FastDeploy
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Published
20 hours ago •
PaddlePaddle
PaddlePaddle
yaml文件加载失败（Failed to load yaml file , maybe you should check this file.）
环境
- 【FastDeploy版本】： develop分支
- 【编译命令】
cmake .. -G "Visual Studio 17 2022" -A x64 -DENABLE_ORT_BACKEND=ON -DENABLE_TRT_BACKEND=ON -DENABLE_VISION=ON -DWITH_GPU=ON -DTRT_DIRECTORY="D:\Downloads\TensorRT-8.5.3.1.Windows10.x86_64.cuda-11.8.cudnn8.6\TensorRT-8.5.3.1" -DCUDA_DIRECTORY="D:\NVIDIA GPU Computing Toolkit\CUDA\v11.6" -DCMAKE_INSTALL_PREFIX="E:\cplus\compiled_fastdeploy" -DOPENCV_DIRECTORY="E:\cplus\opencv-win-x64-3.4.16\build" -DORT_DIRECTORY="E:\cplus\onnxruntime-win-x64-gpu-1.12.0" - 【系统平台】: Windows x64(Windows11)
- 【硬件】： Nvidia GPU 3060， CUDA 11.6 CUDNN 8.6
- 【编译语言】： C++
- 【模型跑不通】
根据 https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/faq/use_sdk_on_windows_build.md中的3.3 SDK使用方式三Visual Studio 2019 创建 CMake 工程使用 C++ SDK 来编译infer_ppyoloe.cpp源文件的，编译也成功了，得到了exe可执行文件：
模型权值文件也下载了：
infer_ppyoloe.cpp源文件代码如下，我做了稍微的修改(主要是判断文件是否存在)：
#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;
}
std::cout << res.Str() << std::endl;
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;
}
std::cout << res.Str() << std::endl;
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";*/
const std::string model_file = "E:/cplus/fastdeploy_test/infer_ppyoloe/out/build/x64-debug/Debug/ppyoloe_crn_l_300e_coco/model.pdmodel";
const std::string params_file = "E:/cplus/fastdeploy_test/infer_ppyoloe/out/build/x64-debug/Debug/ppyoloe_crn_l_300e_coco/model.pdiparams";
const std::string config_file = "E:/cplus/fastdeploy_test/infer_ppyoloe/out/build/x64-debug/Debug/ppyoloe_crn_l_300e_coco/infer_cfg.yml";
std::ifstream fin(config_file);
if (!fin) {
std::cout << config_file + " no existed." << std::endl;
}
else {
std::cout << config_file + " existed." << std::endl;
}
auto option = fastdeploy::RuntimeOption();
option.UseGpu();
option.UseTrtBackend();
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;
}
std::cout << res.Str() << std::endl;
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;
}
static void check_cfg(const std::string& model_dir, const std::string& image_file) {
auto config_file = model_dir + sep + "infer_cfg.yml";
std::ifstream fin(config_file);
if (!fin) {
std::cout << config_file + " no existed." << std::endl;
}
else {
std::cout << config_file + " existed." << std::endl;
}
std::ifstream fin2(image_file);
if (!fin2) {
std::cout << image_file + " no existed." << std::endl;
}
else {
std::cout << image_file + " existed." << 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;
}
check_cfg(argv[1], argv[2]);
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;
}
然后运行exe，报**Failed to load yaml file , maybe you should check this file.**错误，运行命令和结果如下：
我明明判断了yaml文件是否存在，显示结果是存在的，但fastdeploySDK说不存在，我也看了fastdeploy的源代码
触发异常的就是上的代码，但是我明明自己判断文件是存在的，但为什么fastdeploySDK判断文件却不存在。这到底是为啥？
请求大佬指教！
这就是yaml文件，文件是从 https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_300e_coco.tgz中下载的
我没有改过这个yaml文件，内容原汁原味🫥
也可以试试将路径改成 "E:\cplus\fastdeploy_test\infer_ppyoloe\out\build\x64-debug\Debug\ppyoloe_crn_l_300e_coco\infer_cfg.yml"
请问您解决了吗，我也遇到了这个问题，而且我训练了两个模型，用的是同一个框架，一个导出后做推理没问题，另一个却一直报错说failed to load yaml
