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examples/vision/segmentation/paddleseg/ascend/python/README.md
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English | [简体中文](README_CN.md)
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# PaddleSeg Python Deployment Example
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Before deployment, two steps require confirmation
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- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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- 2. Install FastDeploy Python whl package. Refer to [FastDeploy Python Installation](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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【Attention】For the deployment of **PP-Matting**、**PP-HumanMatting** and **ModNet**, refer to [Matting Model Deployment](../../../matting)
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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
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```bash
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# Download the deployment example code
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd FastDeploy/examples/vision/segmentation/paddleseg/python
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# Download Unet model files and test images
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wget https://bj.bcebos.com/paddlehub/fastdeploy/Unet_cityscapes_without_argmax_infer.tgz
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tar -xvf Unet_cityscapes_without_argmax_infer.tgz
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wget https://paddleseg.bj.bcebos.com/dygraph/demo/cityscapes_demo.png
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# CPU inference
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python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_demo.png --device cpu
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# GPU inference
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python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_demo.png --device gpu
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# TensorRT inference on GPU（Attention: It is somewhat time-consuming for the operation of model serialization when running TensorRT inference for the first time. Please be patient.）
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python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_demo.png --device gpu --use_trt True
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# kunlunxin XPU inference
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python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_demo.png --device kunlunxin
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```
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The visualized result after running is as follows
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<div align="center">
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<img src="https://user-images.githubusercontent.com/16222477/191712880-91ae128d-247a-43e0-b1e3-cafae78431e0.jpg", width=512px, height=256px />
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</div>
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## PaddleSegModel Python Interface
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```python
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fd.vision.segmentation.PaddleSegModel(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
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```
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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/develop/docs/model_export_cn.md) for more information
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**Parameter**
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> * **model_file**(str): Model file path
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> * **params_file**(str): Parameter file path
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> * **config_file**(str): Inference deployment configuration file
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> * **runtime_option**(RuntimeOption): Backend inference configuration. None by default, which is the default configuration
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> * **model_format**(ModelFormat): Model format. Paddle format by default
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### predict function
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> ```python
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> PaddleSegModel.predict(input_image)
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> ```
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>
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> Model prediction interface. Input images and output detection results.
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>
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> **Parameter**
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>
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> > * **input_image**(np.ndarray): Input data in HWC or BGR format
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> **Return**
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>
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> > Return `fastdeploy.vision.SegmentationResult` structure. Refer to [Vision Model Prediction Results](../../../../../docs/api/vision_results/) for the description of the structure.
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### Class Member Variable
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#### Pre-processing Parameter
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Users can modify the following pre-processing parameters to their needs, which affects the final inference and deployment results
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> > * **is_vertical_screen**(bool): For PP-HumanSeg models, the input image is portrait with height greater than width by setting this parameter to `true`
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#### Post-processing Parameter
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> > * **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
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## Other Documents
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- [PaddleSeg Model Description](..)
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- [PaddleSeg C++ Deployment](../cpp)
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- [Model Prediction Results](../../../../../docs/api/vision_results/)
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- [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
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# PaddleSeg Python部署示例
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本目录下提供`infer.py`快速完成PP-LiteSeg在华为昇腾上部署的示例。
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在部署前，需自行编译基于华为昇腾NPU的FastDeploy python wheel包，参考文档[华为昇腾NPU部署环境编译](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/huawei_ascend.md)，编译python wheel包并安装
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>>**注意** **PP-Matting**、**PP-HumanMatting**的模型，请从[Matting模型部署](../../../matting)下载
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```bash
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#下载部署示例代码
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd FastDeploy/examples/vision/segmentation/paddleseg/ascend/cpp
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# 下载PP-LiteSeg模型文件和测试图片
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wget https://bj.bcebos.com/paddlehub/fastdeploy/PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer.tgz
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tar -xvf PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer.tgz
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wget https://paddleseg.bj.bcebos.com/dygraph/demo/cityscapes_demo.png
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# 华为昇腾推理
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python infer.py --model PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer --image cityscapes_demo.png
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```
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运行完成可视化结果如下图所示
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<div align="center">
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<img src="https://user-images.githubusercontent.com/16222477/191712880-91ae128d-247a-43e0-b1e3-cafae78431e0.jpg", width=512px, height=256px />
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</div>
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## PaddleSegModel Python接口
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```python
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fd.vision.segmentation.PaddleSegModel(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
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```
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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/develop/docs/model_export_cn.md)
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**参数**
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> * **model_file**(str): 模型文件路径
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> * **params_file**(str): 参数文件路径
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> * **config_file**(str): 推理部署配置文件
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> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
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> * **model_format**(ModelFormat): 模型格式，默认为Paddle格式
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### predict函数
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> ```python
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> PaddleSegModel.predict(input_image)
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> ```
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>
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> 模型预测结口，输入图像直接输出检测结果。
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>
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> **参数**
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>
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> > * **input_image**(np.ndarray): 输入数据，注意需为HWC，BGR格式
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> **返回**
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>
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> > 返回`fastdeploy.vision.SegmentationResult`结构体，SegmentationResult结构体说明参考[SegmentationResult结构体介绍](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/api/vision_results/segmentation_result_CN.md)
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### 类成员属性
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#### 预处理参数
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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
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> > * **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
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#### 后处理参数
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> > * **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
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## 快速链接
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- [PaddleSeg 模型介绍](..)
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- [PaddleSeg C++部署](../cpp)
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## 常见问题
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- [如何将模型预测结果SegmentationResult转为numpy格式](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/api/vision_results/segmentation_result_CN.md)
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- [如何切换模型推理后端引擎](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/faq/how_to_change_backend.md)
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- [PaddleSeg python API文档](https://www.paddlepaddle.org.cn/fastdeploy-api-doc/python/html/semantic_segmentation.html)
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examples/vision/segmentation/paddleseg/ascend/python/infer.py
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34
examples/vision/segmentation/paddleseg/ascend/python/infer.py
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import fastdeploy as fd
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import cv2
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import os
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def parse_arguments():
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import argparse
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import ast
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--model", required=True, help="Path of PaddleSeg model.")
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parser.add_argument(
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"--image", type=str, required=True, help="Path of test image file.")
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return parser.parse_args()
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runtime_option = fd.RuntimeOption()
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runtime_option.use_ascend()
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# 配置runtime，加载模型
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model_file = os.path.join(args.model, "model.pdmodel")
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params_file = os.path.join(args.model, "model.pdiparams")
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config_file = os.path.join(args.model, "deploy.yaml")
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model = fd.vision.segmentation.PaddleSegModel(
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model_file, params_file, config_file, runtime_option=runtime_option)
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# 预测图片分割结果
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im = cv2.imread(args.image)
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result = model.predict(im)
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print(result)
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# 可视化结果
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vis_im = fd.vision.vis_segmentation(im, result, weight=0.5)
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cv2.imwrite("vis_img.png", vis_im)
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