更换文档检测模型

paddle_detection/deploy/fastdeploy/rockchip/rknpu2/cpp/CMakeLists.txt

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+CMAKE_MINIMUM_REQUIRED(VERSION 3.10)
+project(infer_demo)
+
+set(CMAKE_CXX_STANDARD 14)
+
+option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
+
+include(${FASTDEPLOY_INSTALL_DIR}/FastDeployConfig.cmake)
+include_directories(${FastDeploy_INCLUDE_DIRS})
+add_executable(infer_demo infer.cc)
+target_link_libraries(infer_demo ${FastDeploy_LIBS})

paddle_detection/deploy/fastdeploy/rockchip/rknpu2/cpp/README.md

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+[English](README.md) | 简体中文
+# PaddleDetection RKNPU2 C++部署示例
+
+本目录下用于展示PaddleDetection系列模型在RKNPU2上的部署，以下的部署过程以PPYOLOE为例子。
+
+## 1. 部署环境准备
+在部署前，需确认以下两个步骤:
+
+1. 软硬件环境满足要求
+2. 根据开发环境，下载预编译部署库或者从头编译FastDeploy仓库
+
+以上步骤请参考[RK2代NPU部署库编译](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/faq/rknpu2/rknpu2.md)实现
+
+## 2. 部署模型准备
+
+模型转换代码请参考[模型转换文档](../README.md)
+
+## 3. 运行部署示例
+
+```bash
+# 下载部署示例代码
+git clone https://github.com/PaddlePaddle/PaddleDetection.git
+cd PaddleDetection/deploy/fastdeploy/rockchip/rknpu2/cpp
+# 注意：如果当前分支找不到下面的fastdeploy测试代码，请切换到develop分支
+# git checkout develop
+
+# 编译部署示例
+mkdir build && cd build
+cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
+make -j8
+
+wget https://bj.bcebos.com/paddlehub/fastdeploy/rknpu2/ppyoloe_plus_crn_s_80e_coco.zip
+unzip ppyoloe_plus_crn_s_80e_coco.zip
+wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
+
+# 运行部署示例
+# CPU推理
+./infer_demo ./ppyoloe_plus_crn_s_80e_coco 000000014439.jpg 0
+# RKNPU2推理
+./infer_demo ./ppyoloe_plus_crn_s_80e_coco 000000014439.jpg 1
+```
+
+## 4. 更多指南
+RKNPU上对模型的输入要求是使用NHWC格式，且图片归一化操作会在转RKNN模型时，内嵌到模型中，因此我们在使用FastDeploy部署时，需要先调用DisableNormalizeAndPermute(C++)或`disable_normalize_and_permute(Python)，在预处理阶段禁用归一化以及数据格式的转换。
+
+- [Python部署](../python)
+- [转换PaddleDetection RKNN模型文档](../README.md)

paddle_detection/deploy/fastdeploy/rockchip/rknpu2/cpp/infer.cc

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+// 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"
+
+void ONNXInfer(const std::string& model_dir, const std::string& image_file) {
+  std::string model_file = model_dir + "/ppyoloe_plus_crn_s_80e_coco.onnx";
+  std::string params_file;
+  std::string config_file = model_dir + "/infer_cfg.yml";
+  auto option = fastdeploy::RuntimeOption();
+  option.UseCpu();
+  auto format = fastdeploy::ModelFormat::ONNX;
+
+  auto model = fastdeploy::vision::detection::PPYOLOE(
+      model_file, params_file, config_file, option, format);
+
+  fastdeploy::TimeCounter tc;
+  tc.Start();
+  auto im = cv::imread(image_file);
+  fastdeploy::vision::DetectionResult res;
+  if (!model.Predict(im, &res)) {
+    std::cerr << "Failed to predict." << std::endl;
+    return;
+  }
+  auto vis_im = fastdeploy::vision::VisDetection(im, res, 0.5);
+  tc.End();
+  tc.PrintInfo("PPDet in ONNX");
+
+  std::cout << res.Str() << std::endl;
+  cv::imwrite("infer_onnx.jpg", vis_im);
+  std::cout << "Visualized result saved in ./infer_onnx.jpg" << std::endl;
+}
+
+void RKNPU2Infer(const std::string& model_dir, const std::string& image_file) {
+  auto model_file =
+      model_dir + "/ppyoloe_plus_crn_s_80e_coco_rk3588_quantized.rknn";
+  auto params_file = "";
+  auto config_file = model_dir + "/infer_cfg.yml";
+
+  auto option = fastdeploy::RuntimeOption();
+  option.UseRKNPU2();
+
+  auto format = fastdeploy::ModelFormat::RKNN;
+
+  auto model = fastdeploy::vision::detection::PPYOLOE(
+      model_file, params_file, config_file, option, format);
+
+  model.GetPreprocessor().DisablePermute();
+  model.GetPreprocessor().DisableNormalize();
+  model.GetPostprocessor().ApplyNMS();
+
+  auto im = cv::imread(image_file);
+
+  fastdeploy::vision::DetectionResult res;
+  fastdeploy::TimeCounter tc;
+  tc.Start();
+  if (!model.Predict(&im, &res)) {
+    std::cerr << "Failed to predict." << std::endl;
+    return;
+  }
+  tc.End();
+  tc.PrintInfo("PPDet in RKNPU2");
+
+  std::cout << res.Str() << std::endl;
+  auto vis_im = fastdeploy::vision::VisDetection(im, res, 0.5);
+  cv::imwrite("infer_rknpu2.jpg", vis_im);
+  std::cout << "Visualized result saved in ./infer_rknpu2.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_demo ./model_dir ./test.jpeg"
+        << std::endl;
+    return -1;
+  }
+
+  if (std::atoi(argv[3]) == 0) {
+    ONNXInfer(argv[1], argv[2]);
+  } else if (std::atoi(argv[3]) == 1) {
+    RKNPU2Infer(argv[1], argv[2]);
+  }
+  return 0;
+}