[Other] Add detection, segmentation and OCR examples for Ascend deploy. (#983)

* Add Huawei Ascend NPU deploy through PaddleLite CANN

* Add NNAdapter interface for paddlelite

* Modify Huawei Ascend Cmake

* Update way for compiling Huawei Ascend NPU deployment

* remove UseLiteBackend in UseCANN

* Support compile python whlee

* Change names of nnadapter API

* Add nnadapter pybind and remove useless API

* Support Python deployment on Huawei Ascend NPU

* Add models suppor for ascend

* Add PPOCR rec reszie for ascend

* fix conflict for ascend

* Rename CANN to Ascend

* Rename CANN to Ascend

* Improve ascend

* fix ascend bug

* improve ascend docs

* improve ascend docs

* improve ascend docs

* Improve Ascend

* Improve Ascend

* Move ascend python demo

* Imporve ascend

* Improve ascend

* Improve ascend

* Improve ascend

* Improve ascend

* Imporve ascend

* Imporve ascend

* Improve ascend

* acc eval script

* acc eval

* remove acc_eval from branch huawei

* Add detection and segmentation examples for Ascend deployment

* Add detection and segmentation examples for Ascend deployment

* Add PPOCR example for ascend deploy

* Imporve paddle lite compiliation

* Add FlyCV doc

* Add FlyCV doc

* Add FlyCV doc

* Imporve Ascend docs

* Imporve Ascend docs

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

@@ -31,6 +31,8 @@ wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/0000000
 ./infer_paddle_demo yolov5s_infer 000000014439.jpg 2
 # 昆仑芯XPU推理
 ./infer_paddle_demo yolov5s_infer 000000014439.jpg 3
+# 华为昇腾推理
+./infer_paddle_demo yolov5s_infer 000000014439.jpg 4
 ```
 
 上述的模型为 Paddle 模型的推理，如果想要做 ONNX 模型的推理，可以按照如下步骤：

examples/vision/detection/yolov5/cpp/infer_paddle_model.cc

@@ -130,6 +130,35 @@ void KunlunXinInfer(const std::string& model_dir, const std::string& image_file)
   std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
 }
 
+void AscendInfer(const std::string& model_dir, const std::string& image_file) {
+  auto model_file = model_dir + sep + "model.pdmodel";
+  auto params_file = model_dir + sep + "model.pdiparams";
+  fastdeploy::RuntimeOption option;
+  option.UseAscend();
+  auto model = fastdeploy::vision::detection::YOLOv5(
+      model_file, params_file, option, fastdeploy::ModelFormat::PADDLE);
+
+  if (!model.Initialized()) {
+    std::cerr << "Failed to initialize." << std::endl;
+    return;
+  }
+
+  auto im = cv::imread(image_file);
+
+  fastdeploy::vision::DetectionResult res;
+  if (!model.Predict(im, &res)) {
+    std::cerr << "Failed to predict." << std::endl;
+    return;
+  }
+  std::cout << res.Str() << std::endl;
+
+  auto vis_im = fastdeploy::vision::VisDetection(im, res);
+
+  cv::imwrite("vis_result.jpg", vis_im);
+  std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
+}
+
+
 int main(int argc, char* argv[]) {
   if (argc < 4) {
     std::cout << "Usage: infer_demo path/to/model path/to/image run_option, "
@@ -149,6 +178,8 @@ int main(int argc, char* argv[]) {
     TrtInfer(argv[1], argv[2]);
   } else if (std::atoi(argv[3]) == 3) {
     KunlunXinInfer(argv[1], argv[2]);
-  }
+  } else if (std::atoi(argv[3]) == 4) {
+    AscendInfer(argv[1], argv[2]);
+  } 
   return 0;
 }

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

@@ -25,6 +25,8 @@ python infer.py --model yolov5s_infer --image 000000014439.jpg --device gpu
 python infer.py --model yolov5s_infer --image 000000014439.jpg --device gpu --use_trt True
 # 昆仑芯XPU推理
 python infer.py --model yolov5s_infer --image 000000014439.jpg --device kunlunxin
+# 华为昇腾推理
+python infer.py --model yolov5s_infer --image 000000014439.jpg --device ascend
 ```
 
 运行完成可视化结果如下图所示

examples/vision/detection/yolov5/python/infer.py

@@ -31,6 +31,9 @@ def build_option(args):
     if args.device.lower() == "gpu":
         option.use_gpu()
 
+    if args.device.lower() == "ascend":
+        option.use_ascend()
+
     if args.use_trt:
         option.use_trt_backend()
         option.set_trt_input_shape("images", [1, 3, 640, 640])