Avoid copy of ONNX graph during import

modules/dnn/src/onnx/onnx_importer.cpp

@@ -112,7 +112,7 @@ protected:
     std::unique_ptr<ONNXLayerHandler> layerHandler;
     Net& dstNet;
 
-    opencv_onnx::GraphProto graph_proto;
+    opencv_onnx::GraphProto* graph_proto;
     std::string framework_name;
 
     std::map<std::string, Mat> constBlobs;
@@ -787,7 +787,7 @@ void ONNXImporter::setParamsDtype(LayerParams& layerParams, const opencv_onnx::N
 void ONNXImporter::populateNet()
 {
     CV_Assert(model_proto.has_graph());
-    graph_proto = model_proto.graph();
+    graph_proto = model_proto.mutable_graph();
 
     std::string framework_version;
     if (model_proto.has_producer_name())
@@ -799,25 +799,25 @@ void ONNXImporter::populateNet()
             << (model_proto.has_ir_version() ? cv::format(" v%d", (int)model_proto.ir_version()) : cv::String())
             << " model produced by '" << framework_name << "'"
             << (framework_version.empty() ? cv::String() : cv::format(":%s", framework_version.c_str()))
-            << ". Number of nodes = " << graph_proto.node_size()
+            << ". Number of nodes = " << graph_proto->node_size()
-            << ", initializers = " << graph_proto.initializer_size()
+            << ", initializers = " << graph_proto->initializer_size()
-            << ", inputs = " << graph_proto.input_size()
+            << ", inputs = " << graph_proto->input_size()
-            << ", outputs = " << graph_proto.output_size()
+            << ", outputs = " << graph_proto->output_size()
             );
 
     parseOperatorSet();
 
-    simplifySubgraphs(graph_proto);
+    simplifySubgraphs(*graph_proto);
 
-    const int layersSize = graph_proto.node_size();
+    const int layersSize = graph_proto->node_size();
     CV_LOG_DEBUG(NULL, "DNN/ONNX: graph simplified to " << layersSize << " nodes");
 
-    constBlobs = getGraphTensors(graph_proto);  // scan GraphProto.initializer
+    constBlobs = getGraphTensors(*graph_proto);  // scan GraphProto.initializer
     std::vector<String> netInputs;  // map with network inputs (without const blobs)
     // Add all the inputs shapes. It includes as constant blobs as network's inputs shapes.
-    for (int i = 0; i < graph_proto.input_size(); ++i)
+    for (int i = 0; i < graph_proto->input_size(); ++i)
     {
-        const opencv_onnx::ValueInfoProto& valueInfoProto = graph_proto.input(i);
+        const opencv_onnx::ValueInfoProto& valueInfoProto = graph_proto->input(i);
         CV_Assert(valueInfoProto.has_name());
         const std::string& name = valueInfoProto.name();
         CV_Assert(valueInfoProto.has_type());
@@ -873,26 +873,26 @@ void ONNXImporter::populateNet()
     }
 
     // dump outputs
-    for (int i = 0; i < graph_proto.output_size(); ++i)
+    for (int i = 0; i < graph_proto->output_size(); ++i)
     {
-        dumpValueInfoProto(i, graph_proto.output(i), "output");
+        dumpValueInfoProto(i, graph_proto->output(i), "output");
     }
 
     if (DNN_DIAGNOSTICS_RUN) {
         CV_LOG_INFO(NULL, "DNN/ONNX: start diagnostic run!");
-        layerHandler->fillRegistry(graph_proto);
+        layerHandler->fillRegistry(*graph_proto);
     }
 
     for(int li = 0; li < layersSize; li++)
     {
-        const opencv_onnx::NodeProto& node_proto = graph_proto.node(li);
+        const opencv_onnx::NodeProto& node_proto = graph_proto->node(li);
         handleNode(node_proto);
     }
 
     // register outputs
-    for (int i = 0; i < graph_proto.output_size(); ++i)
+    for (int i = 0; i < graph_proto->output_size(); ++i)
     {
-        const std::string& output_name = graph_proto.output(i).name();
+        const std::string& output_name = graph_proto->output(i).name();
         if (output_name.empty())
         {
             CV_LOG_ERROR(NULL, "DNN/ONNX: can't register output without name: " << i);
@@ -3180,9 +3180,9 @@ void ONNXImporter::parseLayerNorm(LayerParams& layerParams, const opencv_onnx::N
     {
         // remove from graph proto
         for (size_t i = 1; i < node_proto.output_size(); i++) {
-            for (int j = graph_proto.output_size() - 1; j >= 0; j--) {
+            for (int j = graph_proto->output_size() - 1; j >= 0; j--) {
-                if (graph_proto.output(j).name() == node_proto.output(i)) {
+                if (graph_proto->output(j).name() == node_proto.output(i)) {
-                    graph_proto.mutable_output()->DeleteSubrange(j, 1);
+                    graph_proto->mutable_output()->DeleteSubrange(j, 1);
                     break;
                 }
             }
@@ -3683,9 +3683,9 @@ void ONNXImporter::parseQEltwise(LayerParams& layerParams, const opencv_onnx::No
                 layerParams.type = "ScaleInt8";
                 layerParams.set("bias_term", op == "sum");
                 int axis = 1;
-                for (int i = 0; i < graph_proto.initializer_size(); i++)
+                for (int i = 0; i < graph_proto->initializer_size(); i++)
                 {
-                    opencv_onnx::TensorProto tensor_proto = graph_proto.initializer(i);
+                    opencv_onnx::TensorProto tensor_proto = graph_proto->initializer(i);
                     if (tensor_proto.name() == node_proto.input(constId))
                     {
                         axis = inpShape.size() - tensor_proto.dims_size();