save layer names in rt file

src/NetworkRT.cpp

@@ -461,6 +461,15 @@ IPlugin* PluginFactory::createPlugin(const char* layerName, const void* serialDa
         for(int i=0; i<3*2*r->num; i++)
             r->bias[i] = readBUF<dnnType>(buf);
 
+		// save classes names
+        r->classesNames.resize(r->classes);
+		for(int i=0; i<r->classes; i++) {
+            char tmp[YOLORT_CLASSNAME_W];
+			for(int j=0; j<YOLORT_CLASSNAME_W; j++)
+				tmp[j] = readBUF<char>(buf);
+            r->classesNames[i] = std::string(tmp);
+		}
+
         yolos[n_yolos++] = r;
         return r;
     } 

src/Yolo.cpp

@@ -25,6 +25,12 @@ Yolo::Yolo(Network *net, int classes, int num, std::string fname_weights) :
         readBinaryFile(fname_weights, 3*num*2, &bias_h, &bias_d, seek);
     }
 
+    // init default classes name
+    classesNames.clear();
+    for(int i=0; i<classes; i++) {
+        classesNames.push_back(std::to_string(i));
+    }
+
     // same
     output_dim.n = input_dim.n;
     output_dim.c = input_dim.c;

src/Yolo3Detection.cpp

@@ -38,6 +38,7 @@ bool Yolo3Detection::init(std::string tensor_path) {
         memcpy(yolo[i]->mask_h, yRT->mask, sizeof(dnnType)*num);
         memcpy(yolo[i]->bias_h, yRT->bias, sizeof(dnnType)*num*3*2);
         yolo[i]->input_dim = yolo[i]->output_dim = tk::dnn::dataDim_t(1, yRT->c, yRT->h, yRT->w);
+        yolo[i]->classesNames = yRT->classesNames;
     }
 
     dets = tk::dnn::Yolo::allocateDetections(tk::dnn::Yolo::MAX_DETECTIONS, classes);