[Docs] rename ppseg kunlun docs -> kunlunxin (#1662)

* [Docs] rename ppseg kunlun -> kunlunxin

* [Docs] rename ppseg fastdeploy kunlun docs -> kunlunxin

examples/vision/segmentation/paddleseg/semantic_segmentation/kunlunxin/python/README.md

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+[English](README.md) | 简体中文
+# PaddleSeg XPU Python部署示例
+
+本目录下提供`infer.py`快速完成PP-LiteSeg昆仑芯 XPU上部署的示例。
+
+## 1. 部署环境准备
+
+在部署前，需自行编译基于昆仑XPU的FastDeploy python wheel包并安装，参考文档[昆仑芯XPU部署环境](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install#自行编译安装)
+
+## 2. 部署模型准备  
+在部署前，请准备好您所需要运行的推理模型，你可以选择使用[预导出的推理模型](../README.md)或者[自行导出PaddleSeg部署模型](../README.md)，如果你部署的为**PP-Matting**、**PP-HumanMatting**以及**ModNet**请参考[Matting模型部署](../../../matting)。  
+
+## 3. 运行部署示例  
+```bash
+# 下载部署示例代码
+git clone https://github.com/PaddlePaddle/FastDeploy.git
+cd  FastDeploy/examples/vision/segmentation/semantic_segmentation/kunlunxin/python
+# 如果您希望从PaddleSeg下载示例代码，请运行
+# git clone https://github.com/PaddlePaddle/PaddleSeg.git
+# # 注意：如果当前分支找不到下面的fastdeploy测试代码，请切换到develop分支
+# # git checkout develop
+# cd PaddleSeg/deploy/fastdeploy/semantic_segmentation/kunlunxin/python
+
+# 下载PP-LiteSeg模型文件和测试图片
+wget https://bj.bcebos.com/paddlehub/fastdeploy/PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer.tgz
+tar -xvf PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer.tgz
+wget https://paddleseg.bj.bcebos.com/dygraph/demo/cityscapes_demo.png
+
+# 华为昇腾推理
+python infer.py --model PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer --image cityscapes_demo.png
+```
+
+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>
+
+## 4. 更多指南  
+- [PaddleSeg python API文档](https://www.paddlepaddle.org.cn/fastdeploy-api-doc/python/html/semantic_segmentation.html)
+- [FastDeploy部署PaddleSeg模型概览](..)
+- [PaddleSeg C++部署](../cpp)
+
+## 5. 常见问题
+- [如何将模型预测结果SegmentationResult转为numpy格式](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/faq/vision_result_related_problems.md)

examples/vision/segmentation/paddleseg/semantic_segmentation/kunlunxin/python/infer.py

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+import fastdeploy as fd
+import cv2
+import os
+
+
+def parse_arguments():
+    import argparse
+    import ast
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model", required=True, help="Path of PaddleSeg model.")
+    parser.add_argument(
+        "--image", type=str, required=True, help="Path of test image file.")
+    return parser.parse_args()
+
+
+runtime_option = fd.RuntimeOption()
+runtime_option.use_kunlunxin()
+
+# setup runtime
+model_file = os.path.join(args.model, "model.pdmodel")
+params_file = os.path.join(args.model, "model.pdiparams")
+config_file = os.path.join(args.model, "deploy.yaml")
+model = fd.vision.segmentation.PaddleSegModel(
+    model_file, params_file, config_file, runtime_option=runtime_option)
+
+# predict
+im = cv2.imread(args.image)
+result = model.predict(im)
+print(result)
+
+# visualize
+vis_im = fd.vision.vis_segmentation(im, result, weight=0.5)
+cv2.imwrite("vis_img.png", vis_im)