[Other] Add Model Clone function for PaddleClas && PaddleDet && PaddleSeg (#791)

* Refactor PaddleSeg with preprocessor && postprocessor * Fix bugs * Delete redundancy code * Modify by comments * Refactor according to comments * Add batch evaluation * Add single test script * Add ppliteseg single test script && fix eval(raise) error * fix bug * Fix evaluation segmentation.py batch predict * Fix segmentation evaluation bug * Fix evaluation segmentation bugs * Update segmentation result docs * Update old predict api and DisableNormalizeAndPermute * Update resize segmentation label map with cv::INTER_NEAREST * Add Model Clone function for PaddleClas && PaddleDet && PaddleSeg * Add multi thread demo * Add python model clone function * Add multi thread python && C++ example * Fix bug Co-authored-by: Jason <jiangjiajun@baidu.com>
2025-12-24 13:28:13 +08:00 · 2022-12-13 15:19:47 +08:00
parent 6a1a3d001f
commit 9937b6c325
15 changed files with 455 additions and 3 deletions
--- a/tutorials/multi_thread.cc
+++ b/tutorials/multi_thread.cc
@@ -0,0 +1,132 @@
+#include <thread>
+#include "fastdeploy/vision.h"
+#ifdef WIN32
+const char sep = '\\';
+#else
+const char sep = '/';
+#endif
+
+void predict(fastdeploy::vision::classification::PaddleClasModel *model, int thread_id, const std::string& image_file) {
+  auto im = cv::imread(image_file);
+
+  fastdeploy::vision::ClassifyResult res;
+  if (!model->Predict(im, &res)) {
+    std::cerr << "Failed to predict." << std::endl;
+    return;
+  }
+
+  // print res
+  std::cout << "Thread Id: " << thread_id << std::endl;
+  std::cout << res.Str() << std::endl;
+}
+
+void CpuInfer(const std::string& model_dir, const std::string& image_file, int thread_num) {
+  auto model_file = model_dir + sep + "inference.pdmodel";
+  auto params_file = model_dir + sep + "inference.pdiparams";
+  auto config_file = model_dir + sep + "inference_cls.yaml";
+
+  auto option = fastdeploy::RuntimeOption();
+  option.UseCpu();
+  auto model = fastdeploy::vision::classification::PaddleClasModel(
+      model_file, params_file, config_file, option);
+  if (!model.Initialized()) {
+    std::cerr << "Failed to initialize." << std::endl;
+    return;
+  }
+
+  std::vector<decltype(model.Clone())> models;
+  for (int i = 0; i < thread_num; ++i) {
+    models.emplace_back(std::move(model.Clone()));
+  }
+
+  std::vector<std::thread> threads;
+  for (int i = 0; i < thread_num; ++i) {
+    threads.emplace_back(predict, models[i].get(), i, image_file);
+  }
+
+  for (int i = 0; i < thread_num; ++i) {
+    threads[i].join();
+  }
+}
+
+void GpuInfer(const std::string& model_dir, const std::string& image_file, int thread_num) {
+  auto model_file = model_dir + sep + "inference.pdmodel";
+  auto params_file = model_dir + sep + "inference.pdiparams";
+  auto config_file = model_dir + sep + "inference_cls.yaml";
+  auto option = fastdeploy::RuntimeOption();
+  option.UseGpu();
+  option.UsePaddleBackend();
+  auto model = fastdeploy::vision::classification::PaddleClasModel(
+      model_file, params_file, config_file, option);
+  if (!model.Initialized()) {
+    std::cerr << "Failed to initialize." << std::endl;
+    return;
+  }
+
+  std::vector<decltype(model.Clone())> models;
+  for (int i = 0; i < thread_num; ++i) {
+    models.emplace_back(std::move(model.Clone()));
+  }
+
+  std::vector<std::thread> threads;
+  for (int i = 0; i < thread_num; ++i) {
+    threads.emplace_back(predict, models[i].get(), i, image_file);
+  }
+
+  for (int i = 0; i < thread_num; ++i) {
+    threads[i].join();
+  }
+}
+
+void TrtInfer(const std::string& model_dir, const std::string& image_file, int thread_num) {
+  auto model_file = model_dir + sep + "inference.pdmodel";
+  auto params_file = model_dir + sep + "inference.pdiparams";
+  auto config_file = model_dir + sep + "inference_cls.yaml";
+  auto option = fastdeploy::RuntimeOption();
+  option.UseGpu();
+  option.UseTrtBackend();
+  // for model.Clone() must SetTrtInputShape first
+  option.SetTrtInputShape("inputs", {1, 3, 224, 224});
+  auto model = fastdeploy::vision::classification::PaddleClasModel(
+      model_file, params_file, config_file, option);
+  if (!model.Initialized()) {
+    std::cerr << "Failed to initialize." << std::endl;
+    return;
+  }
+
+  std::vector<decltype(model.Clone())> models;
+  for (int i = 0; i < thread_num; ++i) {
+    models.emplace_back(std::move(model.Clone()));
+  }
+
+  std::vector<std::thread> threads;
+  for (int i = 0; i < thread_num; ++i) {
+    threads.emplace_back(predict, models[i].get(), i, image_file);
+  }
+
+  for (int i = 0; i < thread_num; ++i) {
+    threads[i].join();
+  }
+}
+
+int main(int argc, char **argv) {
+  if (argc < 5) {
+    std::cout << "Usage: infer_demo path/to/model path/to/image run_option thread_num, "
+                 "e.g ./infer_demo ./ResNet50_vd ./test.jpeg 0 3"
+              << 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;
+  }
+
+  if (std::atoi(argv[3]) == 0) {
+    CpuInfer(argv[1], argv[2], std::atoi(argv[4]));
+  } else if (std::atoi(argv[3]) == 1) {
+    GpuInfer(argv[1], argv[2], std::atoi(argv[4]));
+  } else if (std::atoi(argv[3]) == 2) {
+    TrtInfer(argv[1], argv[2], std::atoi(argv[4]));
+  } 
+  return 0;
+}
+