[Model] Support PaddleYOLOv8 model (PaddlePaddle#1136)

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

@@ -42,6 +42,9 @@ target_link_libraries(infer_yolov6_demo ${FASTDEPLOY_LIBS})
 add_executable(infer_yolov7_demo ${PROJECT_SOURCE_DIR}/infer_yolov7.cc)
 target_link_libraries(infer_yolov7_demo ${FASTDEPLOY_LIBS})
 
+add_executable(infer_yolov8_demo ${PROJECT_SOURCE_DIR}/infer_yolov8.cc)
+target_link_libraries(infer_yolov8_demo ${FASTDEPLOY_LIBS})
+
 add_executable(infer_rtmdet_demo ${PROJECT_SOURCE_DIR}/infer_rtmdet.cc)
 target_link_libraries(infer_rtmdet_demo ${FASTDEPLOY_LIBS})
 

examples/vision/detection/paddledetection/cpp/infer_yolov8.cc

@@ -0,0 +1,159 @@
+// 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";
+  auto config_file = model_dir + sep + "infer_cfg.yml";
+  auto option = fastdeploy::RuntimeOption();
+  option.UseCpu();
+  auto model = fastdeploy::vision::detection::PaddleYOLOv8(model_file, params_file,
+                                                     config_file, option);
+  if (!model.Initialized()) {
+    std::cerr << "Failed to initialize." << std::endl;
+    return;
+  }
+
+  auto im = cv::imread(image_file);
+  auto im_bak = im.clone();
+
+  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_bak, res, 0.5);
+  cv::imwrite("vis_result.jpg", vis_im);
+  std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
+}
+
+void KunlunXinInfer(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.UseKunlunXin();
+  auto model = fastdeploy::vision::detection::PaddleYOLOv8(model_file, params_file,
+                                                     config_file, option);
+  if (!model.Initialized()) {
+    std::cerr << "Failed to initialize." << std::endl;
+    return;
+  }
+
+  auto im = cv::imread(image_file);
+  auto im_bak = im.clone();
+
+  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_bak, res, 0.5);
+  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 config_file = model_dir + sep + "infer_cfg.yml";
+
+  auto option = fastdeploy::RuntimeOption();
+  option.UseGpu();
+  auto model = fastdeploy::vision::detection::PaddleYOLOv8(model_file, params_file,
+                                                     config_file, option);
+  if (!model.Initialized()) {
+    std::cerr << "Failed to initialize." << std::endl;
+    return;
+  }
+
+  auto im = cv::imread(image_file);
+  auto im_bak = im.clone();
+
+  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_bak, res, 0.5);
+  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 config_file = model_dir + sep + "infer_cfg.yml";
+
+  auto option = fastdeploy::RuntimeOption();
+  option.UseGpu();
+  option.UseTrtBackend();
+  auto model = fastdeploy::vision::detection::PaddleYOLOv8(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
+        << "Usage: infer_demo path/to/model_dir path/to/image run_option, "
+           "e.g ./infer_model ./ppyolo_dirname ./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."
+              << 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){
+    KunlunXinInfer(argv[1], argv[2]);
+  }
+  return 0;
+}

examples/vision/detection/paddledetection/python/infer_yolov8.py

@@ -0,0 +1,62 @@
+import fastdeploy as fd
+import cv2
+import os
+
+
+def parse_arguments():
+    import argparse
+    import ast
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model_dir",
+        required=True,
+        help="Path of PaddleDetection model directory")
+    parser.add_argument(
+        "--image", required=True, help="Path of test image file.")
+    parser.add_argument(
+        "--device",
+        type=str,
+        default='cpu',
+        help="Type of inference device, support 'kunlunxin', 'cpu' or 'gpu'.")
+    parser.add_argument(
+        "--use_trt",
+        type=ast.literal_eval,
+        default=False,
+        help="Wether to use tensorrt.")
+    return parser.parse_args()
+
+
+def build_option(args):
+    option = fd.RuntimeOption()
+
+    if args.device.lower() == "kunlunxin":
+        option.use_kunlunxin()
+
+    if args.device.lower() == "gpu":
+        option.use_gpu()
+
+    if args.use_trt:
+        option.use_trt_backend()
+    return option
+
+
+args = parse_arguments()
+
+model_file = os.path.join(args.model_dir, "model.pdmodel")
+params_file = os.path.join(args.model_dir, "model.pdiparams")
+config_file = os.path.join(args.model_dir, "infer_cfg.yml")
+
+# 配置runtime，加载模型
+runtime_option = build_option(args)
+model = fd.vision.detection.PaddleYOLOv8(
+    model_file, params_file, config_file, runtime_option=runtime_option)
+
+# 预测图片检测结果
+im = cv2.imread(args.image)
+result = model.predict(im.copy())
+print(result)
+
+# 预测结果可视化
+vis_im = fd.vision.vis_detection(im, result, score_threshold=0.5)
+cv2.imwrite("visualized_result.jpg", vis_im)
+print("Visualized result save in ./visualized_result.jpg")

fastdeploy/vision/detection/ppdet/model.h

@@ -245,6 +245,23 @@ class FASTDEPLOY_DECL PaddleYOLOv7 : public PPDetBase {
   virtual std::string ModelName() const { return "PaddleDetection/YOLOv7"; }
 };
 
+class FASTDEPLOY_DECL PaddleYOLOv8 : public PPDetBase {
+ public:
+  PaddleYOLOv8(const std::string& model_file, const std::string& params_file,
+               const std::string& config_file,
+               const RuntimeOption& custom_option = RuntimeOption(),
+               const ModelFormat& model_format = ModelFormat::PADDLE)
+      : PPDetBase(model_file, params_file, config_file, custom_option,
+                  model_format) {
+    valid_cpu_backends = {Backend::OPENVINO, Backend::ORT, Backend::PDINFER};
+    valid_gpu_backends = {Backend::ORT, Backend::PDINFER, Backend::TRT};
+    valid_kunlunxin_backends = {Backend::LITE};
+    initialized = Initialize();
+  }
+
+  virtual std::string ModelName() const { return "PaddleDetection/YOLOv8"; }
+};
+
 class FASTDEPLOY_DECL RTMDet : public PPDetBase {
  public:
   RTMDet(const std::string& model_file, const std::string& params_file,

python/fastdeploy/vision/detection/ppdet/__init__.py

@@ -490,6 +490,31 @@ def __init__(self,
         assert self.initialized, "PaddleYOLOv7 model initialize failed."
 
 
+class PaddleYOLOv8(PPYOLOE):
+    def __init__(self,
+                 model_file,
+                 params_file,
+                 config_file,
+                 runtime_option=None,
+                 model_format=ModelFormat.PADDLE):
+        """Load a YOLOv8 model exported by PaddleDetection.
+
+        :param model_file: (str)Path of model file, e.g yolov8/model.pdmodel
+        :param params_file: (str)Path of parameters file, e.g yolov8/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
+        :param config_file: (str)Path of configuration file for deployment, e.g yolov8/infer_cfg.yml
+        :param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
+        :param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
+        """
+
+        super(PPYOLOE, self).__init__(runtime_option)
+
+        assert model_format == ModelFormat.PADDLE, "PaddleYOLOv8 model only support model format of ModelFormat.Paddle now."
+        self._model = C.vision.detection.PaddleYOLOv8(
+            model_file, params_file, config_file, self._runtime_option,
+            model_format)
+        assert self.initialized, "PaddleYOLOv8 model initialize failed."
+
+
 class RTMDet(PPYOLOE):
     def __init__(self,
                  model_file,