Validate all backends for detection models and add demo code & docs (#94)

* Validate all backends for detection models and add demo code and doc

* Delete .README.md.swp

csrc/fastdeploy/backends/paddle/paddle_backend.cc

@@ -26,6 +26,9 @@ void PaddleBackend::BuildOption(const PaddleBackendOption& option) {
       config_.SetMkldnnCacheCapacity(option.mkldnn_cache_size);
     }
   }
+  if (!option.enable_log_info) {
+    config_.DisableGlogInfo();
+  }
   config_.SetCpuMathLibraryNumThreads(option.cpu_thread_num);
 }
 

csrc/fastdeploy/backends/paddle/paddle_backend.h

@@ -32,6 +32,8 @@ struct PaddleBackendOption {
 #endif
   bool enable_mkldnn = true;
 
+  bool enable_log_info = false;
+
   int mkldnn_cache_size = 1;
   int cpu_thread_num = 8;
   // initialize memory size(MB) for GPU

csrc/fastdeploy/fastdeploy_model.cc

@@ -12,7 +12,6 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 #include "fastdeploy/fastdeploy_model.h"
-#include "fastdeploy/utils/unique_ptr.h"
 #include "fastdeploy/utils/utils.h"
 
 namespace fastdeploy {
@@ -54,12 +53,52 @@ bool FastDeployModel::InitRuntime() {
           << std::endl;
       return false;
     }
-    runtime_ = utils::make_unique<Runtime>();
-    if (!runtime_->Init(runtime_option)) {
-      return false;
+
+    bool use_gpu = (runtime_option.device == Device::GPU);
+#ifndef WITH_GPU
+    use_gpu = false;
+#endif
+
+    // whether the model is supported by the setted backend
+    bool is_supported = false;
+    if (use_gpu) {
+      for (auto& item : valid_gpu_backends) {
+        if (item == runtime_option.backend) {
+          is_supported = true;
+          break;
+        }
+      }
+    } else {
+      for (auto& item : valid_cpu_backends) {
+        if (item == runtime_option.backend) {
+          is_supported = true;
+          break;
+        }
+      }
+    }
+
+    if (is_supported) {
+      runtime_ = std::unique_ptr<Runtime>(new Runtime());
+      if (!runtime_->Init(runtime_option)) {
+        return false;
+      }
+      runtime_initialized_ = true;
+      return true;
+    } else {
+      FDWARNING << ModelName() << " is not supported with backend "
+                << Str(runtime_option.backend) << "." << std::endl;
+      if (use_gpu) {
+        FDASSERT(valid_gpu_backends.size() > 0,
+                 "There's no valid gpu backend for " + ModelName() + ".");
+        FDWARNING << "FastDeploy will choose " << Str(valid_gpu_backends[0])
+                  << " for model inference." << std::endl;
+      } else {
+        FDASSERT(valid_gpu_backends.size() > 0,
+                 "There's no valid cpu backend for " + ModelName() + ".");
+        FDWARNING << "FastDeploy will choose " << Str(valid_cpu_backends[0])
+                  << " for model inference." << std::endl;
+      }
     }
-    runtime_initialized_ = true;
-    return true;
   }
 
   if (runtime_option.device == Device::CPU) {

csrc/fastdeploy/fastdeploy_runtime.cc

@@ -181,6 +181,10 @@ void RuntimeOption::EnablePaddleMKLDNN() { pd_enable_mkldnn = true; }
 
 void RuntimeOption::DisablePaddleMKLDNN() { pd_enable_mkldnn = false; }
 
+void RuntimeOption::EnablePaddleLogInfo() { pd_enable_log_info = true; }
+
+void RuntimeOption::DisablePaddleLogInfo() { pd_enable_log_info = false; }
+
 void RuntimeOption::SetPaddleMKLDNNCacheSize(int size) {
   FDASSERT(size > 0, "Parameter size must greater than 0.");
   pd_mkldnn_cache_size = size;
@@ -272,6 +276,7 @@ void Runtime::CreatePaddleBackend() {
 #ifdef ENABLE_PADDLE_BACKEND
   auto pd_option = PaddleBackendOption();
   pd_option.enable_mkldnn = option.pd_enable_mkldnn;
+  pd_option.enable_log_info = option.pd_enable_log_info;
   pd_option.mkldnn_cache_size = option.pd_mkldnn_cache_size;
   pd_option.use_gpu = (option.device == Device::GPU) ? true : false;
   pd_option.gpu_id = option.device_id;

csrc/fastdeploy/fastdeploy_runtime.h

@@ -68,6 +68,11 @@ struct FASTDEPLOY_DECL RuntimeOption {
   // disable mkldnn while use paddle inference in CPU
   void DisablePaddleMKLDNN();
 
+  // enable debug information of paddle backend
+  void EnablePaddleLogInfo();
+  // disable debug information of paddle backend
+  void DisablePaddleLogInfo();
+
   // set size of cached shape while enable mkldnn with paddle inference backend
   void SetPaddleMKLDNNCacheSize(int size);
 
@@ -108,6 +113,7 @@ struct FASTDEPLOY_DECL RuntimeOption {
 
   // ======Only for Paddle Backend=====
   bool pd_enable_mkldnn = true;
+  bool pd_enable_log_info = false;
   int pd_mkldnn_cache_size = 1;
 
   // ======Only for Trt Backend=======

csrc/fastdeploy/pybind/fastdeploy_runtime.cc

@@ -28,6 +28,8 @@ void BindRuntime(pybind11::module& m) {
       .def("use_trt_backend", &RuntimeOption::UseTrtBackend)
       .def("enable_paddle_mkldnn", &RuntimeOption::EnablePaddleMKLDNN)
       .def("disable_paddle_mkldnn", &RuntimeOption::DisablePaddleMKLDNN)
+      .def("enable_paddle_log_info", &RuntimeOption::EnablePaddleLogInfo)
+      .def("disable_paddle_log_info", &RuntimeOption::DisablePaddleLogInfo)
       .def("set_paddle_mkldnn_cache_size",
            &RuntimeOption::SetPaddleMKLDNNCacheSize)
       .def("set_trt_input_shape", &RuntimeOption::SetTrtInputShape)

csrc/fastdeploy/vision/detection/ppdet/picodet.cc

@@ -24,8 +24,8 @@ PicoDet::PicoDet(const std::string& model_file, const std::string& params_file,
                  const RuntimeOption& custom_option,
                  const Frontend& model_format) {
   config_file_ = config_file;
-  valid_cpu_backends = {Backend::PDINFER, Backend::ORT};
-  valid_gpu_backends = {Backend::PDINFER, Backend::ORT};
+  valid_cpu_backends = {Backend::ORT, Backend::PDINFER};
+  valid_gpu_backends = {Backend::ORT, Backend::PDINFER, Backend::TRT};
   runtime_option = custom_option;
   runtime_option.model_format = model_format;
   runtime_option.model_file = model_file;

csrc/fastdeploy/vision/detection/ppdet/ppyoloe.cc

@@ -14,8 +14,8 @@ PPYOLOE::PPYOLOE(const std::string& model_file, const std::string& params_file,
                  const RuntimeOption& custom_option,
                  const Frontend& model_format) {
   config_file_ = config_file;
-  valid_cpu_backends = {Backend::PDINFER, Backend::ORT};
-  valid_gpu_backends = {Backend::PDINFER, Backend::ORT};
+  valid_cpu_backends = {Backend::ORT, Backend::PDINFER};
+  valid_gpu_backends = {Backend::ORT, Backend::PDINFER, Backend::TRT};
   runtime_option = custom_option;
   runtime_option.model_format = model_format;
   runtime_option.model_file = model_file;

csrc/fastdeploy/vision/detection/ppdet/yolov3.cc

@@ -23,8 +23,8 @@ YOLOv3::YOLOv3(const std::string& model_file, const std::string& params_file,
                const RuntimeOption& custom_option,
                const Frontend& model_format) {
   config_file_ = config_file;
-  valid_cpu_backends = {Backend::PDINFER};
-  valid_gpu_backends = {Backend::PDINFER};
+  valid_cpu_backends = {Backend::ORT, Backend::PDINFER};
+  valid_gpu_backends = {Backend::ORT, Backend::PDINFER, Backend::TRT};
   runtime_option = custom_option;
   runtime_option.model_format = model_format;
   runtime_option.model_file = model_file;

csrc/fastdeploy/vision/detection/ppdet/yolox.cc

@@ -18,12 +18,14 @@ namespace fastdeploy {
 namespace vision {
 namespace detection {
 
-PaddleYOLOX::PaddleYOLOX(const std::string& model_file, const std::string& params_file,
-             const std::string& config_file, const RuntimeOption& custom_option,
-             const Frontend& model_format) {
+PaddleYOLOX::PaddleYOLOX(const std::string& model_file,
+                         const std::string& params_file,
+                         const std::string& config_file,
+                         const RuntimeOption& custom_option,
+                         const Frontend& model_format) {
   config_file_ = config_file;
-  valid_cpu_backends = {Backend::PDINFER, Backend::ORT};
-  valid_gpu_backends = {Backend::PDINFER, Backend::ORT};
+  valid_cpu_backends = {Backend::ORT, Backend::PDINFER};
+  valid_gpu_backends = {Backend::ORT, Backend::PDINFER, Backend::TRT};
   runtime_option = custom_option;
   runtime_option.model_format = model_format;
   runtime_option.model_file = model_file;

csrc/fastdeploy/vision/visualize/detection.cc

@@ -24,13 +24,17 @@ namespace vision {
 // If need to visualize num_classes > 1000
 // Please call Visualize::GetColorMap(num_classes) first
 cv::Mat Visualize::VisDetection(const cv::Mat& im,
-                                const DetectionResult& result, int line_size,
+                                const DetectionResult& result,
+                                float score_threshold, int line_size,
                                 float font_size) {
   auto color_map = GetColorMap();
   int h = im.rows;
   int w = im.cols;
   auto vis_im = im.clone();
   for (size_t i = 0; i < result.boxes.size(); ++i) {
+    if (result.scores[i] < score_threshold) {
+      continue;
+    }
     cv::Rect rect(result.boxes[i][0], result.boxes[i][1],
                   result.boxes[i][2] - result.boxes[i][0],
                   result.boxes[i][3] - result.boxes[i][1]);

csrc/fastdeploy/vision/visualize/visualize.h

@@ -26,7 +26,8 @@ class FASTDEPLOY_DECL Visualize {
   static std::vector<int> color_map_;
   static const std::vector<int>& GetColorMap(int num_classes = 1000);
   static cv::Mat VisDetection(const cv::Mat& im, const DetectionResult& result,
-                              int line_size = 1, float font_size = 0.5f);
+                              float score_threshold = 0.0, int line_size = 1,
+                              float font_size = 0.5f);
   static cv::Mat VisFaceDetection(const cv::Mat& im,
                                   const FaceDetectionResult& result,
                                   int line_size = 1, float font_size = 0.5f);

csrc/fastdeploy/vision/visualize/visualize_pybind.cc

@@ -20,10 +20,10 @@ void BindVisualize(pybind11::module& m) {
       .def(pybind11::init<>())
       .def_static("vis_detection",
                   [](pybind11::array& im_data, vision::DetectionResult& result,
-                     int line_size, float font_size) {
+                     float score_threshold, int line_size, float font_size) {
                     auto im = PyArrayToCvMat(im_data);
                     auto vis_im = vision::Visualize::VisDetection(
-                        im, result, line_size, font_size);
+                        im, result, score_threshold, line_size, font_size);
                     FDTensor out;
                     vision::Mat(vis_im).ShareWithTensor(&out);
                     return TensorToPyArray(out);

examples/vision/detection/paddledetection/README.md

@@ -0,0 +1,45 @@
+# PaddleDetection模型部署
+
+## 模型版本说明
+
+- [PaddleDetection Release/2.4](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4)
+
+## 支持模型列表
+
+目前FastDeploy支持如下模型的部署
+
+- [PPYOLOE系列模型](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyoloe)
+- [PicoDet系列模型](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/picodet)
+- [PPYOLO系列模型(含v2)](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo)
+- [YOLOv3系列模型](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3)
+- [YOLOX系列模型](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolox)
+- [FasterRCNN系列模型](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/faster_rcnn)
+
+## 导出部署模型
+
+在部署前，需要先将PaddleDetection导出成部署模型，导出步骤参考文档[导出模型](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.4/deploy/EXPORT_MODEL.md)
+
+注意：在导出模型时不要进行NMS的去除操作，正常导出即可。
+
+## 下载预训练模型
+
+为了方便开发者的测试，下面提供了PaddleDetection导出的各系列模型，开发者可直接下载使用。
+
+其中精度指标来源于PaddleDetection中对各模型的介绍，详情各参考PaddleDetection中的说明。
+
+
+| 模型                                                               | 参数大小    | 精度    | 备注 |
+|:---------------------------------------------------------------- |:----- |:----- | :------ |
+| [picodet_l_320_coco_lcnet](https://bj.bcebos.com/paddlehub/fastdeploy/picodet_l_320_coco_lcnet.tgz) |23MB | 42.6% |
+| [ppyoloe_crn_l_300e_coco](https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_300e_coco.tgz) |200MB | 51.4% |
+| [ppyolo_r50vd_dcn_1x_coco](https://bj.bcebos.com/paddlehub/fastdeploy/ppyolo_r50vd_dcn_1x_coco.tgz) | 180MB | 44.8% | 暂不支持TensorRT |
+| [ppyolov2_r101vd_dcn_365e_coco](https://bj.bcebos.com/paddlehub/fastdeploy/ppyolov2_r101vd_dcn_365e_coco.tgz) | 282MB | 49.7% | 暂不支持TensorRT |
+| [yolov3_darknet53_270e_coco](https://bj.bcebos.com/paddlehub/fastdeploy/yolov3_darknet53_270e_coco.tgz) |237MB | 39.1% | |
+| [yolox_s_300e_coco](https://bj.bcebos.com/paddlehub/fastdeploy/yolox_s_300e_coco.tgz) | 35MB | 40.4% | |
+| [faster_rcnn_r50_vd_fpn_2x_coco](https://bj.bcebos.com/paddlehub/fastdeploy/faster_rcnn_r50_vd_fpn_2x_coco.tgz) | 160MB | 40.8%| 暂不支持TensorRT |
+
+
+## 详细部署文档
+
+- [Python部署](python)
+- [C++部署](cpp)

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

@@ -0,0 +1,28 @@
+PROJECT(infer_demo C CXX)
+CMAKE_MINIMUM_REQUIRED (VERSION 3.12)
+
+# 指定下载解压后的fastdeploy库路径
+option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
+
+include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
+
+# 添加FastDeploy依赖头文件
+include_directories(${FASTDEPLOY_INCS})
+
+add_executable(infer_faster_rcnn_demo ${PROJECT_SOURCE_DIR}/infer_faster_rcnn.cc)
+target_link_libraries(infer_faster_rcnn_demo ${FASTDEPLOY_LIBS})
+
+add_executable(infer_ppyoloe_demo ${PROJECT_SOURCE_DIR}/infer_ppyoloe.cc)
+target_link_libraries(infer_ppyoloe_demo ${FASTDEPLOY_LIBS})
+
+add_executable(infer_picodet_demo ${PROJECT_SOURCE_DIR}/infer_picodet.cc)
+target_link_libraries(infer_picodet_demo ${FASTDEPLOY_LIBS})
+
+add_executable(infer_yolox_demo ${PROJECT_SOURCE_DIR}/infer_yolox.cc)
+target_link_libraries(infer_yolox_demo ${FASTDEPLOY_LIBS})
+
+add_executable(infer_yolov3_demo ${PROJECT_SOURCE_DIR}/infer_yolov3.cc)
+target_link_libraries(infer_yolov3_demo ${FASTDEPLOY_LIBS})
+
+add_executable(infer_ppyolo_demo ${PROJECT_SOURCE_DIR}/infer_ppyolo.cc)
+target_link_libraries(infer_ppyolo_demo ${FASTDEPLOY_LIBS})

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

@@ -0,0 +1,75 @@
+# PaddleDetection C++部署示例
+
+本目录下提供`infer_xxx.cc`快速完成PaddleDetection模型包括PPYOLOE/PicoDet/YOLOX/YOLOv3/PPYOLO/FasterRCNN在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。
+
+在部署前，需确认以下两个步骤
+
+- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/quick_start/requirements.md)  
+- 2. 根据开发环境，下载预编译部署库和samples代码，参考[FastDeploy预编译库](../../../../../docs/compile/prebuilt_libraries.md)
+
+以Linux上CPU推理为例，在本目录执行如下命令即可完成编译测试
+
+```
+mkdir build
+cd build
+wget https://bj.bcebos.com/paddlehub/fastdeploy/libs/0.2.0/fastdeploy-linux-x64-gpu-0.2.0.tgz
+tar xvf fastdeploy-linux-x64-gpu-0.2.0.tgz
+cd fastdeploy-linux-x64-gpu-0.2.0/examples/vision/detection/paddledetection
+mkdir build && cd build
+cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/../../../../../../fastdeploy-linux-x64-gpu-0.2.0
+make -j
+
+# 下载PPYOLOE模型文件和测试图片
+wget https://bj.bcebos.com/paddlehub/fastdeploy/picodet_l_320_coco_lcnet.tgz
+wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000087038.jpg
+tar xvf picodet_l_320_coco_lcnet.tgz
+
+
+# CPU推理
+./infer_ppyoloe_demo ./picodet_l_320_coco_lcnet 000000087038.jpg 0
+# GPU推理
+./infer_ppyoloe_demo ./picodet_l_320_coco_lcnet 000000087038.jpg 1
+# GPU上TensorRT推理
+./infer_ppyoloe_demo ./picodet_l_320_coco_lcnet 000000087038.jpg 2
+```
+
+## PaddleDetection C++接口
+
+### 模型类
+
+PaddleDetection目前支持6种模型系列，类名分别为`PPYOLOE`, `PicoDet`, `PaddleYOLOX`, `PPYOLO`, `FasterRCNN`，所有类名的构造函数和预测函数在参数上完全一致，本文档以PPYOLOE为例讲解API
+```
+fastdeploy::vision::detection::PPYOLOE(
+        const string& model_file,
+        const string& params_file,
+        const string& config_file
+        const RuntimeOption& runtime_option = RuntimeOption(),
+        const Frontend& model_format = Frontend::PADDLE)
+```
+
+PaddleDetection PPYOLOE模型加载和初始化，其中model_file为导出的ONNX模型格式。
+
+**参数**
+
+> * **model_file**(str): 模型文件路径
+> * **params_file**(str): 参数文件路径
+> * **config_file**(str): 配置文件路径，即PaddleDetection导出的部署yaml文件
+> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
+> * **model_format**(Frontend): 模型格式，默认为PADDLE格式
+
+#### Predict函数
+
+> ```
+> PPYOLOE::Predict(cv::Mat* im, DetectionResult* result)
+> ```
+>
+> 模型预测接口，输入图像直接输出检测结果。
+>
+> **参数**
+>
+> > * **im**: 输入图像，注意需为HWC，BGR格式
+> > * **result**: 检测结果，包括检测框，各个框的置信度, DetectionResult说明参考[视觉模型预测结果](../../../../../docs/api/vision_results/)
+
+- [模型介绍](../../)
+- [Python部署](../python)
+- [视觉模型预测结果](../../../../../docs/api/vision_results/)

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

@@ -0,0 +1,94 @@
+// 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 model = fastdeploy::vision::detection::FasterRCNN(
+      model_file, params_file, config_file);
+  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;
+  }
+
+  auto vis_im = fastdeploy::vision::Visualize::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::FasterRCNN(
+      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;
+  }
+
+  auto vis_im = fastdeploy::vision::Visualize::VisDetection(im_bak, 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 ./faster_rcnn_r50_vd_fpn_2x_coco ./test.jpeg 0"
+        << std::endl;
+    std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
+                 "with gpu."
+              << 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]);
+  }
+  return 0;
+}

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

@@ -0,0 +1,127 @@
+// 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 model = fastdeploy::vision::detection::PicoDet(model_file, params_file,
+                                                      config_file);
+  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;
+  }
+
+  auto vis_im = fastdeploy::vision::Visualize::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::PicoDet(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;
+  }
+
+  auto vis_im = fastdeploy::vision::Visualize::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();
+  option.SetTrtInputShape("image", {1, 3, 320, 320});
+  option.SetTrtInputShape("scale_Factor", {1, 2});
+  auto model = fastdeploy::vision::detection::PicoDet(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;
+  }
+
+  auto vis_im = fastdeploy::vision::Visualize::VisDetection(im_bak, 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 ./picodet_model_dir ./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."
+              << 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]);
+  }
+  return 0;
+}

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

@@ -0,0 +1,94 @@
+// 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 model = fastdeploy::vision::detection::PPYOLO(model_file, params_file,
+                                                     config_file);
+  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;
+  }
+
+  auto vis_im = fastdeploy::vision::Visualize::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::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);
+  auto im_bak = im.clone();
+
+  fastdeploy::vision::DetectionResult res;
+  if (!model.Predict(&im, &res)) {
+    std::cerr << "Failed to predict." << std::endl;
+    return;
+  }
+
+  auto vis_im = fastdeploy::vision::Visualize::VisDetection(im_bak, 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."
+              << 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]);
+  }
+  return 0;
+}

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

@@ -0,0 +1,127 @@
+// 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 model = fastdeploy::vision::detection::PPYOLOE(model_file, params_file,
+                                                      config_file);
+  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;
+  }
+
+  auto vis_im = fastdeploy::vision::Visualize::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::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);
+  auto im_bak = im.clone();
+
+  fastdeploy::vision::DetectionResult res;
+  if (!model.Predict(&im, &res)) {
+    std::cerr << "Failed to predict." << std::endl;
+    return;
+  }
+
+  auto vis_im = fastdeploy::vision::Visualize::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();
+  option.SetTrtInputShape("image", {1, 3, 640, 640});
+  option.SetTrtInputShape("scale_factor", {1, 2});
+  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);
+  auto im_bak = im.clone();
+
+  fastdeploy::vision::DetectionResult res;
+  if (!model.Predict(&im, &res)) {
+    std::cerr << "Failed to predict." << std::endl;
+    return;
+  }
+
+  auto vis_im = fastdeploy::vision::Visualize::VisDetection(im_bak, 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 ./ppyoloe_model_dir ./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."
+              << 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]);
+  }
+  return 0;
+}

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

@@ -0,0 +1,94 @@
+// 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 model = fastdeploy::vision::detection::YOLOv3(model_file, params_file,
+                                                     config_file);
+  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;
+  }
+
+  auto vis_im = fastdeploy::vision::Visualize::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::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);
+  auto im_bak = im.clone();
+
+  fastdeploy::vision::DetectionResult res;
+  if (!model.Predict(&im, &res)) {
+    std::cerr << "Failed to predict." << std::endl;
+    return;
+  }
+
+  auto vis_im = fastdeploy::vision::Visualize::VisDetection(im_bak, 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."
+              << 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]);
+  }
+  return 0;
+}

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

@@ -0,0 +1,127 @@
+// 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 model = fastdeploy::vision::detection::PaddleYOLOX(
+      model_file, params_file, config_file);
+  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;
+  }
+
+  auto vis_im = fastdeploy::vision::Visualize::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::PaddleYOLOX(
+      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;
+  }
+
+  auto vis_im = fastdeploy::vision::Visualize::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();
+  option.SetTrtInputShape("image", {1, 3, 640, 640});
+  option.SetTrtInputShape("scale_factor", {1, 2});
+  auto model = fastdeploy::vision::detection::PaddleYOLOX(
+      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;
+  }
+
+  auto vis_im = fastdeploy::vision::Visualize::VisDetection(im_bak, 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 ./paddle_yolox_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 by tensorrt."
+              << 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]);
+  }
+  return 0;
+}

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

@@ -0,0 +1,72 @@
+# PaddleDetection Python部署示例
+
+在部署前，需确认以下两个步骤
+
+- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/quick_start/requirements.md)  
+- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../docs/quick_start/install.md)
+
+本目录下提供`infer_xxx.py`快速完成PPYOLOE/PicoDet等模型在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。执行如下脚本即可完成
+
+```
+#下载PPYOLOE模型文件和测试图片
+wget https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_300e_coco.tgz
+wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
+tar xvf ppyoloe_crn_l_300e_coco.tgz
+
+#下载部署示例代码
+git clone https://github.com/PaddlePaddle/FastDeploy.git
+cd examples/vison/detection/paddledetection/python/
+
+# CPU推理
+python infer.py --model_dir ppyoloe_crn_l_300e_coco --image 000000087038.jpg --device cpu
+# GPU推理
+python infer.py --model_dir ppyoloe_crn_l_300e_coco --image 000000087038.jpg --device gpu
+# GPU上使用TensorRT推理 （注意：TensorRT推理第一次运行，有序列化模型的操作，有一定耗时，需要耐心等待）
+python infer.py --model_dir ppyoloe_crn_l_300e_coco --image 000000087038.jpg --device gpu --use_trt True
+```
+
+运行完成可视化结果如下图所示
+
+## PaddleDetection Python接口
+
+```
+fastdeploy.vision.detection.PPYOLOE(model_file, params_file, config_file, runtime_option=None, model_format=Frontend.PADDLE)
+fastdeploy.vision.detection.PicoDet(model_file, params_file, config_file, runtime_option=None, model_format=Frontend.PADDLE)
+fastdeploy.vision.detection.PaddleYOLOX(model_file, params_file, config_file, runtime_option=None, model_format=Frontend.PADDLE)
+fastdeploy.vision.detection.YOLOv3(model_file, params_file, config_file, runtime_option=None, model_format=Frontend.PADDLE)
+fastdeploy.vision.detection.PPYOLO(model_file, params_file, config_file, runtime_option=None, model_format=Frontend.PADDLE)
+fastdeploy.vision.detection.FasterRCNN(model_file, params_file, config_file, runtime_option=None, model_format=Frontend.PADDLE)
+```
+
+PaddleDetection模型加载和初始化，其中model_file， params_file为导出的Paddle部署模型格式, config_file为PaddleDetection同时导出的部署配置yaml文件
+
+**参数**
+
+> * **model_file**(str): 模型文件路径
+> * **params_file**(str): 参数文件路径
+> * **config_file**(str): 推理配置yaml文件路径
+> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
+> * **model_format**(Frontend): 模型格式，默认为Paddle
+
+### predict函数
+
+PaddleDetection中各个模型，包括PPYOLOE/PicoDet/PaddleYOLOX/YOLOv3/PPYOLO/FasterRCNN，均提供如下同样的成员函数用于进去图像的检测
+> ```
+> PPYOLOE.predict(image_data, conf_threshold=0.25, nms_iou_threshold=0.5)
+> ```
+>
+> 模型预测结口，输入图像直接输出检测结果。
+>
+> **参数**
+>
+> > * **image_data**(np.ndarray): 输入数据，注意需为HWC，BGR格式
+
+> **返回**
+>
+> > 返回`fastdeploy.vision.DetectionResult`结构体，结构体说明参考文档[视觉模型预测结果](../../../../../docs/api/vision_results/)
+
+## 其它文档
+
+- [PaddleDetection 模型介绍](..)
+- [PaddleDetection C++部署](../cpp)
+- [模型预测结果说明](../../../../../docs/api/vision_results/)

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

@@ -0,0 +1,61 @@
+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 '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() == "gpu":
+        option.use_gpu()
+
+    if args.use_trt:
+        option.use_trt_backend()
+        option.set_trt_input_shape("image", [1, 3, 640, 640])
+        option.set_trt_input_shape("scale_factor", [1, 2])
+    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.FasterRCNN(
+    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)
+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")

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

@@ -0,0 +1,61 @@
+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 '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() == "gpu":
+        option.use_gpu()
+
+    if args.use_trt:
+        option.use_trt_backend()
+        option.set_trt_input_shape("image", [1, 3, 320, 320])
+        option.set_trt_input_shape("scale_factor", [1, 2])
+    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.PicoDet(
+    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)
+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")

examples/vision/detection/paddledetection/python/infer_ppyolo.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 '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() == "gpu":
+        option.use_gpu()
+
+    if args.use_trt:
+        option.use_trt_backend()
+        option.set_trt_input_shape("image", [1, 3, 640, 640])
+        option.set_trt_input_shape("scale_factor", [1, 2])
+    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.PPYOLO(
+    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)
+print(result)
+
+# 预测结果可视化
+vis_im = fd.vision.vis_detection(
+    im, result, score_threshold=0.5, score_threshold=0.5)
+cv2.imwrite("visualized_result.jpg", vis_im)
+print("Visualized result save in ./visualized_result.jpg")

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

@@ -0,0 +1,61 @@
+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 '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() == "gpu":
+        option.use_gpu()
+
+    if args.use_trt:
+        option.use_trt_backend()
+        option.set_trt_input_shape("image", [1, 3, 640, 640])
+        option.set_trt_input_shape("scale_factor", [1, 2])
+    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.PPYOLOE(
+    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)
+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")

examples/vision/detection/paddledetection/python/infer_yolov3.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 '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() == "gpu":
+        option.use_gpu()
+
+    if args.use_trt:
+        option.use_trt_backend()
+        option.set_trt_input_shape("image", [1, 3, 608, 608])
+        option.set_trt_input_shape("im_shape", [1, 2])
+        option.set_trt_input_shape("scale_factor", [1, 2])
+    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.YOLOv3(
+    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)
+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")

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

@@ -0,0 +1,61 @@
+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 '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() == "gpu":
+        option.use_gpu()
+
+    if args.use_trt:
+        option.use_trt_backend()
+        option.set_trt_input_shape("image", [1, 3, 640, 640])
+        option.set_trt_input_shape("scale_factor", [1, 2])
+    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.PaddleYOLOX(
+    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)
+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/runtime.py

@@ -81,6 +81,12 @@ class RuntimeOption:
     def disable_paddle_mkldnn(self):
         return self._option.disable_paddle_mkldnn()
 
+    def enable_paddle_log_info(self):
+        return self._option.enable_paddle_log_info()
+
+    def disable_paddle_log_info(self):
+        return self._option.disable_paddle_log_info()
+
     def set_paddle_mkldnn_cache_size(self, cache_size):
         return self._option.set_paddle_mkldnn_cache_size(cache_size)
 

fastdeploy/vision/__init__.py

@@ -14,11 +14,12 @@
 from __future__ import absolute_import
 
 from . import detection
+from . import classification
+
 from . import matting
 from . import facedet
 from . import faceid
 
-from . import ppcls
 from . import ppseg
 from . import evaluation
 from .visualize import *

fastdeploy/vision/visualize/__init__.py

@@ -17,9 +17,13 @@ import logging
 from ... import c_lib_wrap as C
 
 
-def vis_detection(im_data, det_result, line_size=1, font_size=0.5):
-    return C.vision.Visualize.vis_detection(im_data, det_result, line_size,
-                                            font_size)
+def vis_detection(im_data,
+                  det_result,
+                  score_threshold=0.0,
+                  line_size=1,
+                  font_size=0.5):
+    return C.vision.Visualize.vis_detection(
+        im_data, det_result, score_threshold, line_size, font_size)
 
 
 def vis_face_detection(im_data, face_det_result, line_size=1, font_size=0.5):