Merge pull request #20494 from rogday:onnx_diagnostic_fix

fix ONNXImporter diagnostic mode layer registration issue

* fix layer registration, thread unsafe access and align the behavior of DNN_DIAGNOSTICS_RUN between onnx and tf importers

* move skipModelInput

* print all missing layers

* address TF issue

apps/model-diagnostics/model_diagnostics.cpp

@@ -4,6 +4,7 @@ USAGE:
 **************************************************/
 #include <opencv2/dnn.hpp>
 #include <opencv2/core/utils/filesystem.hpp>
+#include <opencv2/dnn/utils/debug_utils.hpp>
 
 #include <iostream>
 
@@ -57,6 +58,7 @@ int main( int argc, const char** argv )
     CV_Assert(!model.empty());
 
     enableModelDiagnostics(true);
+    skipModelImport(true);
     redirectError(diagnosticsErrorCallback, NULL);
 
     Net ocvNet = readNet(model, config, frameworkId);

modules/dnn/include/opencv2/dnn/layer_reg.private.hpp

@@ -12,10 +12,16 @@ CV__DNN_INLINE_NS_BEGIN
 //! @addtogroup dnn
 //! @{
 
-//! Register layer types of DNN model.
 typedef std::map<std::string, std::vector<LayerFactory::Constructor> > LayerFactory_Impl;
+
+//! Register layer types of DNN model.
+//!
+//! @note In order to thread-safely access the factory, see getLayerFactoryMutex() function.
 LayerFactory_Impl& getLayerFactoryImpl();
 
+//! Get the mutex guarding @ref LayerFactory_Impl, see getLayerFactoryImpl() function.
+Mutex& getLayerFactoryMutex();
+
 //! @}
 CV__DNN_INLINE_NS_END
 }

modules/dnn/include/opencv2/dnn/utils/debug_utils.hpp

@@ -0,0 +1,24 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#ifndef OPENCV_DNN_UTILS_DEBUG_UTILS_HPP
+#define OPENCV_DNN_UTILS_DEBUG_UTILS_HPP
+
+#include "../dnn.hpp"
+
+namespace cv { namespace dnn {
+CV__DNN_INLINE_NS_BEGIN
+
+/**
+ * @brief Skip model import after diagnostic run in readNet() functions.
+ * @param[in] skip Indicates whether to skip the import.
+ *
+ * This is an internal OpenCV function not intended for users.
+ */
+CV_EXPORTS void skipModelImport(bool skip);
+
+CV__DNN_INLINE_NS_END
+}} // namespace
+
+#endif // OPENCV_DNN_UTILS_DEBUG_UTILS_HPP

modules/dnn/src/debug_utils.cpp

@@ -0,0 +1,91 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#include "precomp.hpp"
+
+#include <sstream>
+
+#include <opencv2/dnn/layer_reg.private.hpp>
+#include <opencv2/dnn/utils/debug_utils.hpp>
+#include <opencv2/core/utils/logger.hpp>
+
+namespace cv { namespace dnn {
+CV__DNN_INLINE_NS_BEGIN
+
+bool DNN_DIAGNOSTICS_RUN = false;
+bool DNN_SKIP_REAL_IMPORT = false;
+
+void enableModelDiagnostics(bool isDiagnosticsMode)
+{
+    DNN_DIAGNOSTICS_RUN = isDiagnosticsMode;
+
+    if (DNN_DIAGNOSTICS_RUN)
+    {
+        detail::NotImplemented::Register();
+    }
+    else
+    {
+        detail::NotImplemented::unRegister();
+    }
+}
+
+void skipModelImport(bool skip)
+{
+    DNN_SKIP_REAL_IMPORT = skip;
+}
+
+void detail::LayerHandler::addMissing(const std::string& name, const std::string& type)
+{
+    cv::AutoLock lock(getLayerFactoryMutex());
+    auto& registeredLayers = getLayerFactoryImpl();
+
+    // If we didn't add it, but can create it, it's custom and not missing.
+    if (layers.find(type) == layers.end() && registeredLayers.find(type) != registeredLayers.end())
+    {
+        return;
+    }
+
+    layers[type].insert(name);
+}
+
+bool detail::LayerHandler::contains(const std::string& type) const
+{
+    return layers.find(type) != layers.end();
+}
+
+void detail::LayerHandler::printMissing()
+{
+    if (layers.empty())
+    {
+        return;
+    }
+
+    std::stringstream ss;
+    ss << "DNN: Not supported types:\n";
+    for (const auto& type_names : layers)
+    {
+        const auto& type = type_names.first;
+        ss << "Type='" << type << "', affected nodes:\n[";
+        for (const auto& name : type_names.second)
+        {
+            ss << "'" << name << "', ";
+        }
+        ss.seekp(-2, std::ios_base::end);
+        ss << "]\n";
+    }
+    CV_LOG_ERROR(NULL, ss.str());
+}
+
+LayerParams detail::LayerHandler::getNotImplementedParams(const std::string& name, const std::string& op)
+{
+    LayerParams lp;
+    lp.name = name;
+    lp.type = "NotImplemented";
+    lp.set("type", op);
+
+    return lp;
+}
+
+CV__DNN_INLINE_NS_END
+}} // namespace

modules/dnn/src/dnn.cpp

@@ -94,22 +94,6 @@ static bool DNN_CHECK_NAN_INF = utils::getConfigurationParameterBool("OPENCV_DNN
 static bool DNN_CHECK_NAN_INF_DUMP = utils::getConfigurationParameterBool("OPENCV_DNN_CHECK_NAN_INF_DUMP", false);
 static bool DNN_CHECK_NAN_INF_RAISE_ERROR = utils::getConfigurationParameterBool("OPENCV_DNN_CHECK_NAN_INF_RAISE_ERROR", false);
 
-bool DNN_DIAGNOSTICS_RUN = false;
-
-void enableModelDiagnostics(bool isDiagnosticsMode)
-{
-    DNN_DIAGNOSTICS_RUN = isDiagnosticsMode;
-
-    if (DNN_DIAGNOSTICS_RUN)
-    {
-        detail::NotImplemented::Register();
-    }
-    else
-    {
-        detail::NotImplemented::unRegister();
-    }
-}
-
 using std::vector;
 using std::map;
 using std::make_pair;
@@ -5662,7 +5646,7 @@ bool Layer::updateMemoryShapes(const std::vector<MatShape> &inputs)
 }
 //////////////////////////////////////////////////////////////////////////
 
-static Mutex& getLayerFactoryMutex()
+Mutex& getLayerFactoryMutex()
 {
     static Mutex* volatile instance = NULL;
     if (instance == NULL)

modules/dnn/src/dnn_common.hpp

@@ -5,6 +5,9 @@
 #ifndef __OPENCV_DNN_COMMON_HPP__
 #define __OPENCV_DNN_COMMON_HPP__
 
+#include <unordered_set>
+#include <unordered_map>
+
 #include <opencv2/dnn.hpp>
 
 namespace cv { namespace dnn {
@@ -13,6 +16,9 @@ CV__DNN_INLINE_NS_BEGIN
 Mutex& getInitializationMutex();
 void initializeLayerFactory();
 
+extern bool DNN_DIAGNOSTICS_RUN;
+extern bool DNN_SKIP_REAL_IMPORT;
+
 namespace detail {
 #define CALL_MEMBER_FN(object, ptrToMemFn)  ((object).*(ptrToMemFn))
 
@@ -25,6 +31,43 @@ public:
     static void unRegister();
 };
 
+template <typename Importer, typename ... Args>
+Net readNet(Args&& ... args)
+{
+    Net net;
+    Importer importer(net, std::forward<Args>(args)...);
+    return net;
+}
+
+template <typename Importer, typename ... Args>
+Net readNetDiagnostic(Args&& ... args)
+{
+    Net maybeDebugNet = readNet<Importer>(std::forward<Args>(args)...);
+    if (DNN_DIAGNOSTICS_RUN && !DNN_SKIP_REAL_IMPORT)
+    {
+        // if we just imported the net in diagnostic mode, disable it and import again
+        enableModelDiagnostics(false);
+        Net releaseNet = readNet<Importer>(std::forward<Args>(args)...);
+        enableModelDiagnostics(true);
+        return releaseNet;
+    }
+    return maybeDebugNet;
+}
+
+class LayerHandler
+{
+public:
+    void addMissing(const std::string& name, const std::string& type);
+    bool contains(const std::string& type) const;
+    void printMissing();
+
+protected:
+    LayerParams getNotImplementedParams(const std::string& name, const std::string& op);
+
+private:
+    std::unordered_map<std::string, std::unordered_set<std::string>> layers;
+};
+
 struct NetImplBase
 {
     const int networkId;  // network global identifier

modules/dnn/src/onnx/onnx_importer.cpp

@@ -41,6 +41,8 @@ CV__DNN_INLINE_NS_BEGIN
 
 extern bool DNN_DIAGNOSTICS_RUN;
 
+class ONNXLayerHandler;
+
 class ONNXImporter
 {
     opencv_onnx::ModelProto model_proto;
@@ -61,60 +63,16 @@ class ONNXImporter
     void addConstant(const std::string& name, const Mat& blob);
     void addLayer(LayerParams& layerParams,
                   const opencv_onnx::NodeProto& node_proto);
-    static const std::set<String>& getSupportedTypes();
 
 public:
-
+    ONNXImporter(Net& net, const char *onnxFile);
-    ONNXImporter(Net& net, const char *onnxFile)
+    ONNXImporter(Net& net, const char* buffer, size_t sizeBuffer);
-        : dstNet(net), utilNet(), dispatch(buildDispatchMap())
-    {
-        hasDynamicShapes = false;
-        CV_Assert(onnxFile);
-        CV_LOG_DEBUG(NULL, "DNN/ONNX: processing ONNX model from file: " << onnxFile);
-
-        std::fstream input(onnxFile, std::ios::in | std::ios::binary);
-        if (!input)
-        {
-            CV_Error(Error::StsBadArg, cv::format("Can't read ONNX file: %s", onnxFile));
-        }
-
-        if (!model_proto.ParseFromIstream(&input))
-        {
-            CV_Error(Error::StsUnsupportedFormat, cv::format("Failed to parse ONNX model: %s", onnxFile));
-        }
-
-        populateNet();
-    }
-
-    ONNXImporter(Net& net, const char* buffer, size_t sizeBuffer)
-        : dstNet(net), utilNet(), dispatch(buildDispatchMap())
-    {
-        hasDynamicShapes = false;
-        CV_LOG_DEBUG(NULL, "DNN/ONNX: processing in-memory ONNX model (" << sizeBuffer << " bytes)");
-
-        struct _Buf : public std::streambuf
-        {
-            _Buf(const char* buffer, size_t sizeBuffer)
-            {
-                char* p = const_cast<char*>(buffer);
-                setg(p, p, p + sizeBuffer);
-            }
-        };
-
-        _Buf buf(buffer, sizeBuffer);
-        std::istream input(&buf);
-
-        if (!model_proto.ParseFromIstream(&input))
-            CV_Error(Error::StsUnsupportedFormat, "Failed to parse onnx model from in-memory byte array.");
-
-        populateNet();
-    }
 
     void populateNet();
 
 protected:
+    std::unique_ptr<ONNXLayerHandler> layerHandler;
     Net& dstNet;
-    Net utilNet;
 
     opencv_onnx::GraphProto graph_proto;
     std::string framework_name;
@@ -131,9 +89,13 @@ protected:
     void handleNode(const opencv_onnx::NodeProto& node_proto);
 
 private:
+    friend class ONNXLayerHandler;
     typedef void (ONNXImporter::*ONNXImporterNodeParser)(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
     typedef std::map<std::string, ONNXImporterNodeParser> DispatchMap;
 
+    const DispatchMap dispatch;
+    static const DispatchMap buildDispatchMap();
+
     void parseMaxPool              (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
     void parseAveragePool          (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
     void parseReduce               (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
@@ -178,12 +140,84 @@ private:
     void parseSoftMax              (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
     void parseDetectionOutput      (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
     void parseCumSum               (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
-    void parseCustom               (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
 
-    const DispatchMap dispatch;
+    void parseCustomLayer          (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
-    static const DispatchMap buildDispatchMap();
 };
 
+class ONNXLayerHandler : public detail::LayerHandler
+{
+public:
+    explicit ONNXLayerHandler(ONNXImporter* importer_);
+
+    void fillRegistry(const opencv_onnx::GraphProto& net);
+
+protected:
+    ONNXImporter* importer;
+};
+
+ONNXLayerHandler::ONNXLayerHandler(ONNXImporter* importer_) : importer(importer_){}
+
+void ONNXLayerHandler::fillRegistry(const opencv_onnx::GraphProto &net)
+{
+    int layersSize = net.node_size();
+    for (int li = 0; li < layersSize; li++) {
+        const opencv_onnx::NodeProto &node_proto = net.node(li);
+        const std::string& name = node_proto.output(0);
+        const std::string& type = node_proto.op_type();
+        if (importer->dispatch.find(type) == importer->dispatch.end())
+        {
+            addMissing(name, type);
+        }
+    }
+    printMissing();
+}
+
+ONNXImporter::ONNXImporter(Net& net, const char *onnxFile)
+    : layerHandler(DNN_DIAGNOSTICS_RUN ?  new ONNXLayerHandler(this) : nullptr),
+        dstNet(net), dispatch(buildDispatchMap())
+{
+    hasDynamicShapes = false;
+    CV_Assert(onnxFile);
+    CV_LOG_DEBUG(NULL, "DNN/ONNX: processing ONNX model from file: " << onnxFile);
+
+    std::fstream input(onnxFile, std::ios::in | std::ios::binary);
+    if (!input)
+    {
+        CV_Error(Error::StsBadArg, cv::format("Can't read ONNX file: %s", onnxFile));
+    }
+
+    if (!model_proto.ParseFromIstream(&input))
+    {
+        CV_Error(Error::StsUnsupportedFormat, cv::format("Failed to parse ONNX model: %s", onnxFile));
+    }
+
+    populateNet();
+}
+
+ONNXImporter::ONNXImporter(Net& net, const char* buffer, size_t sizeBuffer)
+    : layerHandler(DNN_DIAGNOSTICS_RUN ?  new ONNXLayerHandler(this) : nullptr), dstNet(net), dispatch(buildDispatchMap())
+{
+    hasDynamicShapes = false;
+    CV_LOG_DEBUG(NULL, "DNN/ONNX: processing in-memory ONNX model (" << sizeBuffer << " bytes)");
+
+    struct _Buf : public std::streambuf
+            {
+        _Buf(const char* buffer, size_t sizeBuffer)
+        {
+            char* p = const_cast<char*>(buffer);
+            setg(p, p, p + sizeBuffer);
+        }
+            };
+
+    _Buf buf(buffer, sizeBuffer);
+    std::istream input(&buf);
+
+    if (!model_proto.ParseFromIstream(&input))
+        CV_Error(Error::StsUnsupportedFormat, "Failed to parse onnx model from in-memory byte array.");
+
+    populateNet();
+}
+
 inline void replaceLayerParam(LayerParams& layerParams, const String& oldKey, const String& newKey)
 {
     if (layerParams.has(oldKey)) {
@@ -422,11 +456,7 @@ Mat ONNXImporter::getBlob(const std::string& input_name)
 void ONNXImporter::addLayer(LayerParams& layerParams,
                             const opencv_onnx::NodeProto& node_proto)
 {
-    int id;
+    int id = dstNet.addLayer(layerParams.name, layerParams.type, layerParams);
-    if (DNN_DIAGNOSTICS_RUN)
-        id = utilNet.addLayer(layerParams.name, layerParams.type, layerParams);
-    else
-        id = dstNet.addLayer(layerParams.name, layerParams.type, layerParams);
     for (int i = 0; i < node_proto.output_size(); ++i)
     {
         layer_id.insert(std::make_pair(node_proto.output(i), LayerInfo(id, i)));
@@ -439,10 +469,7 @@ void ONNXImporter::addLayer(LayerParams& layerParams,
         const std::string& input_name = node_proto.input(j);
         IterLayerId_t layerId = layer_id.find(input_name);
         if (layerId != layer_id.end()) {
-            if (DNN_DIAGNOSTICS_RUN)
+            dstNet.connect(layerId->second.layerId, layerId->second.outputId, id, inpNum);
-                utilNet.connect(layerId->second.layerId, layerId->second.outputId, id, inpNum);
-            else
-                dstNet.connect(layerId->second.layerId, layerId->second.outputId, id, inpNum);
             ++inpNum;
             // Collect input shapes.
             IterShape_t shapeIt = outShapes.find(input_name);
@@ -451,11 +478,7 @@ void ONNXImporter::addLayer(LayerParams& layerParams,
         }
     }
     // Compute shape of output blob for this layer.
-    Ptr<Layer> layer;
+    Ptr<Layer> layer = dstNet.getLayer(id);  // FIXIT: avoid instantiation of layers during the import stage
-    if (DNN_DIAGNOSTICS_RUN)
-        layer = utilNet.getLayer(id);
-    else
-        layer = dstNet.getLayer(id);  // FIXIT: avoid instantiation of layers during the import stage
     layer->getMemoryShapes(layerInpShapes, 0, layerOutShapes, layerInternalShapes);
     for (int i = 0; i < node_proto.output_size() && i < (int)layerOutShapes.size(); ++i)
     {
@@ -532,35 +555,11 @@ void ONNXImporter::populateNet()
             layer_id.insert(std::make_pair(name, LayerInfo(0, netInputs.size() - 1)));
         }
     }
-    utilNet.setInputsNames(netInputs);
     dstNet.setInputsNames(netInputs);
 
     if (DNN_DIAGNOSTICS_RUN) {
-        auto &supportedTypes = getSupportedTypes();
-        for (int li = 0; li < layersSize; li++) {
-            const opencv_onnx::NodeProto &node_proto = graph_proto.node(li);
-            std::string name = node_proto.output(0);
-            std::string layer_type = node_proto.op_type();
-            auto registered = supportedTypes.find(layer_type);
-            if (registered == supportedTypes.end()) {
-                CV_LOG_ERROR(NULL, "DNN/ONNX: NOTE: Potential problem with creating node " << name<< " with type " << layer_type << ".\n Type "
-                    << layer_type << " IS NOT SUPPORTED!\n"
-                );
-            }
-        }
-        auto oldConstBlobs = constBlobs;
-        auto oldOutShapes = outShapes;
-        auto oldLayerId = layer_id;
         CV_LOG_INFO(NULL, "DNN/ONNX: start diagnostic run!");
-        for (int li = 0; li < layersSize; li++) {
+        layerHandler->fillRegistry(graph_proto);
-            const opencv_onnx::NodeProto &node_proto = graph_proto.node(li);
-            handleNode(node_proto);
-        }
-        CV_LOG_INFO(NULL, "DNN/ONNX: diagnostic run completed!");
-        constBlobs = oldConstBlobs;
-        outShapes = oldOutShapes;
-        layer_id = oldLayerId;
-        enableModelDiagnostics(false);
     }
 
     for(int li = 0; li < layersSize; li++)
@@ -569,83 +568,7 @@ void ONNXImporter::populateNet()
         handleNode(node_proto);
     }
 
-    CV_LOG_DEBUG(NULL, "DNN/ONNX: import completed!");
+    CV_LOG_DEBUG(NULL, (DNN_DIAGNOSTICS_RUN ? "DNN/ONNX: diagnostic run completed!" : "DNN/ONNX: import completed!"));
-}
-
-const std::set<String>& ONNXImporter::getSupportedTypes()
-{
-    static const std::set<String> layerTypes = {
-        "MaxPool",
-        "AveragePool",
-        "GlobalAveragePool",
-        "GlobalMaxPool",
-        "ReduceMean",
-        "ReduceSum",
-        "ReduceMax",
-        "Slice",
-        "Split",
-        "Add",
-        "Sum",
-        "Sub",
-        "Pow",
-        "Max",
-        "Neg",
-        "Constant",
-        "LSTM",
-        "GRU",
-        "ImageScaler",
-        "Clip",
-        "LeakyRelu",
-        "Relu",
-        "Elu",
-        "Tanh",
-        "PRelu",
-        "LRN",
-        "InstanceNormalization",
-        "BatchNormalization",
-        "Gemm",
-        "MatMul",
-        "Mul",
-        "Div",
-        "Conv",
-        "ConvTranspose",
-        "Transpose",
-        "Squeeze",
-        "Flatten",
-        "Unsqueeze",
-        "Expand",
-        "Reshape",
-        "Pad",
-        "Shape",
-        "Cast",
-        "ConstantOfShape",
-        "ConstantFill",
-        "Gather",
-        "Concat",
-        "Resize",
-        "Upsample",
-        "SoftMax",
-        "Softmax",
-        "LogSoftmax",
-        "DetectionOutput",
-        "Interp",
-        "CropAndResize",
-        "ROIPooling",
-        "PSROIPooling",
-        "ChannelsPReLU",
-        "Sigmoid",
-        "Swish",
-        "Mish",
-        "AbsVal",
-        "BNLL",
-        "MaxUnpool",
-        "Dropout",
-        "Identity",
-        "Crop",
-        "Normalize",
-        "CumSum"
-    };
-    return layerTypes;
 }
 
 void ONNXImporter::handleNode(const opencv_onnx::NodeProto& node_proto)
@@ -673,7 +596,7 @@ void ONNXImporter::handleNode(const opencv_onnx::NodeProto& node_proto)
         }
         else
         {
-            parseCustom(layerParams, node_proto);
+            parseCustomLayer(layerParams, node_proto);
         }
     }
     catch (const cv::Exception& e)
@@ -683,6 +606,7 @@ void ONNXImporter::handleNode(const opencv_onnx::NodeProto& node_proto)
             CV_LOG_ERROR(NULL, "DNN/ONNX: Potential problem during processing node with " << node_proto.input_size() << " inputs and " << node_proto.output_size() << " outputs: "
                     << cv::format("[%s]:(%s)", layer_type.c_str(), name.c_str()) << "\n" << e.msg
             );
+            cv::AutoLock lock(getLayerFactoryMutex());
             auto registeredLayers = getLayerFactoryImpl();
             if (registeredLayers.find(layerParams.type) != registeredLayers.end())
             {
@@ -1068,11 +992,7 @@ void ONNXImporter::parseBias(LayerParams& layerParams, const opencv_onnx::NodePr
                 constParams.name = layerParams.name + "/const";
                 constParams.type = "Const";
                 constParams.blobs.push_back((isSub ? -1 : 1) * blob);
-                int id;
+                int id = dstNet.addLayer(constParams.name, constParams.type, constParams);
-                if (DNN_DIAGNOSTICS_RUN)
-                    id = utilNet.addLayer(constParams.name, constParams.type, constParams);
-                else
-                    id = dstNet.addLayer(constParams.name, constParams.type, constParams);
                 layer_id.insert(std::make_pair(constParams.name, LayerInfo(id, 0)));
                 outShapes[constParams.name] = shape(blob);
 
@@ -1117,19 +1037,12 @@ void ONNXImporter::parseBias(LayerParams& layerParams, const opencv_onnx::NodePr
             powerParams.type = "Power";
             powerParams.set("scale", -1);
 
-            int id;
             //Create Power layer
-            if (DNN_DIAGNOSTICS_RUN)
+            int id = dstNet.addLayer(powerParams.name, powerParams.type, powerParams);
-                id = utilNet.addLayer(powerParams.name, powerParams.type, powerParams);
-            else
-                id = dstNet.addLayer(powerParams.name, powerParams.type, powerParams);
             //Connect to input
             IterLayerId_t layerId = layer_id.find(node_proto.input(1));
             CV_Assert(layerId != layer_id.end());
-            if (DNN_DIAGNOSTICS_RUN)
+            dstNet.connect(layerId->second.layerId, layerId->second.outputId, id, 0);
-                utilNet.connect(layerId->second.layerId, layerId->second.outputId, id, 0);
-            else
-                dstNet.connect(layerId->second.layerId, layerId->second.outputId, id, 0);
             //Add shape
             layer_id.insert(std::make_pair(powerParams.name, LayerInfo(id, 0)));
             outShapes[powerParams.name] = outShapes[node_proto.input(1)];
@@ -1404,18 +1317,11 @@ void ONNXImporter::parseInstanceNormalization(LayerParams& layerParams, const op
     layerParams.erase("epsilon");
 
     //Create MVN layer
-    int id;
+    int id = dstNet.addLayer(mvnParams.name, mvnParams.type, mvnParams);
-    if (DNN_DIAGNOSTICS_RUN)
-        id = utilNet.addLayer(mvnParams.name, mvnParams.type, mvnParams);
-    else
-        id = dstNet.addLayer(mvnParams.name, mvnParams.type, mvnParams);
     //Connect to input
     IterLayerId_t layerId = layer_id.find(node_proto.input(0));
     CV_Assert(layerId != layer_id.end());
-    if (DNN_DIAGNOSTICS_RUN)
+    dstNet.connect(layerId->second.layerId, layerId->second.outputId, id, 0);
-        utilNet.connect(layerId->second.layerId, layerId->second.outputId, id, 0);
-    else
-        dstNet.connect(layerId->second.layerId, layerId->second.outputId, id, 0);
     //Add shape
     layer_id.insert(std::make_pair(mvnParams.name, LayerInfo(id, 0)));
     outShapes[mvnParams.name] = outShapes[node_proto.input(0)];
@@ -1621,19 +1527,12 @@ void ONNXImporter::parseMul(LayerParams& layerParams, const opencv_onnx::NodePro
             powerParams.type = "Power";
             powerParams.set("power", -1);
 
-            int id;
             //Create Power layer
-            if (DNN_DIAGNOSTICS_RUN)
+            int id = dstNet.addLayer(powerParams.name, powerParams.type, powerParams);
-                id = utilNet.addLayer(powerParams.name, powerParams.type, powerParams);
-            else
-                id = dstNet.addLayer(powerParams.name, powerParams.type, powerParams);
             //Connect to input
             IterLayerId_t layerId = layer_id.find(node_proto.input(1));
             CV_Assert(layerId != layer_id.end());
-            if (DNN_DIAGNOSTICS_RUN)
+            dstNet.connect(layerId->second.layerId, layerId->second.outputId, id, 0);
-                utilNet.connect(layerId->second.layerId, layerId->second.outputId, id, 0);
-            else
-                dstNet.connect(layerId->second.layerId, layerId->second.outputId, id, 0);
             //Add shape
             layer_id.insert(std::make_pair(powerParams.name, LayerInfo(id, 0)));
             outShapes[powerParams.name] = outShapes[node_proto.input(1)];
@@ -2418,7 +2317,7 @@ void ONNXImporter::parseCumSum(LayerParams& layerParams, const opencv_onnx::Node
     addLayer(layerParams, node_proto);
 }
 
-void ONNXImporter::parseCustom(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
+void ONNXImporter::parseCustomLayer(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
 {
     for (int j = 0; j < node_proto.input_size(); j++) {
         if (layer_id.find(node_proto.input(j)) == layer_id.end())
@@ -2476,23 +2375,18 @@ const ONNXImporter::DispatchMap ONNXImporter::buildDispatchMap()
     dispatch["SoftMax"] = dispatch["LogSoftmax"] = &ONNXImporter::parseSoftMax;
     dispatch["DetectionOutput"] = &ONNXImporter::parseDetectionOutput;
     dispatch["CumSum"] = &ONNXImporter::parseCumSum;
-    dispatch["Custom"] = &ONNXImporter::parseCustom;
 
     return dispatch;
 }
 
 Net readNetFromONNX(const String& onnxFile)
 {
-    Net net;
+    return detail::readNetDiagnostic<ONNXImporter>(onnxFile.c_str());
-    ONNXImporter onnxImporter(net, onnxFile.c_str());
-    return net;
 }
 
 Net readNetFromONNX(const char* buffer, size_t sizeBuffer)
 {
-    Net net;
+    return detail::readNetDiagnostic<ONNXImporter>(buffer, sizeBuffer);
-    ONNXImporter onnxImporter(net, buffer, sizeBuffer);
-    return net;
 }
 
 Net readNetFromONNX(const std::vector<uchar>& buffer)

modules/dnn/src/tensorflow/tf_importer.cpp

@@ -507,7 +507,7 @@ void ExcludeLayer(tensorflow::GraphDef& net, const int layer_index, const int in
         net.mutable_node()->DeleteSubrange(layer_index, 1);
 }
 
-class LayerHandler;
+class TFLayerHandler;
 
 class TFImporter
 {
@@ -516,8 +516,7 @@ public:
     TFImporter(Net& net, const char *dataModel, size_t lenModel,
                const char *dataConfig = NULL, size_t lenConfig = 0);
 protected:
-    std::unique_ptr<LayerHandler> layerHandler;
+    std::unique_ptr<TFLayerHandler> layerHandler;
-    std::unique_ptr<Net> utilNet;
     Net& dstNet;
     void populateNet();
 
@@ -559,7 +558,7 @@ private:
     void addPermuteLayer(const int* order, const std::string& permName, Pin& inpId);
     void setPadding(LayerParams &layerParams, const tensorflow::NodeDef &layer, std::string& inputName, float value = 0.);
 
-    friend class LayerHandler;
+    friend class TFLayerHandler;
     typedef void (TFImporter::*TFImporterNodeParser)(tensorflow::GraphDef&, const tensorflow::NodeDef&, LayerParams&);
     typedef std::map<std::string, TFImporterNodeParser> DispatchMap;
 
@@ -625,18 +624,17 @@ void TFImporter::setPadding(LayerParams &layerParams, const tensorflow::NodeDef
     layerParams.set("pad_mode", "VALID");
 }
 
-class LayerHandler
+class TFLayerHandler : public detail::LayerHandler
 {
 public:
-    LayerHandler(TFImporter* importer_);
+    explicit TFLayerHandler(TFImporter* importer_);
-    ~LayerHandler() = default;
 
-    bool handleMissing(const opencv_tensorflow::NodeDef& layer);
+    void fillRegistry(const tensorflow::GraphDef& net);
-    void handleFailed(const opencv_tensorflow::NodeDef& layer);
+    bool handleMissing(const tensorflow::NodeDef& layer);
+    void handleFailed(const tensorflow::NodeDef& layer);
 
-private:
+protected:
     TFImporter* importer;
-    std::set<std::string> layers;
 };
 
 const TFImporter::DispatchMap TFImporter::buildDispatchMap()
@@ -2471,9 +2469,8 @@ void TFImporter::parseCustomLayer(tensorflow::GraphDef& net, const tensorflow::N
 }
 
 TFImporter::TFImporter(Net& net, const char *model, const char *config)
-    :  layerHandler(DNN_DIAGNOSTICS_RUN ?  new LayerHandler(this) : nullptr),
+    : layerHandler(DNN_DIAGNOSTICS_RUN ?  new TFLayerHandler(this) : nullptr),
-        utilNet(DNN_DIAGNOSTICS_RUN ?  new Net : nullptr),
+        dstNet(net), dispatch(buildDispatchMap())
-        dstNet(DNN_DIAGNOSTICS_RUN ? *utilNet : net), dispatch(buildDispatchMap())
 {
     if (model && model[0])
     {
@@ -2494,9 +2491,8 @@ TFImporter::TFImporter(
         const char *dataModel, size_t lenModel,
         const char *dataConfig, size_t lenConfig
 )
-    :  layerHandler(DNN_DIAGNOSTICS_RUN ?  new LayerHandler(this) : nullptr),
+    :  layerHandler(DNN_DIAGNOSTICS_RUN ?  new TFLayerHandler(this) : nullptr),
-       utilNet(DNN_DIAGNOSTICS_RUN ?  new Net : nullptr),
+       dstNet(net), dispatch(buildDispatchMap())
-       dstNet(DNN_DIAGNOSTICS_RUN ? *utilNet : net), dispatch(buildDispatchMap())
 {
     if (dataModel != NULL && lenModel > 0)
     {
@@ -2855,6 +2851,11 @@ void TFImporter::populateNet()
     addConstNodes(netBin, value_id, layers_to_ignore);
     addConstNodes(netTxt, value_id, layers_to_ignore);
 
+    if (DNN_DIAGNOSTICS_RUN) {
+        CV_LOG_INFO(NULL, "DNN/TF: start diagnostic run!");
+        layerHandler->fillRegistry(net);
+    }
+
     for (int li = 0; li < layersSize; li++)
     {
         const tensorflow::NodeDef& layer = net.node(li);
@@ -2873,7 +2874,7 @@ void TFImporter::populateNet()
         CV_Assert(!netInputsNames[i].empty());
     }
     dstNet.setInputsNames(netInputsNames);
-    CV_LOG_DEBUG(NULL, "DNN/TF: ===================== Import completed =====================");
+    CV_LOG_DEBUG(NULL, (DNN_DIAGNOSTICS_RUN? "DNN/TF: diagnostic run completed!" : "DNN/TF: import completed!"));
 }
 
 void TFImporter::addPermuteLayer(const int* order, const std::string& permName, Pin& inpId)
@@ -2933,60 +2934,60 @@ void TFImporter::parseNode(const tensorflow::NodeDef& layer)
     }
 }
 
-LayerHandler::LayerHandler(TFImporter* importer_) : importer(importer_) {}
+TFLayerHandler::TFLayerHandler(TFImporter* importer_) : importer(importer_) {}
 
-void LayerHandler::handleFailed(const opencv_tensorflow::NodeDef& layer)
+void TFLayerHandler::fillRegistry(const tensorflow::GraphDef& net)
 {
-    LayerParams lp;
+    for (int li = 0; li < net.node_size(); li++) {
-    lp.name = layer.name();
+        const tensorflow::NodeDef& layer = net.node(li);
-    lp.type = "NotImplemented";
+
-    lp.set("type", layer.op());
+        const std::string& name = layer.name();
+        const std::string& type = layer.op();
+        if (importer->dispatch.find(type) == importer->dispatch.end())
+        {
+            addMissing(name, type);
+        }
+    }
+    printMissing();
+};
+
+bool TFLayerHandler::handleMissing(const tensorflow::NodeDef& layer)
+{
+    bool unsupported = contains(layer.op());
+
+    if (unsupported)
+    {
+        handleFailed(layer);
+    }
+
+    return unsupported;
+}
+
+void TFLayerHandler::handleFailed(const tensorflow::NodeDef& layer)
+{
+    LayerParams lp = getNotImplementedParams(layer.name(), layer.op());
 
     // the layer will be created or its params and type will be replaced
-    int id = importer->dstNet.addLayer(lp.name, "NotImplemented", lp);
+    int id = importer->dstNet.addLayer(lp.name, lp.type, lp);
     if (id != -1) // internal layer failure before the call to addLayer()
     {
         importer->layer_id[lp.name] = id;
     }
 }
 
-bool LayerHandler::handleMissing(const opencv_tensorflow::NodeDef& layer)
-{
-    LayerParams lp;
-    // If we didn't add it, but can create it, it's custom and not missing.
-    if (layers.find(layer.op()) == layers.end() && LayerFactory::createLayerInstance(layer.op(), lp))
-    {
-        return false;
-    }
-
-    if (layers.insert(layer.op()).second)
-    {
-        CV_LOG_ERROR(NULL, "DNN/TF: Node='" << layer.name() << "' of type='"<< layer.op()
-                                            << "' is not supported. This error won't be displayed again.");
-    }
-
-    handleFailed(layer);
-
-    return true;
-}
-
 } // namespace
 
 #endif //HAVE_PROTOBUF
 
 Net readNetFromTensorflow(const String &model, const String &config)
 {
-    Net net;
+    return detail::readNetDiagnostic<TFImporter>(model.c_str(), config.c_str());
-    TFImporter importer(net, model.c_str(), config.c_str());
-    return net;
 }
 
 Net readNetFromTensorflow(const char* bufferModel, size_t lenModel,
                           const char* bufferConfig, size_t lenConfig)
 {
-    Net net;
+    return detail::readNetDiagnostic<TFImporter>(bufferModel, lenModel, bufferConfig, lenConfig);
-    TFImporter importer(net, bufferModel, lenModel, bufferConfig, lenConfig);
-    return net;
 }
 
 Net readNetFromTensorflow(const std::vector<uchar>& bufferModel, const std::vector<uchar>& bufferConfig)

modules/dnn/test/test_tf_importer.cpp

@@ -13,6 +13,7 @@ Test for Tensorflow models loading
 #include "npy_blob.hpp"
 
 #include <opencv2/dnn/layer.details.hpp>  // CV_DNN_REGISTER_LAYER_CLASS
+#include <opencv2/dnn/utils/debug_utils.hpp>
 
 namespace opencv_test
 {
@@ -605,11 +606,13 @@ public:
     Test_TensorFlow_diagnostics()
     {
         enableModelDiagnostics(true);
+        skipModelImport(true);
     }
 
     ~Test_TensorFlow_diagnostics()
     {
         enableModelDiagnostics(false);
+        skipModelImport(false);
     }
 
     void runFailingTensorFlowNet(const std::string& prefix, bool hasText = false)