add ir optimization args

deploy/cpp/demo/classifier.cpp

@@ -37,6 +37,7 @@ DEFINE_int32(batch_size, 1, "Batch size of infering");
 DEFINE_int32(thread_num,
              omp_get_num_procs(),
              "Number of preprocessing threads");
+DEFINE_bool(use_ir_optim, true, "use ir optimization");
 
 int main(int argc, char** argv) {
   // Parsing command-line
@@ -57,7 +58,8 @@ int main(int argc, char** argv) {
              FLAGS_use_gpu,
              FLAGS_use_trt,
              FLAGS_gpu_id,
-             FLAGS_key);
+             FLAGS_key,
+             FLAGS_use_ir_optim);
 
   // 进行预测
   double total_running_time_s = 0.0;

deploy/cpp/demo/detector.cpp

@@ -43,6 +43,7 @@ DEFINE_double(threshold,
 DEFINE_int32(thread_num,
              omp_get_num_procs(),
              "Number of preprocessing threads");
+DEFINE_bool(use_ir_optim, true, "use ir optimization");
 
 int main(int argc, char** argv) {
   // 解析命令行参数
@@ -62,7 +63,8 @@ int main(int argc, char** argv) {
              FLAGS_use_gpu,
              FLAGS_use_trt,
              FLAGS_gpu_id,
-             FLAGS_key);
+             FLAGS_key,
+             FLAGS_use_ir_optim);
 
   double total_running_time_s = 0.0;
   double total_imread_time_s = 0.0;

deploy/cpp/demo/segmenter.cpp

@@ -39,6 +39,7 @@ DEFINE_int32(batch_size, 1, "Batch size of infering");
 DEFINE_int32(thread_num,
              omp_get_num_procs(),
              "Number of preprocessing threads");
+DEFINE_bool(use_ir_optim, true, "use ir optimization");
 
 int main(int argc, char** argv) {
   // 解析命令行参数
@@ -59,7 +60,8 @@ int main(int argc, char** argv) {
              FLAGS_use_gpu,
              FLAGS_use_trt,
              FLAGS_gpu_id,
-             FLAGS_key);
+             FLAGS_key,
+             FLAGS_use_ir_optim);
 
   double total_running_time_s = 0.0;
   double total_imread_time_s = 0.0;

deploy/cpp/include/paddlex/paddlex.h

@@ -72,20 +72,23 @@ class Model {
    * @param use_trt: use Tensor RT or not when infering
    * @param gpu_id: the id of gpu when infering with using gpu
    * @param key: the key of encryption when using encrypted model
+   * @param use_ir_optim: use ir optimization when infering
    * */
   void Init(const std::string& model_dir,
             bool use_gpu = false,
             bool use_trt = false,
             int gpu_id = 0,
-            std::string key = "") {
-    create_predictor(model_dir, use_gpu, use_trt, gpu_id, key);
+            std::string key = "",
+            bool use_ir_optim = true) {
+    create_predictor(model_dir, use_gpu, use_trt, gpu_id, key, use_ir_optim);
   }
 
   void create_predictor(const std::string& model_dir,
                         bool use_gpu = false,
                         bool use_trt = false,
                         int gpu_id = 0,
-                        std::string key = "");
+                        std::string key = "",
+                        bool use_ir_optim = true);
 
   /*
    * @brief

deploy/cpp/src/paddlex.cpp

@@ -22,7 +22,8 @@ void Model::create_predictor(const std::string& model_dir,
                              bool use_gpu,
                              bool use_trt,
                              int gpu_id,
-                             std::string key) {
+                             std::string key,
+                             bool use_ir_optim) {
   paddle::AnalysisConfig config;
   std::string model_file = model_dir + OS_PATH_SEP + "__model__";
   std::string params_file = model_dir + OS_PATH_SEP + "__params__";
@@ -63,6 +64,8 @@ void Model::create_predictor(const std::string& model_dir,
   }
   config.SwitchUseFeedFetchOps(false);
   config.SwitchSpecifyInputNames(true);
+  // 开启图优化
+  config.SwitchIrOptim(use_ir_optim);
   // 开启内存优化
   config.EnableMemoryOptim();
   if (use_trt) {