[Docs] Improve docs related to Ascend inference (#1227)

* Add Readme for vision results

* Add Readme for vision results

* Add Readme for vision results

* Add Readme for vision results

* Add Readme for vision results

* Add Readme for vision results

* Add Readme for vision results

* Add Readme for vision results

* Add Readme for vision results

* Add Readme for vision results

* Add comments to create API docs

* Improve OCR comments

* fix conflict

* Fix OCR Readme

* Fix PPOCR readme

* Fix PPOCR readme

* fix conflict

* Improve ascend readme

* Improve ascend readme

* Improve ascend readme

* Improve ascend readme

Author: yunyaoXYY

docs/cn/build_and_install/huawei_ascend.md

@@ -118,5 +118,13 @@ FastDeploy现在已经集成FlyCV, 用户可以在支持的硬件平台上使用
 
 
 ## 六.昇腾部署Demo参考
-- 华为昇腾NPU 上使用C++部署 PaddleClas 分类模型请参考：[PaddleClas 华为升腾NPU C++ 部署示例](../../../examples/vision/classification/paddleclas/cpp/README.md)
-- 华为昇腾NPU 上使用Python部署 PaddleClas 分类模型请参考：[PaddleClas 华为升腾NPU Python 部署示例](../../../examples/vision/classification/paddleclas/python/README.md)
+
+| 模型系列 | C++ 部署示例 | Python 部署示例 |
+| :-----------| :--------   | :--------------- |
+|   PaddleClas       |   [昇腾NPU C++ 部署示例](../../../examples/vision/classification/paddleclas/cpp/README_CN.md)       |    [昇腾NPU Python 部署示例](../../../examples/vision/classification/paddleclas/python/README_CN.md)          |  
+|   PaddleDetection  |      [昇腾NPU C++ 部署示例](../../../examples/vision/detection/paddledetection/cpp/README_CN.md)        |     [昇腾NPU Python 部署示例](../../../examples/vision/detection/paddledetection/python/README_CN.md)               |
+|   PaddleSeg        |      [昇腾NPU C++ 部署示例](../../../examples/vision/segmentation/paddleseg/cpp/README_CN.md)        |      [昇腾NPU Python 部署示例](../../../examples//vision/segmentation/paddleseg/python/README_CN.md)              |
+|   PaddleOCR        |     [昇腾NPU C++ 部署示例](../../../examples/vision/ocr/PP-OCRv3/cpp/README_CN.md)         |      [昇腾NPU Python 部署示例](../../../examples/vision//ocr/PP-OCRv3/python/README_CN.md)              |
+|   Yolov5           |      [昇腾NPU C++ 部署示例](../../../examples/vision/detection/yolov5/cpp/README_CN.md)       |       [昇腾NPU Python 部署示例](../../../examples/vision/detection/yolov5/python/README_CN.md)             |
+|   Yolov6           |      [昇腾NPU C++ 部署示例](../../../examples/vision/detection/yolov6/cpp/README_CN.md)        |       [昇腾NPU Python 部署示例](../../../examples/vision/detection/yolov6/python/README_CN.md)             |
+|   Yolov7           |      [昇腾NPU C++ 部署示例](../../../examples/vision/detection/yolov7/cpp/README_CN.md)        |       [昇腾NPU Python 部署示例](../../../examples/vision/detection/yolov7/python/README_CN.md)             |

docs/en/build_and_install/huawei_ascend.md

@@ -117,6 +117,12 @@ In end-to-end model inference, the pre-processing and post-processing phases are
 
 
 ## Deployment demo reference
-- Deploying PaddleClas Classification Model on Huawei Ascend NPU using C++ please refer to: [PaddleClas Huawei Ascend NPU C++ Deployment Example](../../../examples/vision/classification/paddleclas/cpp/README.md)
-
-- Deploying PaddleClas classification model on Huawei Ascend NPU using Python please refer to: [PaddleClas Huawei Ascend NPU Python Deployment Example](../../../examples/vision/classification/paddleclas/python/README.md)
+| Model | C++ Example | Python Example |
+| :-----------| :--------   | :--------------- |
+|   PaddleClas       |   [Ascend NPU C++ Example](../../../examples/vision/classification/paddleclas/cpp/README.md)       |    [Ascend NPU Python Example](../../../examples/vision/classification/paddleclas/python/README.md)          |  
+|   PaddleDetection  |      [Ascend NPU C++ Example](../../../examples/vision/detection/paddledetection/cpp/README.md)        |     [Ascend NPU Python Example](../../../examples/vision/detection/paddledetection/python/README.md)               |
+|   PaddleSeg        |      [Ascend NPU C++ Example](../../../examples/vision/segmentation/paddleseg/cpp/README.md)        |      [Ascend NPU Python Example](../../../examples//vision/segmentation/paddleseg/python/README.md)              |
+|   PaddleOCR        |     [Ascend NPU C++ Example](../../../examples/vision/ocr/PP-OCRv3/cpp/README.md)         |      [Ascend NPU Python Example](../../../examples/vision//ocr/PP-OCRv3/python/README.md)              |
+|   Yolov5           |      [Ascend NPU C++ Example](../../../examples/vision/detection/yolov5/cpp/README.md)       |       [Ascend NPU Python Example](../../../examples/vision/detection/yolov5/python/README.md)             |
+|   Yolov6           |      [Ascend NPU C++ Example](../../../examples/vision/detection/yolov6/cpp/README.md)        |       [Ascend NPU Python Example](../../../examples/vision/detection/yolov6/python/README.md)             |
+|   Yolov7           |      [Ascend NPU C++ Example](../../../examples/vision/detection/yolov7/cpp/README.md)        |       [Ascend NPU Python Example](../../../examples/vision/detection/yolov7/python/README.md)             |

examples/vision/detection/paddledetection/cpp/README.md

@@ -1,7 +1,7 @@
 English | [简体中文](README_CN.md)
 # PaddleDetection C++ Deployment Example
 
-This directory provides examples that `infer_xxx.cc` fast finishes the deployment of PaddleDetection models, including PPYOLOE/PicoDet/YOLOX/YOLOv3/PPYOLO/FasterRCNN/YOLOv5/YOLOv6/YOLOv7/RTMDet on CPU/GPU and GPU accelerated by TensorRT. 
+This directory provides examples that `infer_xxx.cc` fast finishes the deployment of PaddleDetection models, including PPYOLOE/PicoDet/YOLOX/YOLOv3/PPYOLO/FasterRCNN/YOLOv5/YOLOv6/YOLOv7/RTMDet on CPU/GPU and GPU accelerated by TensorRT.
 
 Before deployment, two steps require confirmation
 
@@ -15,13 +15,13 @@ ppyoloe is taken as an example for inference deployment
 
 mkdir build
 cd build
-# Download the FastDeploy precompiled library. Users can choose your appropriate version in the `FastDeploy Precompiled Library` mentioned above 
+# Download the FastDeploy precompiled library. Users can choose your appropriate version in the `FastDeploy Precompiled Library` mentioned above
 wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
 tar xvf fastdeploy-linux-x64-x.x.x.tgz
 cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
 make -j
 
-# Download the PPYOLOE model file and test images 
+# Download the PPYOLOE model file and test images
 wget https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_300e_coco.tgz
 wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
 tar xvf ppyoloe_crn_l_300e_coco.tgz
@@ -33,12 +33,16 @@ tar xvf ppyoloe_crn_l_300e_coco.tgz
 ./infer_ppyoloe_demo ./ppyoloe_crn_l_300e_coco 000000014439.jpg 1
 # TensorRT Inference on GPU
 ./infer_ppyoloe_demo ./ppyoloe_crn_l_300e_coco 000000014439.jpg 2
+# Kunlunxin XPU Inference
+./infer_ppyoloe_demo ./ppyoloe_crn_l_300e_coco 000000014439.jpg 3
+# Huawei Ascend Inference
+./infer_ppyoloe_demo ./ppyoloe_crn_l_300e_coco 000000014439.jpg 4
 ```
 
 The above command works for Linux or MacOS. For SDK use-pattern in Windows, refer to:
 - [How to use FastDeploy C++ SDK in Windows](../../../../../docs/en/faq/use_sdk_on_windows.md)
 
-## PaddleDetection C++ Interface 
+## PaddleDetection C++ Interface
 
 ### Model Class
 
@@ -56,7 +60,7 @@ Loading and initializing PaddleDetection PPYOLOE model, where the format of mode
 
 **Parameter**
 
-> * **model_file**(str): Model file path 
+> * **model_file**(str): Model file path
 > * **params_file**(str): Parameter file path
 > * **config_file**(str): •	Configuration file path, which is the deployment yaml file exported by PaddleDetection
 > * **runtime_option**(RuntimeOption): Backend inference configuration. None by default, which is the default configuration
@@ -73,7 +77,7 @@ Loading and initializing PaddleDetection PPYOLOE model, where the format of mode
 > **Parameter**
 >
 > > * **im**: Input images in HWC or BGR format
-> > * **result**: Detection result, including detection box and confidence of each box. Refer to [Vision Model Prediction Result](../../../../../docs/api/vision_results/) for DetectionResult 
+> > * **result**: Detection result, including detection box and confidence of each box. Refer to [Vision Model Prediction Result](../../../../../docs/api/vision_results/) for DetectionResult
 
 - [Model Description](../../)
 - [Python Deployment](../python)

examples/vision/detection/paddledetection/python/README.md

@@ -9,11 +9,11 @@ Before deployment, two steps require confirmation.
 This directory provides examples that `infer_xxx.py` fast finishes the deployment of PPYOLOE/PicoDet models on CPU/GPU and GPU accelerated by TensorRT. The script is as follows
 
 ```bash
-# Download deployment example code 
+# Download deployment example code
 git clone https://github.com/PaddlePaddle/FastDeploy.git
 cd FastDeploy/examples/vision/detection/paddledetection/python/
 
-# Download the PPYOLOE model file and test images 
+# Download the PPYOLOE model file and test images
 wget https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_300e_coco.tgz
 wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
 tar xvf ppyoloe_crn_l_300e_coco.tgz
@@ -24,14 +24,18 @@ python infer_ppyoloe.py --model_dir ppyoloe_crn_l_300e_coco --image 000000014439
 python infer_ppyoloe.py --model_dir ppyoloe_crn_l_300e_coco --image 000000014439.jpg --device gpu
 # 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.）
 python infer_ppyoloe.py --model_dir ppyoloe_crn_l_300e_coco --image 000000014439.jpg --device gpu --use_trt True
+# Kunlunxin XPU Inference
+python infer_ppyoloe.py --model_dir ppyoloe_crn_l_300e_coco --image 000000014439.jpg --device kunlunxin
+# Huawei Ascend Inference
+python infer_ppyoloe.py --model_dir ppyoloe_crn_l_300e_coco --image 000000014439.jpg --device ascend
 ```
 
 The visualized result after running is as follows
 <div  align="center">  
 <img src="https://user-images.githubusercontent.com/19339784/184326520-7075e907-10ed-4fad-93f8-52d0e35d4964.jpg", width=480px, height=320px />
 </div>
 
-## PaddleDetection Python Interface 
+## PaddleDetection Python Interface
 
 ```python
 fastdeploy.vision.detection.PPYOLOE(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
@@ -52,7 +56,7 @@ PaddleDetection model loading and initialization, among which model_file and par
 
 **Parameter**
 
-> * **model_file**(str): Model file path 
+> * **model_file**(str): Model file path
 > * **params_file**(str): Parameter file path
 > * **config_file**(str): Inference configuration yaml file path
 > * **runtime_option**(RuntimeOption): Backend inference configuration. None by default. (use the default configuration)

examples/vision/detection/yolov5/cpp/README.md

@@ -12,12 +12,12 @@ Taking the CPU inference on Linux as an example, the compilation test can be com
 ```bash
 mkdir build
 cd build
-# Download the FastDeploy precompiled library. Users can choose your appropriate version in the `FastDeploy Precompiled Library` mentioned above 
+# Download the FastDeploy precompiled library. Users can choose your appropriate version in the `FastDeploy Precompiled Library` mentioned above
 wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
 tar xvf fastdeploy-linux-x64-x.x.x.tgz
 cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
 make -j
-# Download the official converted yolov5 Paddle model files and test images 
+# Download the official converted yolov5 Paddle model files and test images
 wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s_infer.tar
 tar -xvf yolov5s_infer.tar
 wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
@@ -31,11 +31,13 @@ wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/0000000
 ./infer_paddle_demo yolov5s_infer 000000014439.jpg 2
 # KunlunXin XPU inference
 ./infer_paddle_demo yolov5s_infer 000000014439.jpg 3
+# Huawei Ascend Inference
+./infer_paddle_demo yolov5s_infer 000000014439.jpg 4
 ```
 
 The above steps apply to the inference of Paddle models. If you want to conduct the inference of ONNX models, follow these steps:
 ```bash
-# 1. Download the official converted yolov5 ONNX model files and test images 
+# 1. Download the official converted yolov5 ONNX model files and test images
 wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s.onnx
 wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
 
@@ -53,7 +55,7 @@ The visualized result after running is as follows
 The above command works for Linux or MacOS. For SDK use-pattern in Windows, refer to:
 - [How to use FastDeploy C++ SDK in Windows](../../../../../docs/cn/faq/use_sdk_on_windows.md)
 
-## YOLOv5 C++ Interface 
+## YOLOv5 C++ Interface
 
 ### YOLOv5 Class
 
@@ -69,7 +71,7 @@ YOLOv5 model loading and initialization, among which model_file is the exported
 
 **Parameter**
 
-> * **model_file**(str): Model file path 
+> * **model_file**(str): Model file path
 > * **params_file**(str): Parameter file path. Merely passing an empty string when the model is in ONNX format
 > * **runtime_option**(RuntimeOption): Backend inference configuration. None by default, which is the default configuration
 > * **model_format**(ModelFormat): Model format. ONNX format by default

examples/vision/detection/yolov5/python/README.md

@@ -22,17 +22,19 @@ wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/0000000
 python infer.py --model yolov5s_infer --image 000000014439.jpg --device cpu
 # GPU inference
 python infer.py --model yolov5s_infer --image 000000014439.jpg --device gpu
-# TensorRT inference on GPU 
+# TensorRT inference on GPU
 python infer.py --model yolov5s_infer --image 000000014439.jpg --device gpu --use_trt True
 # KunlunXin XPU inference
 python infer.py --model yolov5s_infer --image 000000014439.jpg --device kunlunxin
+# Huawei Ascend Inference
+python infer.py --model yolov5s_infer --image 000000014439.jpg --device ascend
 ```
 
 The visualized result after running is as follows
 
 <img width="640" src="https://user-images.githubusercontent.com/67993288/184309358-d803347a-8981-44b6-b589-4608021ad0f4.jpg">
 
-## YOLOv5 Python Interface 
+## YOLOv5 Python Interface
 
 ```python
 fastdeploy.vision.detection.YOLOv5(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)
@@ -42,7 +44,7 @@ YOLOv5  model loading and initialization, among which model_file is the exported
 
 **Parameter**
 
-> * **model_file**(str): Model file path 
+> * **model_file**(str): Model file path
 > * **params_file**(str): Parameter file path. No need to set when the model is in ONNX format
 > * **runtime_option**(RuntimeOption): Backend inference configuration. None by default, which is the default configuration
 > * **model_format**(ModelFormat): Model format. ONNX format by default

examples/vision/detection/yolov6/python/README.md

@@ -23,6 +23,9 @@ python infer_paddle_model.py --model yolov6s_infer --image 000000014439.jpg  --d
 python infer_paddle_model.py --model yolov6s_infer --image 000000014439.jpg  --device gpu
 # KunlunXin XPU inference
 python infer_paddle_model.py --model yolov6s_infer --image 000000014439.jpg  --device kunlunxin
+# Huawei Ascend Inference
+python infer_paddle_model.py --model yolov6s_infer --image 000000014439.jpg  --device ascend
+
 ```
 If you want to verify the inference of ONNX models, refer to the following command:
 ```bash
@@ -34,15 +37,15 @@ wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/0000000
 python infer.py --model yolov6s.onnx --image 000000014439.jpg --device cpu
 # GPU inference
 python infer.py --model yolov6s.onnx --image 000000014439.jpg --device gpu
-# TensorRT inference on GPU 
+# TensorRT inference on GPU
 python infer.py --model yolov6s.onnx --image 000000014439.jpg --device gpu --use_trt True
 ```
 
 The visualized result after running is as follows
 
 <img width="640" src="https://user-images.githubusercontent.com/67993288/184301725-390e4abb-db2b-482d-931d-469381322626.jpg">
 
-## YOLOv6 Python Interface 
+## YOLOv6 Python Interface
 
 ```python
 fastdeploy.vision.detection.YOLOv6(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)
@@ -52,7 +55,7 @@ YOLOv6 model loading and initialization, among which model_file is the exported
 
 **Parameter**
 
-> * **model_file**(str): Model file path 
+> * **model_file**(str): Model file path
 > * **params_file**(str): Parameter file path. No need to set when the model is in ONNX format
 > * **runtime_option**(RuntimeOption): Backend inference configuration. None by default, which is the default configuration
 > * **model_format**(ModelFormat): Model format. ONNX format by default

examples/vision/detection/yolov7/cpp/README.md

@@ -1,7 +1,7 @@
 English | [简体中文](README_CN.md)
 # YOLOv7 C++ Deployment Example
 
-This directory provides examples that `infer.cc` fast finishes the deployment of YOLOv7 on CPU/GPU and GPU accelerated by TensorRT. 
+This directory provides examples that `infer.cc` fast finishes the deployment of YOLOv7 on CPU/GPU and GPU accelerated by TensorRT.
 
 Before deployment, two steps require confirmation
 
@@ -13,7 +13,7 @@ Taking the CPU inference on Linux as an example, the compilation test can be com
 ```bash
 mkdir build
 cd build
-# Download the FastDeploy precompiled library. Users can choose your appropriate version in the `FastDeploy  Precompiled Library` mentioned above 
+# Download the FastDeploy precompiled library. Users can choose your appropriate version in the `FastDeploy  Precompiled Library` mentioned above
 wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
 tar xvf fastdeploy-linux-x64-x.x.x.tgz
 cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
@@ -29,10 +29,12 @@ wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/0000000
 ./infer_paddle_model_demo yolov7_infer 000000014439.jpg 1
 # KunlunXin XPU inference
 ./infer_paddle_model_demo yolov7_infer 000000014439.jpg 2
+# Huawei Ascend inference
+./infer_paddle_model_demo yolov7_infer 000000014439.jpg 3
 ```
 If you want to verify the inference of ONNX models, refer to the following command:
 ```bash
-# Download the official converted yolov7 ONNX model files and test images 
+# Download the official converted yolov7 ONNX model files and test images
 wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov7.onnx
 wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
 
@@ -52,7 +54,7 @@ The visualized result after running is as follows
 The above command works for Linux or MacOS. For SDK use-pattern in Windows, refer to:
 - [How to use FastDeploy C++ SDK in Windows](../../../../../docs/en/faq/use_sdk_on_windows.md)
 
-## YOLOv7 C++ Interface 
+## YOLOv7 C++ Interface
 
 ### YOLOv7 Class
 
@@ -68,7 +70,7 @@ YOLOv7 model loading and initialization, among which model_file is the exported
 
 **Parameter**
 
-> * **model_file**(str): Model file path 
+> * **model_file**(str): Model file path
 > * **params_file**(str): Parameter file path. Merely passing an empty string when the model is in ONNX format
 > * **runtime_option**(RuntimeOption): Backend inference configuration. None by default, which is the default configuration
 > * **model_format**(ModelFormat): Model format. ONNX format by default
@@ -86,7 +88,7 @@ YOLOv7 model loading and initialization, among which model_file is the exported
 > **Parameter**
 >
 > > * **im**: Input images in HWC or BGR format
-> > * **result**: Detection results, including detection box and confidence of each box. Refer to [Vision Model Prediction Results](../../../../../docs/api/vision_results/) for DetectionResult 
+> > * **result**: Detection results, including detection box and confidence of each box. Refer to [Vision Model Prediction Results](../../../../../docs/api/vision_results/) for DetectionResult
 > > * **conf_threshold**: Filtering threshold of detection box confidence
 > > * **nms_iou_threshold**: iou threshold during NMS processing