[Backend] Add Huawei Ascend NPU deploy using PaddleLite CANN. (#757)

* 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

examples/vision/classification/paddleclas/cpp/README.md

@@ -34,11 +34,16 @@ wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/Ima
 ./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 3
 # KunlunXin XPU推理
 ./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 4
+# Huawei Ascend NPU推理
+./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 5
 ```
 
 以上命令只适用于Linux或MacOS, Windows下SDK的使用方式请参考:  
 - [如何在Windows中使用FastDeploy C++ SDK](../../../../../docs/cn/faq/use_sdk_on_windows.md)
 
+如果用户使用华为昇腾NPU部署, 请参考以下方式在部署前初始化部署环境:
+- [如何使用华为昇腾NPU部署](../../../../../docs/cn/faq/use_sdk_on_ascend.md)
+
 ## PaddleClas C++接口
 
 ### PaddleClas类

examples/vision/classification/paddleclas/cpp/infer.cc

@@ -148,6 +148,31 @@ void TrtInfer(const std::string& model_dir, const std::string& image_file) {
   std::cout << res.Str() << std::endl;
 }
 
+void AscendInfer(const std::string& model_dir, const std::string& image_file) {
+  auto model_file = model_dir + sep + "inference.pdmodel";
+  auto params_file = model_dir + sep + "inference.pdiparams";
+  auto config_file = model_dir + sep + "inference_cls.yaml";
+  
+  auto option = fastdeploy::RuntimeOption();
+  option.UseAscend();
+
+  auto model = fastdeploy::vision::classification::PaddleClasModel(
+      model_file, params_file, config_file, option);
+
+  assert(model.Initialized());
+
+  auto im = cv::imread(image_file);
+
+  fastdeploy::vision::ClassifyResult res;
+  if (!model.Predict(&im, &res)) {
+    std::cerr << "Failed to predict." << std::endl;
+    return;
+  }
+
+  std::cout << res.Str() << std::endl;
+}
+
+
 int main(int argc, char* argv[]) {
   if (argc < 4) {
     std::cout << "Usage: infer_demo path/to/model path/to/image run_option, "
@@ -169,6 +194,8 @@ int main(int argc, char* argv[]) {
     IpuInfer(argv[1], argv[2]);
   } else if (std::atoi(argv[3]) == 4) {
     XpuInfer(argv[1], argv[2]);
+  } else if (std::atoi(argv[3]) == 5) {
+    AscendInfer(argv[1], argv[2]);
   }
   return 0;
 }

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

@@ -27,6 +27,8 @@ python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg -
 python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device ipu --topk 1
 # XPU推理
 python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device xpu --topk 1
+# 华为昇腾NPU推理
+python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device ascend --topk 1
 ```
 
 运行完成后返回结果如下所示

examples/vision/classification/paddleclas/python/infer.py

@@ -17,7 +17,8 @@ def parse_arguments():
         "--device",
         type=str,
         default='cpu',
-        help="Type of inference device, support 'cpu' or 'gpu' or 'ipu'.")
+        help="Type of inference device, support 'cpu' or 'gpu' or 'ipu' or 'xpu' or 'ascend' ."
+    )
     parser.add_argument(
         "--use_trt",
         type=ast.literal_eval,
@@ -38,6 +39,9 @@ def build_option(args):
     if args.device.lower() == "xpu":
         option.use_xpu()
 
+    if args.device.lower() == "ascend":
+        option.use_ascend()
+
     if args.use_trt:
         option.use_trt_backend()
     return option