[Docs] Pick paddleclas fastdeploy docs from PaddleClas (#1654)

* Adjust folders structures in paddleclas

* remove useless files

* Update sophgo

* improve readme

examples/vision/classification/paddleclas/ascend/python/README.md

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+# PaddleClas 昇腾 Python部署示例
+本目录下提供`infer.py`快速完成PaddleClas在昇腾AI处理器上部署的示例.
+
+## 1. 部署环境准备
+在部署前，需自行编译基于昇腾AI处理器的FastDeploy python wheel包并安装，参考文档，参考文档[昇腾AI处理器部署环境编译](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, 您可以在[FastDeploy支持的PaddleClas模型列表](../README.md)中下载所需模型.
+
+## 3. 运行部署示例
+```bash
+# 安装FastDpeloy 昇腾预测库 python包（详细文档请参考`部署环境准备`）
+
+# 下载部署示例代码
+git clone https://github.com/PaddlePaddle/FastDeploy.git
+cd  FastDeploy/examples/vision/classification/paddleclas/ascend/python
+
+# 如果您希望从PaddleClas下载示例代码，请运行
+git clone https://github.com/PaddlePaddle/PaddleClas.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 PaddleClas/deploy/fastdeploy/ascend/python
+
+# 下载ResNet50_vd模型文件和测试图片
+wget https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz
+tar -xvf ResNet50_vd_infer.tgz
+wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
+
+# 在Ascend AI 处理器上推理
+python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --topk 1
+```
+
+运行完成后返回结果如下所示
+```bash
+ClassifyResult(
+label_ids: 153,
+scores: 0.686229,
+)
+```
+
+## 4. 更多指南
+- [PaddleClas系列 Python API查阅](https://www.paddlepaddle.org.cn/fastdeploy-api-doc/python/html/image_classification.html)
+- [FastDeploy部署PaddleClas模型概览](../../)
+- [PaddleClas C++ 部署](../cpp)

examples/vision/classification/paddleclas/ascend/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 PaddleClas model.")
+    parser.add_argument(
+        "--image", type=str, required=True, help="Path of test image file.")
+    parser.add_argument(
+        "--topk", type=int, default=1, help="Return topk results.")
+    return parser.parse_args()
+
+
+def build_option(args):
+
+    option = fd.RuntimeOption()
+    option.use_ascend()
+
+    return option
+
+
+args = parse_arguments()
+
+# 配置runtime，加载模型
+runtime_option = build_option(args)
+
+model_file = os.path.join(args.model, "inference.pdmodel")
+params_file = os.path.join(args.model, "inference.pdiparams")
+config_file = os.path.join(args.model, "inference_cls.yaml")
+model = fd.vision.classification.PaddleClasModel(
+    model_file, params_file, config_file, runtime_option=runtime_option)
+
+# 预测图片分类结果
+im = cv2.imread(args.image)
+result = model.predict(im, args.topk)
+print(result)