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https://github.com/PaddlePaddle/FastDeploy.git
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Add detection and segmentation examples for Ascend deployment
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@@ -130,6 +130,35 @@ void KunlunXinInfer(const std::string& model_dir, const std::string& image_file)
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std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
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}
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void AscendInfer(const std::string& model_dir, const std::string& image_file) {
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auto model_file = model_dir + sep + "model.pdmodel";
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auto params_file = model_dir + sep + "model.pdiparams";
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fastdeploy::RuntimeOption option;
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option.UseAscend();
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auto model = fastdeploy::vision::detection::YOLOv5(
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model_file, params_file, option, fastdeploy::ModelFormat::PADDLE);
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if (!model.Initialized()) {
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std::cerr << "Failed to initialize." << std::endl;
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return;
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}
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auto im = cv::imread(image_file);
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fastdeploy::vision::DetectionResult res;
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if (!model.Predict(im, &res)) {
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std::cerr << "Failed to predict." << std::endl;
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return;
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}
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std::cout << res.Str() << std::endl;
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auto vis_im = fastdeploy::vision::VisDetection(im, res);
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cv::imwrite("vis_result.jpg", vis_im);
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std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
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}
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int main(int argc, char* argv[]) {
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if (argc < 4) {
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std::cout << "Usage: infer_demo path/to/model path/to/image run_option, "
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@@ -149,6 +178,8 @@ int main(int argc, char* argv[]) {
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TrtInfer(argv[1], argv[2]);
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} else if (std::atoi(argv[3]) == 3) {
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KunlunXinInfer(argv[1], argv[2]);
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}
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} else if (std::atoi(argv[3]) == 4) {
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AscendInfer(argv[1], argv[2]);
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}
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return 0;
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}
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