Add detection and segmentation examples for Ascend deployment

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

@@ -45,30 +45,30 @@ void CpuInfer(const std::string& model_dir, const std::string& image_file) {
   std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
 }
 
-void KunlunXinInfer(const std::string& model_dir, const std::string& image_file) {
-  fastdeploy::RuntimeOption option;
-  option.UseKunlunXin();
-  auto model_file = model_dir + sep + "model.pdmodel";
-  auto params_file = model_dir + sep + "model.pdiparams";
-  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;
-  }
+// void KunlunXinInfer(const std::string& model_dir, const std::string& image_file) {
+//   fastdeploy::RuntimeOption option;
+//   option.UseKunlunXin();
+//   auto model_file = model_dir + sep + "model.pdmodel";
+//   auto params_file = model_dir + sep + "model.pdiparams";
+//   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);
+//   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;
+//   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;
-}
+//   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;
+// }
 
 void GpuInfer(const std::string& model_dir, const std::string& image_file) {
   fastdeploy::RuntimeOption option;
@@ -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) {
@@ -113,7 +139,9 @@ int main(int argc, char* argv[]) {
   } else if (std::atoi(argv[3]) == 1) {
     GpuInfer(argv[1], argv[2]);
   } else if (std::atoi(argv[3]) == 2) {
-    KunlunXinInfer(argv[1], argv[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

examples/vision/detection/yolov6/python/infer_paddle_model.py

@@ -28,6 +28,9 @@ def build_option(args):
     if args.device.lower() == "kunlunxin":
         option.use_kunlunxin()
 
+    if args.device.lower() == "ascend":
+        option.use_ascend()
+
     return option
 
 

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

@@ -28,6 +28,8 @@ wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/0000000
 ./infer_paddle_model_demo yolov7_infer 000000014439.jpg 1
 # 昆仑芯XPU推理
 ./infer_paddle_model_demo yolov7_infer 000000014439.jpg 2
+# 华为昇腾推理
+./infer_paddle_model_demo yolov7_infer 000000014439.jpg 3
 ```
 如果想要验证ONNX模型的推理，可以参考如下命令：
 ```bash

examples/vision/detection/yolov7/cpp/infer_paddle_model.cc

@@ -31,7 +31,7 @@ void InitAndInfer(const std::string& model_dir, const std::string& image_file,
   auto im = cv::imread(image_file);
 
   fastdeploy::vision::DetectionResult res;
-  if (!model.Predict(&im, &res)) {
+  if (!model.Predict(im, &res)) {
     std::cerr << "Failed to predict." << std::endl;
     return;
   }
@@ -68,7 +68,9 @@ int main(int argc, char* argv[]) {
     option.UseTrtBackend();
   } else if (flag == 2) {
     option.UseKunlunXin();
-    }
+  } else if (flag == 3) {
+    option.UseAscend();
+  }
 
   std::string model_dir = argv[1];
   std::string test_image = argv[2];

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

@@ -24,6 +24,8 @@ python infer_paddle_model.py --model yolov7_infer --image 000000014439.jpg --dev
 python infer_paddle_model.py --model yolov7_infer --image 000000014439.jpg --device gpu
 # 昆仑芯XPU推理
 python infer_paddle_model.py --model yolov7_infer --image 000000014439.jpg --device kunlunxin
+# 华为昇腾推理
+python infer_paddle_model.py --model yolov7_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

examples/vision/detection/yolov7/python/README_EN.md

@@ -24,6 +24,8 @@ python infer_paddle_model.py --model yolov7_infer --image 000000014439.jpg --dev
 python infer_paddle_model.py --model yolov7_infer --image 000000014439.jpg --device gpu
 # KunlunXin XPU
 python infer_paddle_model.py --model yolov7_infer --image 000000014439.jpg --device kunlunxin
+# Huawei Ascend
+python infer_paddle_model.py --model yolov7_infer --image 000000014439.jpg --device ascend
 ```
 If you want to test ONNX model:
 ```bash

examples/vision/detection/yolov7/python/infer_paddle_model.py

@@ -28,6 +28,9 @@ def build_option(args):
     if args.device.lower() == "kunlunxin":
         option.use_kunlunxin()
 
+    if args.device.lower() == "ascend":
+        option.use_ascend()
+
     return option
 
 

examples/vision/segmentation/paddleseg/cpp/README.md

@@ -34,6 +34,8 @@ wget https://paddleseg.bj.bcebos.com/dygraph/demo/cityscapes_demo.png
 ./infer_demo Unet_cityscapes_without_argmax_infer cityscapes_demo.png 2
 # 昆仑芯XPU推理
 ./infer_demo Unet_cityscapes_without_argmax_infer cityscapes_demo.png 3
+# 华为昇腾推理
+./infer_demo Unet_cityscapes_without_argmax_infer cityscapes_demo.png 4
 ```
 
 运行完成可视化结果如下图所示

examples/vision/segmentation/paddleseg/cpp/infer.cc

@@ -135,6 +135,34 @@ 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 + "deploy.yaml";
+  auto option = fastdeploy::RuntimeOption();
+  option.UseAscend();
+  auto model = fastdeploy::vision::segmentation::PaddleSegModel(
+      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::SegmentationResult 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::VisSegmentation(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
@@ -155,6 +183,8 @@ 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/segmentation/paddleseg/python/README.md

@@ -27,6 +27,8 @@ python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_
 python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_demo.png --device gpu --use_trt True
 # 昆仑芯XPU推理
 python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_demo.png --device kunlunxin
+# 华为昇腾推理
+python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_demo.png --device ascend
 ```
 
 运行完成可视化结果如下图所示

examples/vision/segmentation/paddleseg/python/infer.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.use_trt:
         option.use_trt_backend()
         option.set_trt_input_shape("x", [1, 3, 256, 256], [1, 3, 1024, 1024],