mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-08 10:00:29 +08:00
1135d33dd7
* 第一次提交 * 补充一处漏翻译 * deleted: docs/en/quantize.md * Update one translation * Update en version * Update one translation in code * Standardize one writing * Standardize one writing * Update some en version * Fix a grammer problem * Update en version for api/vision result * Merge branch 'develop' of https://github.com/charl-u/FastDeploy into develop * Checkout the link in README in vision_results/ to the en documents * Modify a title * Add link to serving/docs/ * Finish translation of demo.md * Update english version of serving/docs/ * Update title of readme * Update some links * Modify a title * Update some links * Update en version of java android README * Modify some titles * Modify some titles * Modify some titles * modify article to document * update some english version of documents in examples * Add english version of documents in examples/visions * Sync to current branch * Add english version of documents in examples
123 lines
4.5 KiB
Markdown
123 lines
4.5 KiB
Markdown
English | [简体中文](README_CN.md)
|
|
# C++推理
|
|
|
|
Before running demo, the following two steps need to be confirmed:
|
|
|
|
- 1. Hardware and software environment meets the requirements. Please refer to [Environment requirements for FastDeploy](../../../docs/en/build_and_install/download_prebuilt_libraries.md).
|
|
- 2. Download pre-compiled libraries and samples according to the development environment. Please refer to [FastDeploy pre-compiled libraries](../../../docs/cn/build_and_install/download_prebuilt_libraries.md).
|
|
|
|
This document shows an inference example on the CPU using the PaddleClas classification model MobileNetV2 as an example.
|
|
|
|
## 1. Obtaining the Model
|
|
|
|
```bash
|
|
wget https://bj.bcebos.com/fastdeploy/models/mobilenetv2.tgz
|
|
tar xvf mobilenetv2.tgz
|
|
```
|
|
|
|
## 2. Backend Configuration
|
|
|
|
The following C++ code is saved as `infer_paddle_onnxruntime.cc`.
|
|
|
|
``` c++
|
|
#include "fastdeploy/runtime.h"
|
|
|
|
namespace fd = fastdeploy;
|
|
|
|
int main(int argc, char* argv[]) {
|
|
std::string model_file = "mobilenetv2/inference.pdmodel";
|
|
std::string params_file = "mobilenetv2/inference.pdiparams";
|
|
|
|
// setup option
|
|
fd::RuntimeOption runtime_option;
|
|
runtime_option.SetModelPath(model_file, params_file, fd::ModelFormat::PADDLE);
|
|
runtime_option.UseOrtBackend();
|
|
runtime_option.SetCpuThreadNum(12);
|
|
// init runtime
|
|
std::unique_ptr<fd::Runtime> runtime =
|
|
std::unique_ptr<fd::Runtime>(new fd::Runtime());
|
|
if (!runtime->Init(runtime_option)) {
|
|
std::cerr << "--- Init FastDeploy Runitme Failed! "
|
|
<< "\n--- Model: " << model_file << std::endl;
|
|
return -1;
|
|
} else {
|
|
std::cout << "--- Init FastDeploy Runitme Done! "
|
|
<< "\n--- Model: " << model_file << std::endl;
|
|
}
|
|
// init input tensor shape
|
|
fd::TensorInfo info = runtime->GetInputInfo(0);
|
|
info.shape = {1, 3, 224, 224};
|
|
|
|
std::vector<fd::FDTensor> input_tensors(1);
|
|
std::vector<fd::FDTensor> output_tensors(1);
|
|
|
|
std::vector<float> inputs_data;
|
|
inputs_data.resize(1 * 3 * 224 * 224);
|
|
for (size_t i = 0; i < inputs_data.size(); ++i) {
|
|
inputs_data[i] = std::rand() % 1000 / 1000.0f;
|
|
}
|
|
input_tensors[0].SetExternalData({1, 3, 224, 224}, fd::FDDataType::FP32, inputs_data.data());
|
|
|
|
//get input name
|
|
input_tensors[0].name = info.name;
|
|
|
|
runtime->Infer(input_tensors, &output_tensors);
|
|
|
|
output_tensors[0].PrintInfo();
|
|
return 0;
|
|
}
|
|
```
|
|
When loading is complete, the following prompt will be output, indicating the initialized backend, and the running hardware devices.
|
|
```
|
|
[INFO] fastdeploy/fastdeploy_runtime.cc(283)::Init Runtime initialized with Backend::OrtBackend in device Device::CPU.
|
|
```
|
|
|
|
## 3. Prepare for CMakeLists.txt
|
|
|
|
FastDeploy contains several dependencies, it is complicated to compile directly with `g++` or compiler, so we recommend using cmake for compiling configuration. The sample configuration is as follows:
|
|
|
|
```cmake
|
|
PROJECT(runtime_demo C CXX)
|
|
CMAKE_MINIMUM_REQUIRED (VERSION 3.12)
|
|
|
|
# Specify the path to the fastdeploy library after downloading and unpacking.
|
|
option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
|
|
|
|
include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
|
|
|
|
# Add FastDeploy dependency headers.
|
|
include_directories(${FASTDEPLOY_INCS})
|
|
|
|
add_executable(runtime_demo ${PROJECT_SOURCE_DIR}/infer_onnx_openvino.cc)
|
|
# Adding FastDeploy library dependencies.
|
|
target_link_libraries(runtime_demo ${FASTDEPLOY_LIBS})
|
|
```
|
|
|
|
## 4. Compile executable program
|
|
|
|
Open the terminal, go to the directory where `infer_paddle_onnxruntime.cc` and `CMakeLists.txt` are located, and run the following command:
|
|
|
|
```bash
|
|
mkdir build & cd build
|
|
cmake .. -DFASTDEPLOY_INSTALL_DIR=$fastdeploy_cpp_sdk
|
|
make -j
|
|
```
|
|
|
|
```fastdeploy_cpp_sdk``` is path to FastDeploy C++ deployment libraries.
|
|
|
|
After compiling, run the following command and get the results.
|
|
```bash
|
|
./runtime_demo
|
|
```
|
|
If you are prompted with `error while loading shared libraries: libxxx.so: cannot open shared object file: No such file... `, it means that the path to FastDeploy libraries is not found, you can run the program again after adding the path to the environment variable by executing the following command.
|
|
```bash
|
|
source /Path/to/fastdeploy_cpp_sdk/fastdeploy_init.sh
|
|
```
|
|
|
|
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](../../../docs/en/faq/use_sdk_on_windows.md).
|
|
|
|
## Other Documents
|
|
|
|
- [A Python example for Runtime](../python)
|
|
- [Switching hardware and backend for model inference](../../../docs/en/faq/how_to_change_backend.md)
|