
# CPU推理
|
||
./infer_demo Unet_cityscapes_without_argmax_infer cityscapes_demo.png 0
|
||
# GPU推理
|
||
./infer_demo Unet_cityscapes_without_argmax_infer cityscapes_demo.png 1
|
||
# GPU上TensorRT推理
|
||
./infer_demo Unet_cityscapes_without_argmax_infer cityscapes_demo.png 2
|
||
```
|
||
|
||
运行完成可视化结果如下图所示
|
||
<div align="center">
|
||
<img src="https://user-images.githubusercontent.com/16222477/191712880-91ae128d-247a-43e0-b1e3-cafae78431e0.jpg", width=512px, height=256px />
|
||
</div>
|
||
|
||
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/compile/how_to_use_sdk_on_windows.md)
|
||
|
||
## PaddleSeg C++接口
|
||
|
||
### PaddleSeg类
|
||
|
||
```c++
|
||
fastdeploy::vision::segmentation::PaddleSegModel(
|
||
const string& model_file,
|
||
const string& params_file = "",
|
||
const string& config_file,
|
||
const RuntimeOption& runtime_option = RuntimeOption(),
|
||
const ModelFormat& model_format = ModelFormat::PADDLE)
|
||
```
|
||
|
||
PaddleSegModel模型加载和初始化，其中model_file为导出的Paddle模型格式。
|
||
|
||
**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++
|
||
> PaddleSegModel::Predict(cv::Mat* im, DetectionResult* result)
|
||
> ```
|
||
>
|
||
> 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**: 分割结果，包括分割预测的标签以及标签对应的概率值, SegmentationResult说明参考[视觉模型预测结果](../../../../../docs/api/vision_results/)
|
||
|
||
### 类成员属性
|
||
#### 预处理参数
|
||
用户可按照自己的实际需求，修改下列预处理参数，从而影响最终的推理和部署效果
|
||
|
||
> > * **is_vertical_screen**(bool): PP-HumanSeg系列模型通过设置此参数为`true`表明输入图片是竖屏，即height大于width的图片
|
||
|
||
#### 后处理参数
|
||
> > * **appy_softmax**(bool): 当模型导出时，并未指定`apply_softmax`参数，可通过此设置此参数为`true`，将预测的输出分割标签（label_map）对应的概率结果(score_map)做softmax归一化处理
|
||
|
||
- [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/runtime/how_to_change_backend.md)
|