[Backend] Add KunlunXin XPU deploy support (#894)

Revert "Revert "[Backend] Add KunlunXin XPU deploy support" (#893)"

This reverts commit 0990ab9b50.

examples/vision/detection/yolov5/cpp/CMakeLists.txt

@@ -12,3 +12,7 @@ include_directories(${FASTDEPLOY_INCS})
 add_executable(infer_demo ${PROJECT_SOURCE_DIR}/infer.cc)
 # 添加FastDeploy库依赖
 target_link_libraries(infer_demo ${FASTDEPLOY_LIBS})
+
+add_executable(infer_paddle_demo ${PROJECT_SOURCE_DIR}/infer_paddle_model.cc)
+# 添加FastDeploy库依赖
+target_link_libraries(infer_paddle_demo ${FASTDEPLOY_LIBS})

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

@@ -12,16 +12,33 @@
 ```bash
 mkdir build
 cd build
-# 下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
+# 下载 FastDeploy 预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
 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
-#下载官方转换好的yolov5模型文件和测试图片
-wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s.onnx
+#下载官方转换好的 yolov5 Paddle 模型文件和测试图片
+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
 
 
+# CPU推理
+./infer_paddle_demo yolov5s_infer 000000014439.jpg 0
+# GPU推理
+./infer_paddle_demo yolov5s_infer 000000014439.jpg 1
+# GPU上TensorRT推理
+./infer_paddle_demo yolov5s_infer 000000014439.jpg 2
+# XPU推理
+./infer_paddle_demo yolov5s_infer 000000014439.jpg 3
+```
+
+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：
+```bash
+# 1. 下载官方转换好的 yolov5 ONNX 模型文件和测试图片
+wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s.onnx
+wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
+
 # CPU推理
 ./infer_demo yolov5s.onnx 000000014439.jpg 0
 # GPU推理
@@ -29,7 +46,6 @@ wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/0000000
 # GPU上TensorRT推理
 ./infer_demo yolov5s.onnx 000000014439.jpg 2
 ```
-
 运行完成可视化结果如下图所示
 
 <img width="640" src="https://user-images.githubusercontent.com/67993288/184309358-d803347a-8981-44b6-b589-4608021ad0f4.jpg">

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

@@ -102,4 +102,4 @@ int main(int argc, char* argv[]) {
     TrtInfer(argv[1], argv[2]);
   }
   return 0;
-}
+}

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

@@ -0,0 +1,154 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "fastdeploy/vision.h"
+#ifdef WIN32
+const char sep = '\\';
+#else
+const char sep = '/';
+#endif
+
+void CpuInfer(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.UseCpu();
+  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;
+}
+
+void GpuInfer(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 option = fastdeploy::RuntimeOption();
+  option.UseGpu();
+  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;
+}
+
+void TrtInfer(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 option = fastdeploy::RuntimeOption();
+  option.UseGpu();
+  option.UseTrtBackend();
+  option.SetTrtInputShape("images", {1, 3, 640, 640});
+  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::Visualize::VisDetection(im, res);
+  cv::imwrite("vis_result.jpg", vis_im);
+  std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
+}
+
+void XpuInfer(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.UseXpu();
+  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, "
+                 "e.g ./infer_model ./yolov5s_infer ./test.jpeg 0"
+              << std::endl;
+    std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
+                 "with gpu; 2: run with gpu and use tensorrt backend; 3: run with KunlunXin XPU."
+              << std::endl;
+    return -1;
+  }
+
+  if (std::atoi(argv[3]) == 0) {
+    CpuInfer(argv[1], argv[2]);
+  } else if (std::atoi(argv[3]) == 1) {
+    GpuInfer(argv[1], argv[2]);
+  } else if (std::atoi(argv[3]) == 2) {
+    TrtInfer(argv[1], argv[2]);
+  } else if (std::atoi(argv[3]) == 3) {
+    XpuInfer(argv[1], argv[2]);
+  }
+  return 0;
+}

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

@@ -13,15 +13,18 @@ git clone https://github.com/PaddlePaddle/FastDeploy.git
 cd examples/vision/detection/yolov5/python/
 
 #下载yolov5模型文件和测试图片
-wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s.onnx
+wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s_infer.tar
+tar -xf yolov5s_infer.tar
 wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
 
 # CPU推理
-python infer.py --model yolov5s.onnx --image 000000014439.jpg --device cpu
+python infer.py --model yolov5s_infer --image 000000014439.jpg --device cpu
 # GPU推理
-python infer.py --model yolov5s.onnx --image 000000014439.jpg --device gpu
+python infer.py --model yolov5s_infer --image 000000014439.jpg --device gpu
 # GPU上使用TensorRT推理
-python infer.py --model yolov5s.onnx --image 000000014439.jpg --device gpu --use_trt True
+python infer.py --model yolov5s_infer --image 000000014439.jpg --device gpu --use_trt True
+# XPU推理
+python infer.py --model yolov5s_infer --image 000000014439.jpg --device xpu
 ```
 
 运行完成可视化结果如下图所示

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

@@ -1,20 +1,20 @@
 import fastdeploy as fd
 import cv2
+import os
 
 
 def parse_arguments():
     import argparse
     import ast
     parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--model", default=None, help="Path of yolov5 onnx model.")
+    parser.add_argument("--model", default=None, help="Path of yolov5 model.")
     parser.add_argument(
         "--image", default=None, help="Path of test image file.")
     parser.add_argument(
         "--device",
         type=str,
         default='cpu',
-        help="Type of inference device, support 'cpu' or 'gpu'.")
+        help="Type of inference device, support 'cpu' or 'gpu' or 'xpu'.")
     parser.add_argument(
         "--use_trt",
         type=ast.literal_eval,
@@ -25,6 +25,8 @@ def parse_arguments():
 
 def build_option(args):
     option = fd.RuntimeOption()
+    if args.device.lower() == "xpu":
+        option.use_xpu()
 
     if args.device.lower() == "gpu":
         option.use_gpu()
@@ -37,14 +39,15 @@ def build_option(args):
 
 args = parse_arguments()
 
-if args.model is None:
-    model = fd.download_model(name='YOLOv5s')
-else:
-    model = args.model
-
 # 配置runtime，加载模型
 runtime_option = build_option(args)
-model = fd.vision.detection.YOLOv5(model, runtime_option=runtime_option)
+model_file = os.path.join(args.model, "model.pdmodel")
+params_file = os.path.join(args.model, "model.pdiparams")
+model = fd.vision.detection.YOLOv5(
+    model_file,
+    params_file,
+    runtime_option=runtime_option,
+    model_format=fd.ModelFormat.PADDLE)
 
 # 预测图片检测结果
 if args.image is None: