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Add detection and segmentation examples for Ascend deployment

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This commit is contained in:
yunyaoXYY
2022-12-27 07:40:46 +00:00
parent 76a876406e
commit 090c3a68b4
27 changed files with 280 additions and 26 deletions
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74
examples/vision/detection/yolov6/cpp/infer_paddle_model.cc
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@@ -45,30 +45,30 @@ void CpuInfer(const std::string& model_dir, const std::string& image_file) {
std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
}
void KunlunXinInfer(const std::string& model_dir, const std::string& image_file) {
fastdeploy::RuntimeOption option;
option.UseKunlunXin();
auto model_file = model_dir + sep + "model.pdmodel";
auto params_file = model_dir + sep + "model.pdiparams";
auto model = fastdeploy::vision::detection::YOLOv6(model_file, params_file, option, fastdeploy::ModelFormat::PADDLE);
if (!model.Initialized()) {
std::cerr << "Failed to initialize." << std::endl;
return;
}
// void KunlunXinInfer(const std::string& model_dir, const std::string& image_file) {
// fastdeploy::RuntimeOption option;
// option.UseKunlunXin();
// auto model_file = model_dir + sep + "model.pdmodel";
// auto params_file = model_dir + sep + "model.pdiparams";
// auto model = fastdeploy::vision::detection::YOLOv6(model_file, params_file, option, fastdeploy::ModelFormat::PADDLE);
// if (!model.Initialized()) {
// std::cerr << "Failed to initialize." << std::endl;
// return;
// }
auto im = cv::imread(image_file);
// auto im = cv::imread(image_file);
fastdeploy::vision::DetectionResult res;
if (!model.Predict(&im, &res)) {
std::cerr << "Failed to predict." << std::endl;
return;
}
std::cout << res.Str() << std::endl;
// 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::VisDetection(im, res);
cv::imwrite("vis_result.jpg", vis_im);
std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
}
// auto vis_im = fastdeploy::vision::VisDetection(im, res);
// 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) {
fastdeploy::RuntimeOption option;
@@ -96,6 +96,32 @@ void GpuInfer(const std::string& model_dir, const std::string& image_file) {
std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
}
void AscendInfer(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";
fastdeploy::RuntimeOption option;
option.UseAscend();
auto model = fastdeploy::vision::detection::YOLOv6(
model_file, params_file, option, fastdeploy::ModelFormat::PADDLE);
if (!model.Initialized()) {
std::cerr << "Failed to initialize." << std::endl;
return;
}
auto im = cv::imread(image_file);
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::VisDetection(im, res);
cv::imwrite("vis_result.jpg", vis_im);
std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
}
int main(int argc, char* argv[]) {
if (argc < 4) {
@@ -113,7 +139,9 @@ int main(int argc, char* argv[]) {
} else if (std::atoi(argv[3]) == 1) {
GpuInfer(argv[1], argv[2]);
} else if (std::atoi(argv[3]) == 2) {
KunlunXinInfer(argv[1], argv[2]);
}
// KunlunXinInfer(argv[1], argv[2]);
} else if (std::atoi(argv[3]) == 3) {
AscendInfer(argv[1], argv[2]);
}
return 0;
}