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# PaddleSeg Python部署示例
English | [简体中文](README_CN.md)
# PaddleSeg Python Deployment Example
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. FastDeploy Python whl包安装,参考[FastDeploy Python安装](../../../../../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. Install FastDeploy Python whl package. Refer to [FastDeploy Python Installation](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
注意】如你部署的为**PP-Matting**、**PP-HumanMatting**以及**ModNet**请参考[Matting模型部署](../../../matting)
本目录下提供`infer.py`快速完成Unet在CPU/GPU以及GPU上通过TensorRT加速部署的示例。执行如下脚本即可完成
Attention】For the deployment of **PP-Matting**、**PP-HumanMatting** and **ModNet**, refer to [Matting Model Deployment](../../../matting)
This directory provides examples that `infer.py` fast finishes the deployment of Unet on CPU/GPU and GPU accelerated by TensorRT. The script is as follows
```bash
#下载部署示例代码
# Download the deployment example code
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd FastDeploy/examples/vision/segmentation/paddleseg/python
# 下载Unet模型文件和测试图片
# Download Unet model files and test images
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
# CPU inference
python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_demo.png --device cpu
# GPU推理
# GPU inference
python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_demo.png --device gpu
# GPU上使用TensorRT推理 注意TensorRT推理第一次运行有序列化模型的操作有一定耗时需要耐心等待
# TensorRT inference on GPUAttention: It is somewhat time-consuming for the operation of model serialization when running TensorRT inference for the first time. Please be patient.
python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_demo.png --device gpu --use_trt True
# 昆仑芯XPU推理
# kunlunxin XPU inference
python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_demo.png --device kunlunxin
# 华为昇腾推理
python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_demo.png --device ascend
```
运行完成可视化结果如下图所示
The visualized result after running is as follows
<div align="center">
<img src="https://user-images.githubusercontent.com/16222477/191712880-91ae128d-247a-43e0-b1e3-cafae78431e0.jpg", width=512px, height=256px />
</div>
## PaddleSegModel Python接口
## PaddleSegModel Python Interface
```python
fd.vision.segmentation.PaddleSegModel(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
```
PaddleSeg模型加载和初始化,其中model_file, params_file以及config_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/PaddleSeg/blob/release/2.6/docs/model_export_cn.md)
PaddleSeg model loading and initialization, among which model_file, params_file, and config_file are the Paddle inference files exported from the training model. Refer to [Model Export](https://github.com/PaddlePaddle/PaddleSeg/blob/release/2.6/docs/model_export_cn.md) 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, which is the default configuration
> * **model_format**(ModelFormat): Model format. Paddle format by default
### predict函数
### predict function
> ```python
> PaddleSegModel.predict(input_image)
> ```
>
> 模型预测结口,输入图像直接输出检测结果。
> Model prediction interface. Input images and output detection results.
>
> **参数**
> **Parameter**
>
> > * **input_image**(np.ndarray): 输入数据注意需为HWCBGR格式
> > * **input_image**(np.ndarray): Input data in HWC or BGR format
> **返回**
> **Return**
>
> > 返回`fastdeploy.vision.SegmentationResult`结构体,结构体说明参考文档[视觉模型预测结果](../../../../../docs/api/vision_results/)
> > Return `fastdeploy.vision.SegmentationResult` structure. Refer to [Vision Model Prediction Results](../../../../../docs/api/vision_results/) for the description of the structure.
### 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
### Class Member Variable
#### Pre-processing Parameter
Users can modify the following pre-processing parameters to their needs, which affects the final inference and deployment results
> > * **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
> > * **is_vertical_screen**(bool): For PP-HumanSeg models, the input image is portrait with height greater than width by setting this parameter to `true`
#### Post-processing Parameter
> > * **apply_softmax**(bool): The `apply_softmax` parameter is not specified when the model is exported. Set this parameter to `true` to normalize the probability result (score_map) of the predicted output segmentation label (label_map) in softmax
#### 后处理参数
> > * **apply_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
## Other Documents
## 其它文档
- [PaddleSeg 模型介绍](..)
- [PaddleSeg C++部署](../cpp)
- [模型预测结果说明](../../../../../docs/api/vision_results/)
- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)
- [PaddleSeg Model Description](..)
- [PaddleSeg C++ Deployment](../cpp)
- [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
# PaddleSeg Python部署示例
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. FastDeploy Python whl包安装参考[FastDeploy Python安装](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
【注意】如你部署的为**PP-Matting**、**PP-HumanMatting**以及**ModNet**请参考[Matting模型部署](../../../matting)
本目录下提供`infer.py`快速完成Unet在CPU/GPU以及GPU上通过TensorRT加速部署的示例。执行如下脚本即可完成
```bash
#下载部署示例代码
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd FastDeploy/examples/vision/segmentation/paddleseg/python
# 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
python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_demo.png --device cpu
# GPU推理
python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_demo.png --device gpu
# GPU上使用TensorRT推理 注意TensorRT推理第一次运行有序列化模型的操作有一定耗时需要耐心等待
python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_demo.png --device gpu --use_trt True
# 昆仑芯XPU推理
python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_demo.png --device kunlunxin
# 华为昇腾推理
python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_demo.png --device ascend
```
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>
## PaddleSegModel Python接口
```python
fd.vision.segmentation.PaddleSegModel(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
```
PaddleSeg模型加载和初始化其中model_file, params_file以及config_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/PaddleSeg/blob/release/2.6/docs/model_export_cn.md)
**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
> ```python
> PaddleSegModel.predict(input_image)
> ```
>
> 模型预测结口,输入图像直接输出检测结果。
>
> **参数**
>
> > * **input_image**(np.ndarray): 输入数据注意需为HWCBGR格式
> **返回**
>
> > 返回`fastdeploy.vision.SegmentationResult`结构体,结构体说明参考文档[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
> > * **apply_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
## 其它文档
- [PaddleSeg 模型介绍](..)
- [PaddleSeg C++部署](../cpp)
- [模型预测结果说明](../../../../../docs/api/vision_results/)
- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)