[Other] Add detection, segmentation and OCR examples for Ascend deploy.

cmake/ascend.cmake

@@ -16,13 +16,4 @@ else ()
   if(NOT PADDLELITE_URL)
     set(PADDLELITE_URL "https://bj.bcebos.com/fastdeploy/test/lite-linux_arm64_huawei_ascend_npu_python_1207.tgz")
   endif()
-  execute_process(COMMAND sh -c "ls *.so*" WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/third_libs/install/paddlelite/lib
-  COMMAND sh -c "xargs ${PATCHELF_EXE} --set-rpath '$ORIGIN'" WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/third_libs/install/paddlelite/lib
-          RESULT_VARIABLE result
-                OUTPUT_VARIABLE curr_out
-                ERROR_VARIABLE  curr_out)
-  if(ret EQUAL "1")
-    message(FATAL_ERROR "Failed to patchelf Paddle Lite libraries when using Ascend.")
-  endif()
-  message(STATUS "result:${result} out:${curr_out}")
 endif()	

cmake/paddlelite.cmake

@@ -114,6 +114,10 @@ else()
     BUILD_BYPRODUCTS ${PADDLELITE_LIB})
 endif()  
 
+if(UNIX AND (NOT APPLE) AND (NOT ANDROID))
+  add_custom_target(patchelf_paddle_lite ALL COMMAND  bash -c "PATCHELF_EXE=${PATCHELF_EXE} python ${PROJECT_SOURCE_DIR}/scripts/patch_paddle_lite.py ${PADDLELITE_INSTALL_DIR}/lib/" DEPENDS ${LIBRARY_NAME})
+endif()
+
 add_library(external_paddle_lite STATIC IMPORTED GLOBAL)
 set_property(TARGET external_paddle_lite PROPERTY IMPORTED_LOCATION ${PADDLELITE_LIB})
 add_dependencies(external_paddle_lite ${PADDLELITE_PROJECT})

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

@@ -34,6 +34,8 @@ tar xvf ppyoloe_crn_l_300e_coco.tgz
 ./infer_ppyoloe_demo ./ppyoloe_crn_l_300e_coco 000000014439.jpg 2
 # 昆仑芯XPU推理
 ./infer_ppyoloe_demo ./ppyoloe_crn_l_300e_coco 000000014439.jpg 3
+# 华为昇腾推理
+./infer_ppyoloe_demo ./ppyoloe_crn_l_300e_coco 000000014439.jpg 4
 ```
 
 以上命令只适用于Linux或MacOS, Windows下SDK的使用方式请参考:  

examples/vision/detection/paddledetection/cpp/infer_ppyolo.cc

@@ -102,6 +102,33 @@ void GpuInfer(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";
+  auto config_file = model_dir + sep + "infer_cfg.yml";
+  auto option = fastdeploy::RuntimeOption();
+  option.UseAscend();
+  auto model = fastdeploy::vision::detection::PPYOLO(model_file, params_file,
+                                                     config_file, option);
+  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, 0.5);
+  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
@@ -120,6 +147,8 @@ int main(int argc, char* argv[]) {
     GpuInfer(argv[1], argv[2]);
   } else if (std::atoi(argv[3]) == 2) {
     KunlunXinInfer(argv[1], argv[2]);
+  } else if (std::atoi(argv[3]) == 3) {
+    AscendInfer(argv[1], argv[2]);
   }
   return 0;
 }

examples/vision/detection/paddledetection/cpp/infer_ppyoloe.cc

@@ -131,6 +131,33 @@ void TrtInfer(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";
+  auto config_file = model_dir + sep + "infer_cfg.yml";
+  auto option = fastdeploy::RuntimeOption();
+  option.UseAscend();
+  auto model = fastdeploy::vision::detection::PPYOLOE(model_file, params_file,
+                                                      config_file, option);
+  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, 0.5);
+  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
@@ -151,6 +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/paddledetection/cpp/infer_ssd.cc

@@ -104,6 +104,33 @@ void GpuInfer(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";
+  auto config_file = model_dir + sep + "infer_cfg.yml";
+  auto option = fastdeploy::RuntimeOption();
+  option.UseAscend();
+  auto model = fastdeploy::vision::detection::SSD(model_file, params_file,
+                                                     config_file, option);
+  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, 0.5);
+  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
@@ -122,6 +149,8 @@ int main(int argc, char* argv[]) {
     GpuInfer(argv[1], argv[2]);
   } else if (std::atoi(argv[3]) == 2) {
     KunlunXinInfer(argv[1], argv[2]);
+  } else if (std::atoi(argv[3]) == 3) {
+    AscendInfer(argv[1], argv[2]);
   }
   return 0;
 }

examples/vision/detection/paddledetection/cpp/infer_yolov3.cc

@@ -102,6 +102,34 @@ void GpuInfer(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";
+  auto config_file = model_dir + sep + "infer_cfg.yml";
+  auto option = fastdeploy::RuntimeOption();
+  option.UseAscend();
+  auto model = fastdeploy::vision::detection::YOLOv3(model_file, params_file,
+                                                     config_file, option);
+  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, 0.5);
+  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
@@ -120,6 +148,8 @@ int main(int argc, char* argv[]) {
     GpuInfer(argv[1], argv[2]);
   } else if (std::atoi(argv[3]) == 2) {
     KunlunXinInfer(argv[1], argv[2]);
+  } else if (std::atoi(argv[3]) == 3) {
+    AscendInfer(argv[1], argv[2]);
   }
   return 0;
 }

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

@@ -25,6 +25,8 @@ 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 --use_trt True
 # 昆仑芯XPU推理
 python infer_ppyoloe.py --model_dir ppyoloe_crn_l_300e_coco --image 000000014439.jpg --device kunlunxin
+# 华为昇腾推理
+python infer_ppyoloe.py --model_dir ppyoloe_crn_l_300e_coco --image 000000014439.jpg --device ascend
 ```
 
 运行完成可视化结果如下图所示

examples/vision/detection/paddledetection/python/infer_ppyolo.py

@@ -32,6 +32,9 @@ def build_option(args):
     if args.device.lower() == "kunlunxin":
         option.use_kunlunxin()
 
+    if args.device.lower() == "ascend":
+        option.use_ascend()
+
     if args.device.lower() == "gpu":
         option.use_gpu()
 

examples/vision/detection/paddledetection/python/infer_ppyoloe.py

@@ -33,6 +33,9 @@ def build_option(args):
     if args.device.lower() == "kunlunxin":
         option.use_kunlunxin()
 
+    if args.device.lower() == "ascend":
+        option.use_ascend()
+
     if args.device.lower() == "gpu":
         option.use_gpu()
 

examples/vision/detection/paddledetection/python/infer_ssd.py

@@ -26,6 +26,9 @@ def build_option(args):
     if args.device.lower() == "kunlunxin":
         option.use_kunlunxin()
 
+    if args.device.lower() == "ascend":
+        option.use_ascend()
+
     if args.device.lower() == "gpu":
         option.use_gpu()
     return option

examples/vision/detection/paddledetection/python/infer_yolov3.py

@@ -32,6 +32,9 @@ def build_option(args):
     if args.device.lower() == "kunlunxin":
         option.use_kunlunxin()
 
+    if args.device.lower() == "ascend":
+        option.use_ascend()
+
     if args.device.lower() == "gpu":
         option.use_gpu()
 

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])

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

@@ -29,6 +29,8 @@ wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/0000000
 ./infer_paddle_demo yolov6s_infer 000000014439.jpg 1
 # 昆仑芯XPU推理
 ./infer_paddle_demo yolov6s_infer 000000014439.jpg 2
+# 华为昇腾推理
+./infer_paddle_demo yolov6s_infer 000000014439.jpg 3
 ```
 
 如果想要验证ONNX模型的推理，可以参考如下命令：

examples/vision/detection/yolov6/cpp/infer_paddle_model.cc

@@ -96,6 +96,32 @@ void GpuInfer(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::YOLOv6(
+      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) {
@@ -114,6 +140,8 @@ int main(int argc, char* argv[]) {
     GpuInfer(argv[1], argv[2]);
   } else if (std::atoi(argv[3]) == 2) {
     KunlunXinInfer(argv[1], argv[2]);
-  }
+  } else if (std::atoi(argv[3]) == 3) {
+    AscendInfer(argv[1], argv[2]);
+  } 
   return 0;
 }

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

@@ -22,6 +22,8 @@ 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
 # 昆仑芯XPU推理
 python infer_paddle_model.py --model yolov6s_infer --image 000000014439.jpg  --device kunlunxin
+# 华为昇腾推理
+python infer_paddle_model.py --model yolov6s_infer --image 000000014439.jpg  --device ascend
 ```
 如果想要验证ONNX模型的推理，可以参考如下命令：
 ```bash