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123 lines
4.1 KiB
Markdown
123 lines
4.1 KiB
Markdown
简体中文 ｜ [English](README.md)
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# C++推理
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在运行demo前，需确认以下两个步骤
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- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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- 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/cn/build_and_install/download_prebuilt_libraries.md)
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本文档以 PaddleClas 分类模型 MobileNetV2 为例展示CPU上的推理示例
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## 1. 获取模型
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```bash
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wget https://bj.bcebos.com/fastdeploy/models/mobilenetv2.tgz
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tar xvf mobilenetv2.tgz
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```
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## 2. 配置后端
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如下C++代码保存为`infer_paddle_onnxruntime.cc`
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``` c++
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#include "fastdeploy/runtime.h"
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namespace fd = fastdeploy;
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int main(int argc, char* argv[]) {
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std::string model_file = "mobilenetv2/inference.pdmodel";
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std::string params_file = "mobilenetv2/inference.pdiparams";
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// setup option
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fd::RuntimeOption runtime_option;
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runtime_option.SetModelPath(model_file, params_file, fd::ModelFormat::PADDLE);
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runtime_option.UseOrtBackend();
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runtime_option.SetCpuThreadNum(12);
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// init runtime
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std::unique_ptr<fd::Runtime> runtime =
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std::unique_ptr<fd::Runtime>(new fd::Runtime());
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if (!runtime->Init(runtime_option)) {
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std::cerr << "--- Init FastDeploy Runitme Failed! "
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<< "\n--- Model: " << model_file << std::endl;
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return -1;
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} else {
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std::cout << "--- Init FastDeploy Runitme Done! "
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<< "\n--- Model: " << model_file << std::endl;
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}
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// init input tensor shape
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fd::TensorInfo info = runtime->GetInputInfo(0);
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info.shape = {1, 3, 224, 224};
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std::vector<fd::FDTensor> input_tensors(1);
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std::vector<fd::FDTensor> output_tensors(1);
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std::vector<float> inputs_data;
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inputs_data.resize(1 * 3 * 224 * 224);
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for (size_t i = 0; i < inputs_data.size(); ++i) {
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inputs_data[i] = std::rand() % 1000 / 1000.0f;
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}
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input_tensors[0].SetExternalData({1, 3, 224, 224}, fd::FDDataType::FP32, inputs_data.data());
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//get input name
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input_tensors[0].name = info.name;
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runtime->Infer(input_tensors, &output_tensors);
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output_tensors[0].PrintInfo();
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return 0;
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}
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```
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加载完成，会输出提示如下，说明初始化的后端，以及运行的硬件设备
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```
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[INFO] fastdeploy/fastdeploy_runtime.cc(283)::Init Runtime initialized with Backend::OrtBackend in device Device::CPU.
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```
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## 3. 准备CMakeLists.txt
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FastDeploy中包含多个依赖库，直接采用`g++`或编译器编译较为繁杂，推荐使用cmake进行编译配置。示例配置如下，
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```cmake
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PROJECT(runtime_demo C CXX)
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CMAKE_MINIMUM_REQUIRED (VERSION 3.12)
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# 指定下载解压后的fastdeploy库路径
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option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
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include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
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# 添加FastDeploy依赖头文件
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include_directories(${FASTDEPLOY_INCS})
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add_executable(runtime_demo ${PROJECT_SOURCE_DIR}/infer_onnx_openvino.cc)
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# 添加FastDeploy库依赖
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target_link_libraries(runtime_demo ${FASTDEPLOY_LIBS})
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```
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## 4. 编译可执行程序
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打开命令行终端，进入`infer_paddle_onnxruntime.cc`和`CMakeLists.txt`所在的目录，执行如下命令
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```bash
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mkdir build & cd build
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cmake .. -DFASTDEPLOY_INSTALL_DIR=$fastdeploy_cpp_sdk
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make -j
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```
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```fastdeploy_cpp_sdk``` 为FastDeploy C++部署库路径
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编译完成后，使用如下命令执行可得到预测结果
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```bash
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./runtime_demo
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```
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执行时如提示`error while loading shared libraries: libxxx.so: cannot open shared object file: No such file...`，说明程序执行时没有找到FastDeploy的库路径，可通过执行如下命令，将FastDeploy的库路径添加到环境变量之后，重新执行二进制程序。
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```bash
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source /Path/to/fastdeploy_cpp_sdk/fastdeploy_init.sh
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```
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本示例代码在各平台(Windows/Linux/Mac)上通用，但编译过程仅支持(Linux/Mac)，Windows上使用msbuild进行编译，具体使用方式参考[Windows平台使用FastDeploy C++ SDK](../../../docs/cn/faq/use_sdk_on_windows.md)
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## 其它文档
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- [Runtime Python 示例](../python)
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- [切换模型推理的硬件和后端](../../../docs/cn/faq/how_to_change_backend.md)
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