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Merge pull request #25163 from dkurt:onnx_graph_ref

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Avoid copy of ONNX graph during import
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Alexander Smorkalov 2024-03-06 10:17:15 +03:00 committed by GitHub
commit 66ff71085e
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1 changed files with 22 additions and 22 deletions
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44
modules/dnn/src/onnx/onnx_importer.cpp
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@ -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()
<< ", initializers = " << graph_proto.initializer_size()
<< ", inputs = " << graph_proto.input_size()
<< ", outputs = " << graph_proto.output_size()
<< ". Number of nodes = " << graph_proto->node_size()
<< ", initializers = " << graph_proto->initializer_size()
<< ", inputs = " << graph_proto->input_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--) {
if (graph_proto.output(j).name() == node_proto.output(i)) {
graph_proto.mutable_output()->DeleteSubrange(j, 1);
for (int j = graph_proto->output_size() - 1; j >= 0; j--) {
if (graph_proto->output(j).name() == node_proto.output(i)) {
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();