merge

tests/darknet/yolo4_berkeley.cpp

@@ -17,7 +17,7 @@ int main() {
     std::string wgs_path  = bin_path + "/layers";
     std::string cfg_path  = "../tests/darknet/cfg/yolo4_berkeley.cfg";
     std::string name_path = "../tests/darknet/names/berkeley.names";
-    downloadWeightsifDoNotExist(input_bins[0], bin_path, "https://cloud.hipert.unimore.it/s//download");
+    downloadWeightsifDoNotExist(input_bins[0], bin_path, "https://cloud.hipert.unimore.it/s/nkWFa5fgb4NTdnB/download");
 
     // parse darknet network
     tk::dnn::Network *net = tk::dnn::darknetParser(cfg_path, wgs_path, name_path);

tests/mobilenet/bdd-mobilenetv2ssd/bdd-mobilenetv2ssd.cpp

@@ -134,7 +134,7 @@ const char *regression_header5 = "bdd-mobilenetv2ssd/layers/regression_headers-5
 int main()
 {
 
-    // downloadWeightsifDoNotExist(input_bin, "bdd-mobilenetv2ssd", "https://cloud.hipert.unimore.it/s//download");
+    downloadWeightsifDoNotExist(input_bin, "bdd-mobilenetv2ssd", "https://cloud.hipert.unimore.it/s/jzRBxcEJYJ99RLa/download");
 
     int classes = 11;
 

tests/test_rtinference/rtinference.cpp

@@ -30,7 +30,8 @@ int main(int argc, char *argv[]) {
     int ret_tensorrt = 0; 
     std::cout<<"Testing with batchsize: "<<BATCH_SIZE<<"\n";
     printCenteredTitle(" TENSORRT inference ", '=', 30); 
-    for(int i=0; i<10; i++) {
+    float total_time = 0;
+    for(int i=0; i<1200; i++) {
 
         // generate input
         for(int j=0; j<netRT.input_dim.tot(); j++) {
@@ -44,6 +45,7 @@ int main(int argc, char *argv[]) {
         TIMER_START
         netRT.infer(dim, input_d);
         TIMER_STOP
+        total_time+= t_ns;
 
         // control output
         std::cout<<"Output Buffers: "<<netRT.getBuffersN()-1<<"\n";
@@ -56,6 +58,6 @@ int main(int argc, char *argv[]) {
             }
         }
     }
-    
+    std::cout<<"avg: "<<total_time/1200.<<std::endl;
     return ret_tensorrt;
 }