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121 lines
4.3 KiB
Markdown
121 lines
4.3 KiB
Markdown
English | [中文](../../../cn/quick_start/runtime/cpp.md)
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# C++ Inference
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Please check out the FastDeploy C++ deployment library is already in your environment. You can refer to [FastDeploy Installation](../../build_and_install/) to install the pre-compiled FastDeploy, or customize your installation.
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This document shows an inference sample on the CPU using the PaddleClas classification model MobileNetV2 as an example.
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## 1. Obtaining the Model
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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. Backend Configuration
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The following C++ code is saved as `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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When loading is complete, you can get the following output information indicating the initialized backend and the hardware devices.
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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. Prepare for CMakeLists.txt
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FastDeploy contains several dependencies, it is more complicated to compile directly with `g++` or a compiler, so we recommend to use cmake to compile and configure. The sample configuration is as follows.
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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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# Specify path to the fastdeploy library after downloading and unpacking
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option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
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include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
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# Add FastDeploy dependency headers
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include_directories(${FASTDEPLOY_INCS})
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add_executable(runtime_demo ${PROJECT_SOURCE_DIR}/infer_onnx_openvino.cc)
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# Add FastDeploy dependency libraries
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target_link_libraries(runtime_demo ${FASTDEPLOY_LIBS})
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```
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## 4. Compile executable program
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Open a terminal, go to the directory where `infer_paddle_onnxruntime.cc` and `CMakeLists.txt` are located, and then run:
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```bash
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cd examples/runtime/cpp
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mkdir build & cd build
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cmake .. -DFASTDEPLOY_INSTALL_DIR=$fastdeploy_cpp_sdk
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make -j
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```
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```fastdeploy_cpp_sdk``` is path to FastDeploy C++ deployment library.
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After compiling, you can get your results by running:
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```bash
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./runtime_demo
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```
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If `error while loading shared libraries: libxxx.so: cannot open shared object file: No such file...`is reported, it means that the path to FastDeploy is not found. You can re-execute the program after adding the FastDeploy library path to the environment variable by running the following command.
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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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This sample code is common on all platforms (Windows/Linux/Mac), but the compilation process is only supported on (Linux/Mac),while using msbuild to compile on Windows. Please refer to [FastDeploy C++ SDK on Windows](../../faq/use_sdk_on_windows.md).
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## Other Documents
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- [Runtime demos on different backends](../../../../examples/runtime/README.md)
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- [Switching hardware and backend for model inference](../../faq/how_to_change_backend.md)
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