[Backend] add sophgo backend (#1015)

* Add Sophgo Device

add sophgo backend in fastdeploy

add resnet50, yolov5s, liteseg examples.

* replace sophgo lib with download links; fix model.cc bug

* modify CodeStyle

* remove unuseful files;change the names of sophgo device and sophgo
backend

* sophgo support python and add python examples

* remove unuseful rows in cmake according pr

Co-authored-by: Zilong Xing <zilong.xing@sophgo.com>

examples/vision/segmentation/paddleseg/sophgo/python/README.md

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+# 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/sophgo.md)
+
+本目录下提供`infer.py`快速完成 pp_liteseg 在SOPHGO TPU上部署的示例。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/sophgo/python
+
+# 下载图片
+wget https://paddleseg.bj.bcebos.com/dygraph/demo/cityscapes_demo.png
+
+# 推理
+python3 infer.py --model_file ./bmodel/pp_liteseg_1684x_f32.bmodel --config_file ./bmodel/deploy.yaml --image cityscapes_demo.png
+
+# 运行完成后返回结果如下所示
+运行结果保存在sophgo_img.png中
+```
+
+## 其它文档
+- [pp_liteseg C++部署](../cpp)
+- [转换 pp_liteseg SOPHGO模型文档](../README.md)

examples/vision/segmentation/paddleseg/sophgo/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 model.")
+    parser.add_argument(
+        "--config_file", required=True, help="Path of config file.")
+    parser.add_argument(
+        "--image", type=str, required=True, help="Path of test image file.")
+
+    return parser.parse_args()
+
+
+args = parse_arguments()
+
+# 配置runtime，加载模型
+runtime_option = fd.RuntimeOption()
+runtime_option.use_sophgo()
+
+model_file = args.model
+params_file = ""
+config_file = args.config_file
+
+model = fd.vision.segmentation.PaddleSegModel(
+    model_file,
+    params_file,
+    config_file,
+    runtime_option=runtime_option,
+    model_format=fd.ModelFormat.SOPHGO)
+
+# 预测图片分类结果
+im_org = cv2.imread(args.image)
+#bmodel 是静态模型，模型输入固定，这里设置为[512, 512]
+im = cv2.resize(im_org, [512, 512], interpolation=cv2.INTER_LINEAR)
+result = model.predict(im)
+print(result)
+
+# 预测结果可视化
+vis_im = fd.vision.vis_segmentation(im, result, weight=0.5)
+cv2.imwrite("sophgo_img.png", vis_im)