opencv/modules/dnn/src/onnx/onnx_importer.cpp

// 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.

// Copyright (C) 2018, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.

#include "../precomp.hpp"
#include <opencv2/dnn/shape_utils.hpp>

#include <opencv2/dnn/layer_reg.private.hpp>

#include <opencv2/core/utils/logger.defines.hpp>
#undef CV_LOG_STRIP_LEVEL
#define CV_LOG_STRIP_LEVEL CV_LOG_LEVEL_DEBUG + 1
#include <opencv2/core/utils/logger.hpp>

#ifdef HAVE_PROTOBUF

#include <iostream>
#include <fstream>
#include <string>
#include <limits>
#include <algorithm>


#if defined(__GNUC__) && __GNUC__ >= 5
#pragma GCC diagnostic push
#pragma GCC diagnostic ignored "-Wsuggest-override"
#endif
#include "opencv-onnx.pb.h"
#if defined(__GNUC__) && __GNUC__ >= 5
#pragma GCC diagnostic pop
#endif

#include "onnx_graph_simplifier.hpp"

namespace cv {
namespace dnn {
CV__DNN_INLINE_NS_BEGIN

extern bool DNN_DIAGNOSTICS_RUN;

class ONNXLayerHandler;

class ONNXImporter
{
    opencv_onnx::ModelProto model_proto;
    struct LayerInfo {
        int layerId;
        int outputId;
        LayerInfo(int _layerId = 0, int _outputId = 0) : layerId(_layerId), outputId(_outputId) {}
    };

    std::map<std::string, Mat> getGraphTensors(
                                    const opencv_onnx::GraphProto& graph_proto);
    Mat getBlob(const opencv_onnx::NodeProto& node_proto, int index);
    Mat getBlob(const std::string& input_name);

    LayerParams getLayerParams(const opencv_onnx::NodeProto& node_proto);

    void addConstant(const std::string& name, const Mat& blob);
    void addLayer(LayerParams& layerParams,
                  const opencv_onnx::NodeProto& node_proto);
    void handleQuantizedNode(LayerParams& layerParams,
                             const opencv_onnx::NodeProto& node_proto);

    void expandMid(const std::string& prefix, opencv_onnx::NodeProto& node_proto,
                   const std::string& input, size_t n);
    void addNegation(const LayerParams& layerParams, opencv_onnx::NodeProto& node_proto, int input_id);
public:
    ONNXImporter(Net& net, const char *onnxFile);
    ONNXImporter(Net& net, const char* buffer, size_t sizeBuffer);

    void populateNet();

protected:
    std::unique_ptr<ONNXLayerHandler> layerHandler;
    Net& dstNet;

    opencv_onnx::GraphProto graph_proto;
    std::string framework_name;

    std::map<std::string, Mat> constBlobs;

    std::map<std::string, MatShape> outShapes;  // List of internal blobs shapes.
    bool hasDynamicShapes;  // Whether the model has inputs with dynamic shapes
    typedef std::map<std::string, MatShape>::iterator IterShape_t;

    std::map<std::string, LayerInfo> layer_id;
    typedef std::map<std::string, LayerInfo>::iterator IterLayerId_t;

    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 parseArg                  (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
    void parseMaxUnpool            (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
    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);
    void parseSlice                (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
    void parseSplit                (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
    void parseBias                 (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
    void parsePow                  (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
    void parseMinMax               (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
    void parseNeg                  (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
    void parseConstant             (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
    void parseLSTM                 (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
    void parseGRU                  (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
    void parseImageScaler          (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
    void parseClip                 (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
    void parseLeakyRelu            (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
    void parseRelu                 (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
    void parseElu                  (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
    void parseTanh                 (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
    void parseAbs                  (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
    void parseCompare              (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
    void parsePRelu                (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
    void parseLRN                  (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
    void parseInstanceNormalization(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
    void parseBatchNormalization   (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
    void parseGemm                 (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
    void parseMatMul               (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
    void parseMul                  (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
    void parseConv                 (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
    void parseConvTranspose        (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
    void parseTranspose            (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
    void parseSqueeze              (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
    void parseFlatten              (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
    void parseUnsqueeze            (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
    void parseExpand               (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
    void parseReshape              (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
    void parsePad                  (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
    void parseShape                (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
    void parseCast                 (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
    void parseConstantFill         (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
    void parseGather               (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
    void parseConcat               (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
    void parseResize               (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
    void parseUpsample             (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
    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 parseQuantDequant         (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
    void parseQConv                (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
    void parseQMatMul              (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
    void parseQEltwise             (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
    void parseQLeakyRelu           (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
    void parseQSigmoid             (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
    void parseQAvgPool             (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
    void parseQConcat              (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);

    void parseCustomLayer          (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
};

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)) {
        layerParams.set(newKey, layerParams.get(oldKey));
        layerParams.erase(oldKey);
    }
}

void releaseONNXTensor(opencv_onnx::TensorProto& tensor_proto)
{
    if (!tensor_proto.raw_data().empty()) {
        delete tensor_proto.release_raw_data();
    }
}

void runLayer(LayerParams& params, const std::vector<Mat>& inputs,
              std::vector<Mat>& outputs)
{
    Ptr<Layer> layer = LayerFactory::createLayerInstance(params.type, params);
    CV_Assert((bool)layer);

    std::vector<MatShape> inpShapes(inputs.size());
    int ddepth = params.get<int>("depth", CV_32F);
    for (size_t i = 0; i < inputs.size(); ++i)
    {
        inpShapes[i] = shape(inputs[i]);
        if (i > 0 && ddepth != inputs[i].depth())
            CV_Error(Error::StsNotImplemented, "Mixed input data types.");
        ddepth = inputs[i].depth();
    }

    std::vector<MatShape> outShapes, internalShapes;
    layer->getMemoryShapes(inpShapes, 0, outShapes, internalShapes);

    std::vector<Mat> internals(internalShapes.size());
    outputs.resize(outShapes.size());
    for (size_t i = 0; i < outShapes.size(); ++i)
        outputs[i].create(outShapes[i], ddepth);
    for (size_t i = 0; i < internalShapes.size(); ++i)
        internals[i].create(internalShapes[i], ddepth);

    layer->finalize(inputs, outputs);
    layer->forward(inputs, outputs, internals);
}

std::map<std::string, Mat> ONNXImporter::getGraphTensors(
                                        const opencv_onnx::GraphProto& graph_proto)
{
  opencv_onnx::TensorProto tensor_proto;
  std::map<std::string, Mat> layers_weights;

  for (int i = 0; i < graph_proto.initializer_size(); i++)
  {
    tensor_proto = graph_proto.initializer(i);
    Mat mat = getMatFromTensor(tensor_proto);
    releaseONNXTensor(tensor_proto);

    if (DNN_DIAGNOSTICS_RUN && mat.empty())
        continue;

    layers_weights.insert(std::make_pair(tensor_proto.name(), mat));
  }
  return layers_weights;
}

static DictValue parse(const ::google::protobuf::RepeatedField< ::google::protobuf::int64>& src) {
    std::vector<int32_t> dst(src.size());
    convertInt64ToInt32(src, dst, src.size());
    return DictValue::arrayInt(&dst[0], src.size());
}

static DictValue parseStr(const ::google::protobuf::RepeatedPtrField< ::std::string>& src) {
    return DictValue::arrayString(src.begin(), static_cast<int>(src.size()));
}

LayerParams ONNXImporter::getLayerParams(const opencv_onnx::NodeProto& node_proto)
{
    LayerParams lp;
    for(int i = 0; i < node_proto.attribute_size(); i++)
    {
        opencv_onnx::AttributeProto attribute_proto = node_proto.attribute(i);
        std::string attribute_name = attribute_proto.name();

        try
        {
            if(attribute_name == "kernel_shape")
            {
                CV_Assert(attribute_proto.ints_size() == 1 || attribute_proto.ints_size() == 2 || attribute_proto.ints_size() == 3);
                lp.set("kernel_size", parse(attribute_proto.ints()));
            }
            else if(attribute_name == "strides")
            {
                CV_Assert(attribute_proto.ints_size() == 1 || attribute_proto.ints_size() == 2 || attribute_proto.ints_size() == 3);
                lp.set("stride", parse(attribute_proto.ints()));
            }
            else if(attribute_name == "pads")
            {
                if (node_proto.op_type() == "Pad")
                {
                    // Padding layer.
                    // Paddings are in order begin0, begin1, .. beginN, end0, end1, ..., endN.
                    // We need to shuffle it to begin0, end0, begin1, end1, ...
                    CV_Assert(attribute_proto.ints_size() % 2 == 0);
                    const int dims = attribute_proto.ints_size() / 2;
                    std::vector<int32_t> paddings;
                    paddings.reserve(attribute_proto.ints_size());
                    for (int i = 0; i < dims; ++i)
                    {
                        paddings.push_back(attribute_proto.ints(i));
                        paddings.push_back(attribute_proto.ints(dims + i));
                    }
                    lp.set("paddings", DictValue::arrayInt(&paddings[0], paddings.size()));
                }
                else
                {
                    // Convolution or pooling.
                    CV_Assert(attribute_proto.ints_size() == 2 || attribute_proto.ints_size() == 4 || attribute_proto.ints_size() == 6);
                    lp.set("pad", parse(attribute_proto.ints()));
                }
            }
            else if(attribute_name == "auto_pad")
            {
                if (attribute_proto.s() == "SAME_UPPER" || attribute_proto.s() == "SAME_LOWER") {
                    lp.set("pad_mode",  "SAME");
                }
                else if (attribute_proto.s() == "VALID") {
                    lp.set("pad_mode", "VALID");
                }
            }
            else if(attribute_name == "dilations")
            {
                CV_Assert(attribute_proto.ints_size() == 1 || attribute_proto.ints_size() == 2 || attribute_proto.ints_size() == 3);
                lp.set("dilation", parse(attribute_proto.ints()));
            }
            else if(attribute_name == "activations" && node_proto.op_type() == "LSTM")
            {
                lp.set(attribute_name, parseStr(attribute_proto.strings()));
            }
            else if (attribute_proto.has_i())
            {
                ::google::protobuf::int64 src = attribute_proto.i();
                if (src < std::numeric_limits<int32_t>::min() || src > std::numeric_limits<int32_t>::max())
                    CV_Error(Error::StsOutOfRange, "Input is out of OpenCV 32S range");
                else
                    lp.set(attribute_name, saturate_cast<int32_t>(src));
            }
            else if (attribute_proto.has_f())
            {
                lp.set(attribute_name, attribute_proto.f());
            }
            else if (attribute_proto.has_s())
            {
                lp.set(attribute_name, attribute_proto.s());
            }
            else if (attribute_proto.floats_size() > 0)
            {
                lp.set(attribute_name, DictValue::arrayReal(
                    attribute_proto.floats().data(), attribute_proto.floats_size()));
            }
            else if (attribute_proto.ints_size() > 0)
            {
                lp.set(attribute_name, parse(attribute_proto.ints()));
            }
            else if (attribute_proto.has_t())
            {
                opencv_onnx::TensorProto tensor = attribute_proto.t();
                Mat blob = getMatFromTensor(tensor);
                lp.blobs.push_back(blob);
            }
            else if (attribute_proto.has_g())
            {
                CV_Error(Error::StsNotImplemented, cv::format("DNN/ONNX/Attribute[%s]: 'Graph' is not supported", attribute_name.c_str()));
            }
            else if (attribute_proto.graphs_size() > 0)
            {
                CV_Error(Error::StsNotImplemented,
                        cv::format("DNN/ONNX/Attribute[%s]: 'Graphs' (%d) in attributes is not supported",
                                attribute_name.c_str(), attribute_proto.graphs_size())
                );
            }
            else if (attribute_proto.strings_size() > 0)
            {
                std::string msg = cv::format("DNN/ONNX/Attribute[%s]: 'Strings' (%d) are not supported",
                        attribute_name.c_str(), attribute_proto.strings_size());
                CV_LOG_ERROR(NULL, msg);
                for (int i = 0; i < attribute_proto.strings_size(); i++)
                {
                    CV_LOG_ERROR(NULL, "    Attribute[" << attribute_name << "].string(" << i << ") = '" << attribute_proto.strings(i) << "'");
                }
                CV_Error(Error::StsNotImplemented, msg);
            }
            else if (attribute_proto.tensors_size() > 0)
            {
                CV_Error(Error::StsNotImplemented,
                        cv::format("DNN/ONNX/Attribute[%s]: 'Tensors' (%d) in attributes are not supported",
                                attribute_name.c_str(), attribute_proto.tensors_size())
                );
            }
            else
            {
                CV_Error(Error::StsNotImplemented, cv::format("DNN/ONNX/Attribute[%s]: unsupported attribute format", attribute_name.c_str()));
            }
        }
        catch (const cv::Exception& e)
        {
            CV_UNUSED(e);
            if (DNN_DIAGNOSTICS_RUN)
            {
                CV_LOG_ERROR(NULL, "DNN/ONNX: Potential problem with processing attributes for node " << node_proto.name() << " Attribute " << attribute_name.c_str()
                );
                continue;
            }
            throw;
        }
    }
    return lp;
}

Mat ONNXImporter::getBlob(const opencv_onnx::NodeProto& node_proto, int index)
{
    CV_Assert(index < node_proto.input_size());
    const std::string& input_name = node_proto.input(index);
    return getBlob(input_name);
}

Mat ONNXImporter::getBlob(const std::string& input_name)
{
    std::map<std::string, Mat>::const_iterator constBlob = constBlobs.find(input_name);
    if (constBlob == constBlobs.end())
    {
        CV_Error(Error::StsBadArg, std::string("Blob ") + input_name + " not found in const blobs");
    }
    return constBlob->second;
}

void ONNXImporter::addLayer(LayerParams& layerParams,
                            const opencv_onnx::NodeProto& node_proto)
{
    int depth = layerParams.get<int>("depth", CV_32F);
    int id = dstNet.addLayer(layerParams.name, layerParams.type, depth, layerParams);
    for (int i = 0; i < node_proto.output_size(); ++i)
    {
        layer_id.insert(std::make_pair(node_proto.output(i), LayerInfo(id, i)));
    }

    std::vector<MatShape> layerInpShapes, layerOutShapes, layerInternalShapes;
    int inpNum = 0;
    for (int j = 0; j < node_proto.input_size(); j++)
    {
        const std::string& input_name = node_proto.input(j);
        IterLayerId_t layerId = layer_id.find(input_name);
        if (layerId != layer_id.end()) {
            dstNet.connect(layerId->second.layerId, layerId->second.outputId, id, inpNum);
            ++inpNum;
            // Collect input shapes.
            IterShape_t shapeIt = outShapes.find(input_name);
            CV_Assert(shapeIt != outShapes.end());
            layerInpShapes.push_back(shapeIt->second);
        }
    }
    // Compute shape of output blob for this layer.
    Ptr<Layer> 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)
    {
        outShapes[node_proto.output(i)] = layerOutShapes[i];
    }
}

/** @brief Make N copies of input layer and set them as input to node_proto.
 * @param prefix prefix of new layers' names
 * @param node_proto node which will contain all copies as inputs
 * @param input name of the node to copy
 * @param n number of copies
 */
void ONNXImporter::expandMid(const std::string& prefix, opencv_onnx::NodeProto& node_proto,
                             const std::string& input, size_t n)
{
    std::vector<std::string> input_names;
    input_names.reserve(n);
    for (size_t j = 0; j < n; j++)
    {
        LayerParams copyLP;
        copyLP.name = format("%s/copy_%zu", prefix.c_str(), j);
        copyLP.type = "Identity";
        CV_Assert((layer_id.find(copyLP.name) == layer_id.end()) &&
            "Couldn't copy the node: generated name already exists in the graph.");
        input_names.push_back(copyLP.name);

        node_proto.set_input(0, input);
        node_proto.set_output(0, copyLP.name);
        addLayer(copyLP, node_proto);
    }
    node_proto.clear_input();
    for (size_t i = 0; i < input_names.size(); i++)
    {
        node_proto.add_input(input_names[i]);
    }
}

/** @brief Multiply one of node_proto inputs by -1
 * @param layerParams parameters of the node
 * @param node_proto node which input will be replaced
 * @param input_id id of input to be multiplied by -1
 */
void ONNXImporter::addNegation(const LayerParams& layerParams, opencv_onnx::NodeProto& node_proto, int input_id)
{
    LayerParams powerParams;
    powerParams.name = layerParams.name + "/neg";
    powerParams.type = "Power";
    powerParams.set("scale", -1.f);

    //Create Power layer
    int id = dstNet.addLayer(powerParams.name, powerParams.type, powerParams);
    //Connect to input
    IterLayerId_t layerId = layer_id.find(node_proto.input(input_id));
    CV_Assert(layerId != layer_id.end());
    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(input_id)];

    //Replace input to Power
    node_proto.set_input(input_id, powerParams.name);
}

void ONNXImporter::addConstant(const std::string& name, const Mat& blob)
{
    constBlobs.insert(std::make_pair(name, blob));
    outShapes.insert(std::make_pair(name, shape(blob)));
}

void ONNXImporter::handleQuantizedNode(LayerParams& layerParams,
                                       const opencv_onnx::NodeProto& node_proto)
{
    // Quantized nodes have output names ending with 'quantized'
    std::string outName = node_proto.output(0);
    int len = outName.length();
    if (len <= 9)
        return;

    if (outName.substr(len - 9) == "quantized")
    {
        outName = outName.substr(0, len - 9);
        Mat scale, zeropoint;

        if (constBlobs.find(outName + "scale") != constBlobs.end() &&
            constBlobs.find(outName + "zero_point") != constBlobs.end())
        {
            scale = getBlob(outName + "scale");
            zeropoint = getBlob(outName + "zero_point");
        }
        else
        {
            std::string inpName = node_proto.input(0);
            inpName = inpName.substr(0, inpName.length() - 9);
            scale = getBlob(inpName + "scale");
            zeropoint = getBlob(inpName + "zero_point");

            for (int i = 0; i < node_proto.output_size(); i++)
            {
                std::string out = node_proto.output(i);
                out = out.substr(0, out.length() - 9);
                addConstant(out + "scale", scale);
                addConstant(out + "zero_point", zeropoint);
            }
        }

        if (scale.total() != 1 || zeropoint.total() != 1)
            CV_Error(Error::StsNotImplemented, "Per-channel scales/zeropoints are not supported");

        layerParams.set("depth", CV_8S);
        layerParams.set("scales", DictValue::arrayReal(scale.ptr<float>(), 1));
        layerParams.set("zeropoints", DictValue::arrayInt(zeropoint.ptr<int8_t>(), 1));
    }
}

void ONNXImporter::populateNet()
{
    CV_Assert(model_proto.has_graph());
    graph_proto = model_proto.graph();

    std::string framework_version;
    if (model_proto.has_producer_name())
        framework_name = model_proto.producer_name();
    if (model_proto.has_producer_version())
        framework_version = model_proto.producer_version();

    CV_LOG_INFO(NULL, "DNN/ONNX: loading ONNX"
            << (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()
            << ", inputs = " << graph_proto.input_size()
            << ", outputs = " << graph_proto.output_size()
            );

    simplifySubgraphs(graph_proto);

    const int layersSize = graph_proto.node_size();
    CV_LOG_DEBUG(NULL, "DNN/ONNX: graph simplified to " << layersSize << " nodes");

    constBlobs = getGraphTensors(graph_proto);
    // 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)
    {
        const opencv_onnx::ValueInfoProto& valueInfoProto = graph_proto.input(i);
        CV_Assert(valueInfoProto.has_name());
        CV_Assert(valueInfoProto.has_type());
        opencv_onnx::TypeProto typeProto = valueInfoProto.type();
        CV_Assert(typeProto.has_tensor_type());
        opencv_onnx::TypeProto::Tensor tensor = typeProto.tensor_type();
        CV_Assert(tensor.has_shape());
        opencv_onnx::TensorShapeProto tensorShape = tensor.shape();

        MatShape inpShape(tensorShape.dim_size());
        for (int j = 0; j < inpShape.size(); ++j)
        {
            inpShape[j] = tensorShape.dim(j).dim_value();
            // NHW, NCHW(NHWC), NCDHW(NDHWC); do not set this flag if only N is dynamic
            if (!tensorShape.dim(j).dim_param().empty() && !(j == 0 && inpShape.size() >= 3))
                hasDynamicShapes = true;
        }
        if (!inpShape.empty() && !hasDynamicShapes)
        {
            inpShape[0] = std::max(inpShape[0], 1); // It's OK to have undetermined batch size
        }
        outShapes[valueInfoProto.name()] = inpShape;
    }

    // create map with network inputs (without const blobs)
    // fill map: push layer name, layer id and output id
    std::vector<String> netInputs;
    for (int j = 0; j < graph_proto.input_size(); j++)
    {
        const std::string& name = graph_proto.input(j).name();
        if (constBlobs.find(name) == constBlobs.end()) {
            netInputs.push_back(name);
            layer_id.insert(std::make_pair(name, LayerInfo(0, netInputs.size() - 1)));
        }
    }
    dstNet.setInputsNames(netInputs);

    if (DNN_DIAGNOSTICS_RUN) {
        CV_LOG_INFO(NULL, "DNN/ONNX: start diagnostic run!");
        layerHandler->fillRegistry(graph_proto);
    }

    for(int li = 0; li < layersSize; li++)
    {
        const opencv_onnx::NodeProto& node_proto = graph_proto.node(li);
        handleNode(node_proto);
    }

    CV_LOG_DEBUG(NULL, (DNN_DIAGNOSTICS_RUN ? "DNN/ONNX: diagnostic run completed!" : "DNN/ONNX: import completed!"));
}

void ONNXImporter::handleNode(const opencv_onnx::NodeProto& node_proto)
{
    CV_Assert(node_proto.output_size() >= 1);
    std::string name = node_proto.output(0);
    const std::string& layer_type = node_proto.op_type();
    CV_LOG_DEBUG(NULL, "DNN/ONNX: 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())
    );
    LayerParams layerParams;
    try
    {
        // FIXIT not all cases can be repacked into "LayerParams". Importer should handle such cases directly for each "layer_type"
        layerParams = getLayerParams(node_proto);

        layerParams.name = name;
        layerParams.type = layer_type;
        layerParams.set("has_dynamic_shapes", hasDynamicShapes);

        handleQuantizedNode(layerParams, node_proto);

        DispatchMap::const_iterator iter = dispatch.find(layer_type);
        if (iter != dispatch.end())
        {
            CALL_MEMBER_FN(*this, iter->second)(layerParams, node_proto);
        }
        else
        {
            parseCustomLayer(layerParams, node_proto);
        }
    }
    catch (const cv::Exception& e)
    {
        if (DNN_DIAGNOSTICS_RUN)
        {
            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())
            {
                try
                {
                    Ptr<Layer> layer = LayerFactory::createLayerInstance(layerParams.type, layerParams);
                }
                catch (const std::exception& e)
                {
                    CV_LOG_ERROR(NULL, "DNN/ONNX: Layer of type " << layerParams.type << "(" << layer_type << ") cannot be created with parameters " << layerParams << ". Error: " << e.what()
                    );
                }
            }
        }
        else
        {
            CV_LOG_ERROR(NULL, "DNN/ONNX: ERROR 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())
            );
        }
        for (int i = 0; i < node_proto.input_size(); i++)
        {
            CV_LOG_INFO(NULL, "    Input[" << i << "] = '" << node_proto.input(i) << "'");
        }
        for (int i = 0; i < node_proto.output_size(); i++)
        {
            CV_LOG_INFO(NULL, "    Output[" << i << "] = '" << node_proto.output(i) << "'");
        }
        if (DNN_DIAGNOSTICS_RUN)
        {
            for (int i = 0; i < node_proto.output_size(); ++i)
            {
                layer_id.insert(std::make_pair(node_proto.output(i), LayerInfo(0, i)));
                outShapes[node_proto.output(i)] = outShapes[node_proto.input(0)];
            }
        }
        else
            CV_Error(Error::StsError, cv::format("Node [%s]:(%s) parse error: %s", layer_type.c_str(), name.c_str(), e.what()));
    }
}

void ONNXImporter::parseArg(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{
    const std::string& layer_type = node_proto.op_type();
    layerParams.type = "Arg";
    layerParams.set("op", layer_type == "ArgMax" ? "max" : "min");
    addLayer(layerParams, node_proto);
}

void setCeilMode(LayerParams& layerParams)
{
    // auto_pad attribute is deprecated and uses ceil
    if (layerParams.has("pad_mode"))
    {
        layerParams.set("ceil_mode", true);
    }
    else if (!layerParams.has("ceil_mode"))
    {
        layerParams.set("ceil_mode", false);
    }
}

void ONNXImporter::parseMaxUnpool(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{
    layerParams.type = "MaxUnpool";

    DictValue kernel_shape = layerParams.get("kernel_size");
    CV_Assert(kernel_shape.size() == 2);
    layerParams.set("pool_k_w", kernel_shape.get<int>(0));
    layerParams.set("pool_k_h", kernel_shape.get<int>(1));

    int pool_pad_w = 0, pool_pad_h = 0;
    if (layerParams.has("pad"))
    {
        DictValue pads = layerParams.get("pad");
        CV_CheckEQ(pads.size(), 2, "");
        pool_pad_w = pads.get<int>(0);
        pool_pad_h = pads.get<int>(1);
    }
    layerParams.set("pool_pad_w", pool_pad_w);
    layerParams.set("pool_pad_h", pool_pad_h);


    int pool_stride_w = 1, pool_stride_h = 1;
    if (layerParams.has("stride"))
    {
        DictValue strides = layerParams.get("stride");
        CV_CheckEQ(strides.size(), 2, "");
        pool_stride_w = strides.get<int>(0);
        pool_stride_h = strides.get<int>(1);
    }
    layerParams.set("pool_stride_w", pool_stride_w);
    layerParams.set("pool_stride_h", pool_stride_h);

    addLayer(layerParams, node_proto);
}

void ONNXImporter::parseMaxPool(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{
    int depth = layerParams.get<int>("depth", CV_32F);
    layerParams.type = (depth == CV_8S) ? "PoolingInt8" : "Pooling";
    layerParams.set("pool", "MAX");
    setCeilMode(layerParams);
    addLayer(layerParams, node_proto);
}

void ONNXImporter::parseAveragePool(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{
    layerParams.type = "Pooling";
    layerParams.set("pool", "AVE");
    setCeilMode(layerParams);
    layerParams.set("ave_pool_padded_area", framework_name == "pytorch");
    addLayer(layerParams, node_proto);
}

void ONNXImporter::parseReduce(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto_)
{
    opencv_onnx::NodeProto node_proto = node_proto_;
    const std::string& layer_type = node_proto.op_type();

    CV_Assert(node_proto.input_size() == 1);
    layerParams.type = "Pooling";
    String pool;
    if (layer_type == "GlobalMaxPool" || layer_type == "ReduceMax")
        pool = "MAX";
    else if (layer_type == "ReduceSum")
        pool = "SUM";
    else
        pool = "AVE";
    layerParams.set("pool", pool);
    layerParams.set("global_pooling", !layerParams.has("axes"));
    bool keepdims = layerParams.get<int>("keepdims", 1) == 1;
    if (layerParams.has("axes") && (layer_type == "ReduceMean" || layer_type == "ReduceSum" || layer_type == "ReduceMax"))
    {
        MatShape inpShape = outShapes[node_proto.input(0)];
        DictValue axes = layerParams.get("axes");
        MatShape targetShape;
        std::vector<bool> shouldDelete(inpShape.size(), false);
        for (int i = 0; i < axes.size(); i++) {
            int axis = normalize_axis(axes.get<int>(i), inpShape.size());
            shouldDelete[axis] = true;
        }
        for (int axis = 0; axis < inpShape.size(); ++axis){
            if (!shouldDelete[axis])
                targetShape.push_back(inpShape[axis]);
            else if (keepdims)
                targetShape.push_back(1);
        }

        if (inpShape.size() == 3 && axes.size() <= 2)
        {
            int axis = normalize_axis(axes.get<int>(0), inpShape.size());
            CV_CheckNE(axis, 0, "");

            LayerParams reshapeLp;
            reshapeLp.name = layerParams.name + "/reshape";
            reshapeLp.type = "Reshape";
            CV_Assert(layer_id.find(reshapeLp.name) == layer_id.end());
            reshapeLp.set("axis", 0);
            reshapeLp.set("num_axes", 1);
            int newShape[] = {1, -1};
            reshapeLp.set("dim", DictValue::arrayInt(&newShape[0], 2));

            opencv_onnx::NodeProto proto;
            proto.add_input(node_proto.input(0));
            proto.add_output(reshapeLp.name);
            addLayer(reshapeLp, proto);

            LayerParams avgLp;
            avgLp.name = layerParams.name + "/avg";
            avgLp.type = "Pooling";
            CV_Assert(layer_id.find(avgLp.name) == layer_id.end());
            avgLp.set("pool", pool);
            if (axes.size() == 2)
            {
                CV_CheckEQ(normalize_axis(axes.get<int>(0), inpShape.size()), 1, "Unsupported mode");
                CV_CheckEQ(normalize_axis(axes.get<int>(1), inpShape.size()), 2, "Unsupported mode");
                avgLp.set("global_pooling", true);
            }
            else
            {
                avgLp.set(axis == 2 ? "global_pooling_w" : "global_pooling_h", true);
                avgLp.set(axis == 2 ? "kernel_h" : "kernel_w", 1);
            }

            node_proto.set_input(0, reshapeLp.name);
            node_proto.set_output(0, avgLp.name);
            addLayer(avgLp, node_proto);
        }
        else
        {
            if (inpShape.size() != 4 && inpShape.size() != 5)
                CV_Error(Error::StsNotImplemented, "Unsupported input shape of " + layer_type + " operation.");

            CV_Assert(axes.size() <= inpShape.size() - 2);
            std::vector<int> kernel_size(inpShape.size() - 2, 1);
            if (axes.size() == 1 && (normalize_axis(axes.get<int>(0), inpShape.size()) <= 1))
            {
                int axis = normalize_axis(axes.get<int>(0), inpShape.size());
                MatShape newShape = inpShape;
                newShape[axis + 1] = total(newShape, axis + 1);
                newShape.resize(axis + 2);
                newShape.insert(newShape.begin(), 2 - axis, 1);

                LayerParams reshapeLp;
                reshapeLp.type = "Reshape";
                reshapeLp.name = layerParams.name + "/reshape";
                CV_Assert(layer_id.find(reshapeLp.name) == layer_id.end());
                reshapeLp.set("dim", DictValue::arrayInt(&newShape[0], newShape.size()));

                node_proto.set_output(0, reshapeLp.name);
                addLayer(reshapeLp, node_proto);

                kernel_size.resize(2);
                kernel_size[0] = inpShape[axis];
                node_proto.set_input(0, node_proto.output(0));
            }
            else
            {
                for (int i = 0; i < axes.size(); i++) {
                    int axis = normalize_axis(axes.get<int>(i), inpShape.size());
                    CV_Assert_N(axis >= 2 + i, axis < inpShape.size());
                    kernel_size[axis - 2] = inpShape[axis];
                }
            }

            LayerParams poolLp = layerParams;
            poolLp.name = layerParams.name + "/avg";
            CV_Assert(layer_id.find(poolLp.name) == layer_id.end());
            poolLp.set("kernel_size", DictValue::arrayInt(&kernel_size[0], kernel_size.size()));

            node_proto.set_output(0, poolLp.name);
            addLayer(poolLp, node_proto);
        }

        layerParams.type = "Reshape";
        layerParams.set("dim", DictValue::arrayInt(&targetShape[0], targetShape.size()));

        node_proto.set_input(0, node_proto.output(0));
        node_proto.set_output(0, layerParams.name);
    }
    else if (!layerParams.has("axes") && (layer_type == "ReduceMean" || layer_type == "ReduceSum" || layer_type == "ReduceMax"))
    {
        IterShape_t shapeIt = outShapes.find(node_proto.input(0));
        CV_Assert(shapeIt != outShapes.end());
        const size_t dims = keepdims ? shapeIt->second.size() : 1;

        LayerParams reshapeLp;
        reshapeLp.name = layerParams.name + "/reshape";
        reshapeLp.type = "Reshape";
        CV_Assert(layer_id.find(reshapeLp.name) == layer_id.end());
        int newShape[] = {1, 1, 1, -1};
        reshapeLp.set("dim", DictValue::arrayInt(&newShape[0], 4));

        opencv_onnx::NodeProto proto;
        proto.add_input(node_proto.input(0));
        proto.add_output(reshapeLp.name);
        addLayer(reshapeLp, proto);

        LayerParams poolLp = layerParams;
        poolLp.name = layerParams.name + "/pool";
        CV_Assert(layer_id.find(poolLp.name) == layer_id.end());

        node_proto.set_input(0, reshapeLp.name);
        node_proto.set_output(0, poolLp.name);
        addLayer(poolLp, node_proto);

        layerParams.type = "Reshape";
        std::vector<int> targetShape(dims, 1);
        layerParams.set("dim", DictValue::arrayInt(targetShape.data(), targetShape.size()));

        node_proto.set_input(0, node_proto.output(0));
        node_proto.set_output(0, layerParams.name);
    }
    addLayer(layerParams, node_proto);
}

void ONNXImporter::parseSlice(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{
    int axis = 0;
    std::vector<int> begin;
    std::vector<int> end;
    std::vector<int> steps;
    int inp_size = node_proto.input_size();

    if (inp_size == 1)
    {
        if (layerParams.has("axes")) {
            DictValue axes = layerParams.get("axes");
            for (int i = 1; i < axes.size(); ++i) {
                CV_Assert(axes.get<int>(i - 1) == axes.get<int>(i) - 1);
            }
            axis = axes.get<int>(0);
        }

        DictValue starts = layerParams.get("starts");
        DictValue ends = layerParams.get("ends");
        CV_Assert(starts.size() == ends.size());

        if (axis > 0) {
            begin.resize(axis, 0);
            end.resize(axis, -1);
        }
        for (int i = 0; i < starts.size(); ++i)
        {
            begin.push_back(starts.get<int>(i));
            int finish = ends.get<int>(i);
            end.push_back((finish < 0) ? --finish : finish); // numpy doesn't include last dim
        }
    } else { // inp_size > 1
        CV_Assert(inp_size >= 3);
        for (int i = 1; i < inp_size; i++) {
            CV_Assert(constBlobs.find(node_proto.input(i)) != constBlobs.end());
        }
        Mat start_blob = getBlob(node_proto, 1);
        Mat end_blob   = getBlob(node_proto, 2);
        CV_Assert(start_blob.total() == end_blob.total());

        if (inp_size > 3) {
            Mat axes_blob = getBlob(node_proto, 3);
            const int* axes = (int*)axes_blob.data;
            for (int i = 1; i < axes_blob.total(); ++i) {
                CV_Assert(axes[i - 1] == axes[i] - 1);
            }
            axis = axes[0];
        }

        const int* starts = start_blob.ptr<int>();
        const int* ends   = end_blob.ptr<int>();
        if (axis > 0) {
            begin.resize(axis, 0);
            end.resize(axis, -1);
        }
        std::copy(starts, starts + start_blob.total(), std::back_inserter(begin));
        for (int i = 0; i < end_blob.total(); ++i)
        {
            int finish = ends[i];
            end.push_back((finish < 0) ? --finish : finish); // numpy doesn't include last dim
        }

        if (inp_size == 5) {
            CV_Assert(constBlobs.find(node_proto.input(4)) != constBlobs.end());
            Mat step_blob = getBlob(node_proto, 4);
            const int* steps_ptr = step_blob.ptr<int>();

            if (axis > 0)
                steps.resize(axis, 1);

            std::copy(steps_ptr, steps_ptr + step_blob.total(), std::back_inserter(steps));

            // Very strange application for Slice op with tensor reversing.
            // We just workaround it for 2d constants.
            if (constBlobs.find(node_proto.input(0)) != constBlobs.end() &&
                axis == 0 &&
                start_blob.at<int>(0) == -1 && step_blob.at<int>(0) == -1 &&
                end_blob.at<int>(0) == std::numeric_limits<int32_t>::min())
            {
                Mat inp = getBlob(node_proto, 0);
                if (inp.dims == 2)
                {
                    Mat flipped;
                    flip(inp, flipped, 0);
                    addConstant(layerParams.name, flipped);
                    return;
                }
            }
        }
    }
    layerParams.set("begin", DictValue::arrayInt(&begin[0], begin.size()));
    layerParams.set("end", DictValue::arrayInt(&end[0], end.size()));
    layerParams.set("axis", axis);

    if (!steps.empty())
        layerParams.set("steps", DictValue::arrayInt(&steps[0], steps.size()));

    if (constBlobs.find(node_proto.input(0)) != constBlobs.end())
    {
        Mat inp = getBlob(node_proto, 0);
        std::vector<Mat> inputs, sliced;
        inputs.push_back(inp);
        runLayer(layerParams, inputs, sliced);
        CV_Assert(sliced.size() == 1);
        addConstant(layerParams.name, sliced[0]);
        return;
    }
    addLayer(layerParams, node_proto);
}

void ONNXImporter::parseSplit(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{
    if (layerParams.has("split"))
    {
        DictValue splits = layerParams.get("split");
        const int numSplits = splits.size();
        CV_Assert(numSplits > 1);

        std::vector<int> slicePoints(numSplits - 1, splits.get<int>(0));
        for (int i = 1; i < splits.size() - 1; ++i)
        {
            slicePoints[i] = slicePoints[i - 1] + splits.get<int>(i);
        }
        layerParams.set("slice_point", DictValue::arrayInt(&slicePoints[0], slicePoints.size()));
    }
    else
    {
        layerParams.set("num_split", node_proto.output_size());
    }
    int depth = layerParams.get<int>("depth", CV_32F);
    layerParams.type = (depth == CV_8S) ? "SliceInt8" : "Slice";
    layerParams.set("axis", layerParams.get<float>("axis", 0));
    addLayer(layerParams, node_proto);
}

void ONNXImporter::parseBias(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto_)
{
    opencv_onnx::NodeProto node_proto = node_proto_;
    const std::string& layer_type = node_proto.op_type();
    bool isSub = layer_type == "Sub";

    if (layer_type == "Sum" && node_proto.input_size() == 1)
    {
        layerParams.type = "Identity";
        addLayer(layerParams, node_proto);
        return;
    }

    CV_Assert((node_proto.input_size() == 2) || (layer_type == "Sum" && node_proto.input_size() > 2));

    if (layer_type == "Sum" && node_proto.input_size() > 2)
    {
        for (int i = 0; i < node_proto.input_size(); ++i)
        {
            if (layer_id.find(node_proto.input(i)) == layer_id.end())
            {
                CV_Error(Error::StsNotImplemented, "Sum of constants is not implemented for inputs > 2");
            }
        }
    }

    bool is_const_0 = layer_id.find(node_proto.input(0)) == layer_id.end();
    bool is_const_1 = layer_id.find(node_proto.input(1)) == layer_id.end();
    if (is_const_0 && is_const_1)
    {
        Mat blob_0 = getBlob(node_proto, 0);
        Mat blob_1 = getBlob(node_proto, 1);
        CV_Assert(blob_0.size == blob_1.size);
        Mat output = isSub ? (blob_0 - blob_1) : (blob_0 + blob_1);
        addConstant(layerParams.name, output);
        return;
    }
    else if (is_const_0 || is_const_1)
    {
        int const_blob_id = is_const_0 ? 0 : 1;
        int input_id = 1 - const_blob_id;
        Mat blob = getBlob(node_proto, const_blob_id);
        int blob_total = blob.total();

        const float inputScale = isSub && is_const_0 ? -1.f : 1.f;
        const float constScale = isSub && is_const_1 ? -1.f : 1.f;

        if (blob_total == 1) {
            layerParams.type = "Power";
            layerParams.set("scale", inputScale);
            layerParams.set("shift", constScale * blob.ptr<float>()[0]);
        }
        else {
            MatShape inpShape = outShapes[node_proto.input(input_id)];
            if (shape(blob) == inpShape)
            {
                LayerParams constParams;
                constParams.name = layerParams.name + "/const";
                constParams.type = "Const";
                constParams.blobs.push_back(blob);
                int id = dstNet.addLayer(constParams.name, constParams.type, constParams);
                layer_id.insert(std::make_pair(constParams.name, LayerInfo(id, 0)));
                outShapes[constParams.name] = shape(blob);

                layerParams.type = "Eltwise";
                float coeffs[] = {1., isSub ? -1.f : 1.f};
                layerParams.set("coeff", DictValue::arrayReal<float*>(coeffs, 2));
                node_proto.set_input(const_blob_id, constParams.name);
            }
            else
            {
                if (inputScale < 0.f)
                {
                    addNegation(layerParams, node_proto, input_id);
                }

                layerParams.type = "Scale";
                layerParams.set("bias_term", true);
                int axis = 1;
                for (int i = 0; i < graph_proto.initializer_size(); i++)
                {
                    opencv_onnx::TensorProto tensor_proto = graph_proto.initializer(i);
                    if (tensor_proto.name() == node_proto.input(const_blob_id))
                    {
                        axis = inpShape.size() - tensor_proto.dims_size();
                        break;
                    }
                }
                layerParams.set("axis", axis);
                blob = blob.reshape(1, 1);
                layerParams.blobs.push_back(constScale * blob);
            }
        }
    }
    else if (outShapes[node_proto.input(0)] == outShapes[node_proto.input(1)])
    {
        layerParams.type = "Eltwise";
        if (isSub)
        {
            static float subCoeffs[] = {1.f, -1.f};
            layerParams.set("coeff", DictValue::arrayReal<float*>(subCoeffs, 2));
        }
    }
    else
    {
        if (isSub)
        {
            addNegation(layerParams, node_proto, 1);
        }
        layerParams.type = "Scale";
        layerParams.set("bias_term", true);
    }
    addLayer(layerParams, node_proto);
}

void ONNXImporter::parsePow(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{
    if (layer_id.find(node_proto.input(1)) != layer_id.end())
        CV_Error(Error::StsNotImplemented, "Unsupported Pow op with variable power");

    Mat blob = getBlob(node_proto, 1);
    if (blob.total() != 1)
        CV_Error(Error::StsNotImplemented, "Pow op supports only scalar power");

    blob.convertTo(blob, CV_32F);
    layerParams.type = "Power";
    layerParams.set("power", blob.ptr<float>()[0]);
    addLayer(layerParams, node_proto);
}

// "Min" "Max"
void ONNXImporter::parseMinMax(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{
    const std::string& layer_type = node_proto.op_type();
    layerParams.type = "Eltwise";
    layerParams.set("operation", layer_type == "Max" ? "max" : "min");
    addLayer(layerParams, node_proto);
}

void ONNXImporter::parseNeg(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{
    layerParams.type = "Power";
    layerParams.set("scale", -1);
    addLayer(layerParams, node_proto);
}

void ONNXImporter::parseConstant(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{
    CV_Assert(node_proto.input_size() == 0);
    CV_Assert(layerParams.blobs.size() == 1);
    addConstant(layerParams.name, layerParams.blobs[0]);
}

void ONNXImporter::parseLSTM(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto_)
{
    opencv_onnx::NodeProto node_proto = node_proto_;
    LayerParams lstmParams = layerParams;
    lstmParams.name += "/lstm";

    // https://pytorch.org/docs/stable/nn.html#lstm
    CV_Assert(node_proto.input_size() >= 7);
    Mat Wx = getBlob(node_proto, 1);
    Mat Wh = getBlob(node_proto, 2);
    Mat b = getBlob(node_proto, 3);

    const int numHidden = lstmParams.get<int>("hidden_size");
    const int numDirs = Wx.size[0];  // Is 1 for forward only and 2 for bidirectional LSTM.
    const int numFeatures = Wx.size[2];

    Mat h0, c0;
    if (!node_proto.input(5).empty()) {
        h0 = getBlob(node_proto, 5);
        h0 = h0.reshape(1, h0.size[0] * h0.size[1]);
    } else {
        // initial_h attribute can be empty in case of keras2onnx producer. fill it with zeros
        h0 = Mat::zeros(numDirs * numFeatures, numHidden, CV_32FC1);
    }
    if (!node_proto.input(6).empty()) {
        c0 = getBlob(node_proto, 6);
        c0 = c0.reshape(1, c0.size[0] * c0.size[1]);
    } else {
        // initial_c attribute can be empty in case of keras2onnx producer. fill it with zeros
        c0 = Mat::zeros(numDirs * numFeatures, numHidden, CV_32FC1);
    }

    b = b.reshape(1, b.size[0]);
    Mat bx = b.colRange(0, b.cols / 2);
    Mat bh = b.colRange(b.cols / 2, b.cols);
    b = bx + bh;

    // IFGO->IGFO
    for (int k = 0; k < numDirs; ++k)
    {
        float* WxData = Wx.ptr<float>(k);
        float* WhData = Wh.ptr<float>(k);
        float* biasData = b.ptr<float>(k);
        for (int j = 0; j < numHidden; ++j)
        {
            for (int i = 0; i < numFeatures; ++i)
            {
                std::swap(WxData[(numHidden + j) * numFeatures + i],
                          WxData[(numHidden * 2 + j) * numFeatures + i]);
            }
            for (int i = 0; i < numHidden; ++i)
            {
                std::swap(WhData[(numHidden + j) * numHidden + i],
                          WhData[(numHidden * 2 + j) * numHidden + i]);
            }
            std::swap(biasData[numHidden + j], biasData[numHidden * 2 + j]);
        }
    }
    Wx = Wx.reshape(1, Wx.size[0] * Wx.size[1]);
    Wh = Wh.reshape(1, Wh.size[0] * Wh.size[1]);


    lstmParams.blobs.resize(5);
    lstmParams.blobs[0] = Wh;
    lstmParams.blobs[1] = Wx;
    lstmParams.blobs[2] = b;
    lstmParams.blobs[3] = h0;
    lstmParams.blobs[4] = c0;

    // read direction attribute
    lstmParams.set("reverse", lstmParams.get<String>("direction", "") == "reverse");
    lstmParams.set("bidirectional", lstmParams.get<String>("direction", "") == "bidirectional");

    node_proto.set_output(0, lstmParams.name);  // set different name so output shapes will be registered on that name
    addLayer(lstmParams, node_proto);

    MatShape lstmShape = outShapes[node_proto.output(0)];

    // Add fake 1 as it is done in ONNX
    lstmShape.insert(lstmShape.begin() + 1, 1);

    layerParams.type = "Reshape";
    layerParams.set("dim", DictValue::arrayInt(&lstmShape[0], lstmShape.size()));
    node_proto.set_input(0, lstmParams.name);  // redirect input to LSTM
    node_proto.set_output(0, layerParams.name);  // keep origin LSTM's name
    addLayer(layerParams, node_proto);
}

void ONNXImporter::parseGRU(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto_)
{
    opencv_onnx::NodeProto node_proto = node_proto_;
    LayerParams gruParams = layerParams;
    gruParams.name += "/gru";

    // https://pytorch.org/docs/stable/generated/torch.nn.GRU.html?highlight=gru#
    CV_Assert(node_proto.input_size() == 6);
    Mat Wx = getBlob(node_proto, 1);
    Mat Wh = getBlob(node_proto, 2);
    Mat b = getBlob(node_proto, 3);
    Mat h0 = getBlob(node_proto, 5);

    Wx = Wx.reshape(1, Wx.size[0] * Wx.size[1]);
    Wh = Wh.reshape(1, Wh.size[0] * Wh.size[1]);
    h0 = h0.reshape(1, h0.size[0] * h0.size[1]);
    b = b.reshape(1, b.size[0]);

    gruParams.blobs.resize(4);
    gruParams.blobs[0] = Wh;
    gruParams.blobs[1] = Wx;
    gruParams.blobs[2] = b;
    gruParams.blobs[3] = h0;
    gruParams.set("bidirectional", gruParams.get<String>("direction", "") == "bidirectional");

    node_proto.set_output(0, gruParams.name);  // set different name so output shapes will be registered on that name
    addLayer(gruParams, node_proto);

    MatShape gruShape = outShapes[node_proto.output(0)];

    // Add fake 1 as it is done in ONNX
    gruShape.insert(gruShape.begin() + 1, 1);

    layerParams.type = "Reshape";
    layerParams.set("dim", DictValue::arrayInt(&gruShape[0], gruShape.size()));
    node_proto.set_input(0, gruParams.name);  // redirect input to GRU
    node_proto.set_output(0, layerParams.name);  // keep origin GRU's name
    addLayer(layerParams, node_proto);
}

void ONNXImporter::parseImageScaler(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{
    const float scale = layerParams.has("scale") ? layerParams.get<float>("scale") : 1.0f;
    layerParams.erase("scale");

    if (layerParams.has("bias"))
    {
        layerParams.type = "Scale";
        layerParams.blobs.push_back(
                Mat(Size(1,  layerParams.get("bias").size()), CV_32FC1, scale));

        layerParams.set("bias_term", true);
        Mat bias(1, layerParams.get("bias").size(), CV_32FC1);
        for (int j = 0; j < bias.total(); j++) {
            bias.at<float>(0, j) = layerParams.get("bias").getRealValue(j);
        }
        layerParams.blobs.push_back(bias);
        layerParams.erase("bias");
    }
    else {
        layerParams.set("scale", scale);
        layerParams.type = "Power";
    }
    addLayer(layerParams, node_proto);
}

void ONNXImporter::parseClip(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{
    CV_CheckEQ(node_proto.input_size(), 1, "");
    layerParams.type = "ReLU6";
    layerParams.set("min_value", layerParams.get<float>("min", -FLT_MAX));
    layerParams.set("max_value", layerParams.get<float>("max", FLT_MAX));
    addLayer(layerParams, node_proto);
}

void ONNXImporter::parseLeakyRelu(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{
    layerParams.type = "ReLU";
    layerParams.set("negative_slope", layerParams.get<float>("alpha", 0.01));
    addLayer(layerParams, node_proto);
}

void ONNXImporter::parseRelu(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{
    layerParams.type = "ReLU";
    addLayer(layerParams, node_proto);
}

void ONNXImporter::parseElu(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{
    layerParams.type = "ELU";
    addLayer(layerParams, node_proto);
}

void ONNXImporter::parseTanh(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{
    layerParams.type = "TanH";
    addLayer(layerParams, node_proto);
}

void ONNXImporter::parseAbs(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{
    layerParams.type = "AbsVal";
    addLayer(layerParams, node_proto);
}

void ONNXImporter::parseCompare(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{
    CV_Assert(node_proto.input_size() == 2);
    const std::string& layer_type = node_proto.op_type();

    bool is_const_0 = layer_id.find(node_proto.input(0)) == layer_id.end();
    bool is_const_1 = layer_id.find(node_proto.input(1)) == layer_id.end();

    if (is_const_0 || is_const_1)
    {
        Mat blob = getBlob(node_proto, static_cast<int>(is_const_1));
        blob = blob.reshape(1, 1);
        layerParams.blobs.push_back(blob);
    }

    layerParams.type = "Compare";

    if (layer_type == "Equal")
        layerParams.set("mode", "equal");
    else if (layer_type == "Greater")
        layerParams.set("mode", "greater");
    else
        layerParams.set("mode", "less");
    addLayer(layerParams, node_proto);
}

void ONNXImporter::parsePRelu(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{
    layerParams.type = "PReLU";
    layerParams.blobs.push_back(getBlob(node_proto, 1));
    addLayer(layerParams, node_proto);
}

void ONNXImporter::parseLRN(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{
    replaceLayerParam(layerParams, "size", "local_size");
    addLayer(layerParams, node_proto);
}

void ONNXImporter::parseInstanceNormalization(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto_)
{
    opencv_onnx::NodeProto node_proto = node_proto_;
    if (node_proto.input_size() != 3)
        CV_Error(Error::StsNotImplemented,
                 "Expected input, scale, bias");

    layerParams.blobs.resize(4);
    layerParams.blobs[2] = getBlob(node_proto, 1);  // weightData
    layerParams.blobs[3] = getBlob(node_proto, 2);  // biasData
    layerParams.set("has_bias", true);
    layerParams.set("has_weight", true);

    // Get number of channels in input
    int size = layerParams.blobs[2].total();
    layerParams.blobs[0] = Mat::zeros(size, 1, CV_32F); // mean
    layerParams.blobs[1] = Mat::ones(size, 1, CV_32F); // std

    LayerParams mvnParams;
    mvnParams.name = layerParams.name + "/MVN";
    mvnParams.type = "MVN";
    mvnParams.set("eps", layerParams.get<float>("epsilon"));
    layerParams.erase("epsilon");

    //Create MVN layer
    int 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());
    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)];

    //Replace Batch Norm's input to MVN
    node_proto.set_input(0, mvnParams.name);
    layerParams.type = "BatchNorm";
    addLayer(layerParams, node_proto);
}

void ONNXImporter::parseBatchNormalization(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{
    if (node_proto.input_size() != 5)
        CV_Error(Error::StsNotImplemented,
                 "Expected input, scale, bias, mean and var");

    layerParams.type = "BatchNorm";
    replaceLayerParam(layerParams, "epsilon", "eps");
    replaceLayerParam(layerParams, "spatial", "use_global_stats");

    Mat meanData = getBlob(node_proto, 3);
    Mat stdData =  getBlob(node_proto, 4);

    layerParams.blobs.push_back(meanData);
    layerParams.blobs.push_back(stdData);

    if (!node_proto.input(1).empty()) {
        layerParams.set("has_weight", true);
        layerParams.blobs.push_back(getBlob(node_proto, 1));  // weightData
    } else {
        layerParams.set("has_weight", false);
    }

    if (!node_proto.input(2).empty()) {
        layerParams.set("has_bias", true);
        layerParams.blobs.push_back(getBlob(node_proto, 2)); // biasData
    } else {
        layerParams.set("has_bias", false);
    }
    addLayer(layerParams, node_proto);
}

void ONNXImporter::parseGemm(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{
    CV_Assert(node_proto.input_size() >= 2);
    layerParams.type = "InnerProduct";
    Mat weights = getBlob(node_proto, 1);
    int ind_num_out = 0;
    if (layerParams.has("transB") && !layerParams.get<int>("transB")) {
        transpose(weights, weights);
        ind_num_out = 1;
    }
    layerParams.blobs.push_back(weights);

    if (node_proto.input_size() == 3) {
        Mat bias = getBlob(node_proto, 2);
        layerParams.blobs.push_back(bias);
    }
    if (constBlobs.find(node_proto.input(0)) != constBlobs.end())
    {
        Mat inputBuf = getBlob(node_proto, 0);

        LayerParams constParams;
        constParams.name = node_proto.input(0);
        constParams.type = "Const";
        constParams.blobs.push_back(inputBuf);

        opencv_onnx::NodeProto proto;
        proto.add_output(constParams.name);
        addLayer(constParams, proto);
    }

    layerParams.set("num_output", layerParams.blobs[0].size[ind_num_out]);
    layerParams.set("bias_term", node_proto.input_size() == 3);
    addLayer(layerParams, node_proto);
}

void ONNXImporter::parseMatMul(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{
    CV_Assert(node_proto.input_size() == 2);
    layerParams.type = "InnerProduct";
    layerParams.set("bias_term", false);
    CV_Assert(constBlobs.find(node_proto.input(0)) == constBlobs.end());
    int firstInpDims = outShapes[node_proto.input(0)].size();
    int secondInpDims;

    if (constBlobs.find(node_proto.input(1)) != constBlobs.end())
    {
        Mat blob = getBlob(node_proto, 1);
        secondInpDims = blob.dims;
        layerParams.blobs.push_back(blob.t());
        layerParams.set("num_output", layerParams.blobs[0].size[0]);
    } else {
        secondInpDims = outShapes[node_proto.input(1)].size();
    }
    layerParams.set("axis", firstInpDims - secondInpDims + 1);
    addLayer(layerParams, node_proto);
}

void findBroadAxis(const MatShape& broadShape, const MatShape& outShape, size_t& axis, int& broadAxis)
{
    const size_t diff = outShape.size() - broadShape.size();

    // find the first non-one element of the broadcasting shape
    axis = 0;
    for (; axis < broadShape.size() && broadShape[axis] == 1; ++axis) {}

    // find the last non-one element of the broadcasting shape
    size_t endAxis = broadShape.size();
    for (; endAxis > axis && broadShape[endAxis - 1] == 1; --endAxis) {}

    // find one between axis and endAxis - as it needs to be broadcasted,
    // dimensions from the left of axis and from the right of endAxis will be handled by Scale layer
    broadAxis = -1;
    for (size_t i = axis; i < endAxis; ++i)
    {
        size_t outAxis = i + diff;
        if (outShape[outAxis] == broadShape[i])
        {
            continue;
        }

        // ensure we need to broadcast only 1 dimension in the middle
        CV_Assert(broadShape[i] == 1 && broadAxis == -1);
        broadAxis = static_cast<int>(outAxis);
    }

    axis += diff;
}

// "Mul" "Div"
void ONNXImporter::parseMul(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto_)
{
    opencv_onnx::NodeProto node_proto = node_proto_;
    const std::string& layer_type = node_proto.op_type();
    CV_Assert(node_proto.input_size() == 2);

    bool isDiv = layer_type == "Div";
    int constId = -1;
    bool haveVariables = false;
    for (int i = 0; i < 2; ++i)
    {
        if (constBlobs.find(node_proto.input(i)) != constBlobs.end())
            constId = i;
        else
            haveVariables = true;
    }
    if (constId != -1 && haveVariables)
    {
        Mat blob = getBlob(node_proto, constId);
        blob = blob.reshape(1, 1);
        if (blob.total() == 1) {
            float blob_value = blob.ptr<float>()[0];
            float coeff = isDiv ? 1.0 / blob_value : blob_value;
            layerParams.set("scale", coeff);
            layerParams.type = "Power";
        }
        else {
            if (isDiv)
                divide(1.0, blob, blob);
            layerParams.blobs.push_back(blob);
            layerParams.type = "Scale";
        }
    }
    else if (!haveVariables)
    {
        Mat inp0 = getBlob(node_proto, 0);
        Mat inp1 = getBlob(node_proto, 1);

        if (inp0.size != inp1.size && (inp0.total() != 1 || inp1.total() != 1))
            CV_Error_(Error::StsNotImplemented, ("Different shapes case is not supported with constant inputs: %s", layer_type.c_str()));

        if (inp0.total() == 1 && inp1.total() == 1 && inp0.dims != inp1.dims)
        {
            if (inp0.dims < inp1.dims)
            {
                inp0 = inp0.reshape(1, inp1.dims, inp1.size);
                inp0.dims = inp1.dims;
            }
            else
            {
                inp1 = inp1.reshape(1, inp0.dims, inp0.size);
                inp1.dims = inp0.dims;
            }
        }

        Mat out;
        if (inp0.total() != inp1.total())
        {
            if (inp0.total() == 1)
            {
                float inp0_value = inp0.ptr<float>()[0];
                float coeff = isDiv ? 1.0 / inp0_value : inp0_value;
                multiply(inp1, coeff, out);
            }
            else
            {
                float inp1_value = inp1.ptr<float>()[0];
                float coeff = isDiv ? 1.0 / inp1_value : inp1_value;
                multiply(inp0, coeff, out);
            }

        }
        else
        {
            out = isDiv ? inp0 / inp1 : inp0.mul(inp1);
        }

        if (inp0.dims == 1 && inp1.dims == 1)
            out.dims = 1;  // to workaround dims == 1
        addConstant(layerParams.name, out);
        return;
    }
    else if (outShapes[node_proto.input(0)] == outShapes[node_proto.input(1)])
    {
        layerParams.type = "Eltwise";
        layerParams.set("operation", isDiv ? "div" : "prod");
    }
    else
    {
        // Scale layer allocate output with the first input shape
        if (total(outShapes[node_proto.input(0)]) < total(outShapes[node_proto.input(1)]))
        {
            opencv_onnx::NodeProto proto;
            proto.add_input(node_proto.input(1));
            proto.add_input(node_proto.input(0));
            proto.add_output(layerParams.name);
            node_proto = proto;
        }

        if (isDiv)
        {
            LayerParams powerParams;
            powerParams.name = layerParams.name + "/inv";
            powerParams.type = "Power";
            powerParams.set("power", -1);

            //Create Power layer
            int 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());
            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)];

            //Replace input to Power
            node_proto.set_input(1, powerParams.name);
        }

        const MatShape& broadShape = outShapes[node_proto.input(1)];
        const MatShape& outShape = outShapes[node_proto.input(0)];

        size_t axis = 0;
        int broadAxis = -1;
        findBroadAxis(broadShape, outShape, axis, broadAxis);

        // if there is a one dimension in the middle that should be broadcasted, broadcast it
        if (broadAxis != -1)
        {
            opencv_onnx::NodeProto concat_node_proto = node_proto;
            const std::string& input1 = concat_node_proto.input(1);

            expandMid(layerParams.name, concat_node_proto, input1, outShape[broadAxis]);

            LayerParams concatLP;
            concatLP.name = layerParams.name + "/concat";
            concatLP.set("axis", broadAxis);
            concatLP.type = "Concat";
            concat_node_proto.set_output(0, concatLP.name);

            addLayer(concatLP, concat_node_proto);
            node_proto.set_input(1, concatLP.name);
        }

        CV_Assert(axis != outShape.size());
        layerParams.set("axis", static_cast<int>(axis));
        layerParams.type = "Scale";
    }
    addLayer(layerParams, node_proto);
}

void ONNXImporter::parseConv(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto_)
{
    opencv_onnx::NodeProto node_proto = node_proto_;
    CV_Assert(node_proto.input_size() >= 2);
    layerParams.type = "Convolution";
    for (int j = 1; j < node_proto.input_size(); j++) {
        if (constBlobs.find(node_proto.input(j)) != constBlobs.end())
        {
            layerParams.blobs.push_back(getBlob(node_proto, j));
        }
    }
    int outCn = layerParams.blobs.empty() ? outShapes[node_proto.input(1)][0] : layerParams.blobs[0].size[0];
    layerParams.set("num_output", outCn);

    // Check for asymmetric padding in Conv2D
    if (layerParams.has("pad"))
    {
        bool asymmetricPadding = false;
        DictValue pads = layerParams.get("pad");
        const int dims = pads.size() / 2;
        for (int i = 0; i < dims; ++i)
        {
            if (pads.get<int>(i) != pads.get<int>(i + dims))
            {
                asymmetricPadding = true;
                break;
            }
        }
        if (asymmetricPadding && pads.size() == 4) // [pad_t, pad_l, pad_b, pad_r]
        {
            layerParams.erase("pad");
            // No paddings required for N, C axis
            std::vector<int> paddings(4, 0);
            // Add paddings for H, W axis
            for (int i = 0; i < dims; ++i)
            {
                paddings.push_back(pads.get<int>(i));
                paddings.push_back(pads.get<int>(dims + i));
            }
            LayerParams padLp;
            padLp.name = layerParams.name + "/pad";
            padLp.type = "Padding";
            padLp.set("paddings", DictValue::arrayInt(&paddings[0], paddings.size()));

            opencv_onnx::NodeProto proto;
            proto.add_input(node_proto.input(0));
            proto.add_output(padLp.name);

            addLayer(padLp, proto);
            node_proto.set_input(0, padLp.name);
        }
    }
    addLayer(layerParams, node_proto);
}

void ONNXImporter::parseConvTranspose(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{
    CV_Assert(node_proto.input_size() >= 2);
    layerParams.type = "Deconvolution";
    for (int j = 1; j < node_proto.input_size(); j++) {
        layerParams.blobs.push_back(getBlob(node_proto, j));
    }
    layerParams.set("num_output", layerParams.blobs[0].size[1] * layerParams.get<int>("group", 1));
    layerParams.set("bias_term", node_proto.input_size() == 3);

    if (!layerParams.has("kernel_size"))
        CV_Error(Error::StsNotImplemented,
                 "Required attribute 'kernel_size' is not present.");

    if (layerParams.has("output_shape"))
    {
        const DictValue& outShape = layerParams.get("output_shape");
        DictValue strides = layerParams.get("stride");
        DictValue kernel = layerParams.get("kernel_size");

        String padMode;
        std::vector<int> adjust_pads;
        if (layerParams.has("pad_mode"))
        {
            padMode = toUpperCase(layerParams.get<String>("pad_mode"));
            if (padMode != "SAME" && padMode != "VALID")
                CV_Error(Error::StsError, "Unsupported padding mode " + padMode);

            for (int i = 0; i < strides.size(); i++)
            {
                int sz = outShape.get<int>(2 + i);
                int stride = strides.get<int>(i);
                adjust_pads.push_back(padMode == "SAME"? (sz - 1) % stride :
                                                         (sz - kernel.get<int>(i)) % stride);
            }
            layerParams.set("adj", DictValue::arrayInt(&adjust_pads[0], adjust_pads.size()));
        }
    }
    else if (layerParams.has("output_padding"))
    {
        replaceLayerParam(layerParams, "output_padding", "adj");
    }
    addLayer(layerParams, node_proto);
}

void ONNXImporter::parseTranspose(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{
    int depth = layerParams.get<int>("depth", CV_32F);
    layerParams.type = (depth == CV_8S) ? "PermuteInt8" : "Permute";
    replaceLayerParam(layerParams, "perm", "order");
    if (!layerParams.has("order")) {
        MatShape inpShape = outShapes[node_proto.input(0)];
        size_t dims = inpShape.size();
        std::vector<int> perm(dims);
        for (size_t d = 0; d < dims; ++d)
        {
            perm[d] = static_cast<int>(dims - 1 - d);
        }
        layerParams.set("order", DictValue::arrayInt(perm.data(), perm.size()));
    }

    CV_Assert(node_proto.input_size() == 1);
    if (constBlobs.find(node_proto.input(0)) != constBlobs.end())
    {
        std::vector<Mat> inputs(1, getBlob(node_proto, 0)), transposed;
        runLayer(layerParams, inputs, transposed);
        CV_Assert(transposed.size() == 1);
        addConstant(layerParams.name, transposed[0]);
        return;
    }
    addLayer(layerParams, node_proto);
}

void ONNXImporter::parseSqueeze(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{
    CV_Assert_N(node_proto.input_size() == 1, layerParams.has("axes"));
    DictValue axes_dict = layerParams.get("axes");
    MatShape inpShape = outShapes[node_proto.input(0)];

    std::vector<bool> maskedAxes(inpShape.size(), false);
    for (int i = 0; i < axes_dict.size(); ++i)
    {
        int axis = axes_dict.getIntValue(i);
        CV_CheckLE(axis, static_cast<int>(inpShape.size()), "Squeeze axis");
        maskedAxes[axis] = inpShape[axis] == 1;
    }
    MatShape outShape;
    for (int i = 0; i < inpShape.size(); ++i)
    {
        if (!maskedAxes[i])
            outShape.push_back(inpShape[i]);
    }
    if (outShape.size() != inpShape.size())
    {
        layerParams.type = "Reshape";
        layerParams.set("dim", DictValue::arrayInt(&outShape[0], outShape.size()));
        if (hasDynamicShapes)
        {
            std::vector<int> dynamicAxes;
            std::vector<int> inputIndices;
            for (int index = 0; index < inpShape.size(); ++index)
            {
                if (!maskedAxes[index])
                    inputIndices.push_back(index);
            }
            for (int index = 0; index < outShape.size(); ++index)
                dynamicAxes.push_back(index);
            layerParams.set("dynamic_axes", DictValue::arrayInt(dynamicAxes.data(), dynamicAxes.size()));
            layerParams.set("input_indices", DictValue::arrayInt(inputIndices.data(), inputIndices.size()));
        }
    }
    else
        layerParams.type = "Identity";

    if (constBlobs.find(node_proto.input(0)) != constBlobs.end())
    {
        Mat inp = getBlob(node_proto, 0);
        Mat out = inp.reshape(1, outShape);
        out.dims = outShape.size();  // to workaround dims == 1
        addConstant(layerParams.name, out);
        return;
    }
    int depth = layerParams.get<int>("depth", CV_32F);
    layerParams.type += (depth == CV_8S) ? "Int8" : "";
    addLayer(layerParams, node_proto);
}

void ONNXImporter::parseFlatten(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{
    CV_CheckEQ(node_proto.input_size(), 1, "");
    if (constBlobs.find(node_proto.input(0)) != constBlobs.end())
    {
        Mat input = getBlob(node_proto, 0);
        int axis = normalize_axis(layerParams.get<int>("axis", 1), input.dims);

        std::vector<int> out_size(&input.size[0], &input.size[0] + axis);
        out_size.push_back(input.total(axis));
        Mat output = input.reshape(1, out_size);
        addConstant(layerParams.name, output);
        return;
    }
    addLayer(layerParams, node_proto);
}

void ONNXImporter::parseUnsqueeze(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{
    CV_Assert(node_proto.input_size() == 1 || node_proto.input_size() == 2);
    DictValue axes;
    if (node_proto.input_size() == 2)
    {
        Mat blob = getBlob(node_proto, 1);
        axes = DictValue::arrayInt(blob.ptr<int>(), blob.total());
    }
    else
        axes = layerParams.get("axes");

    if (constBlobs.find(node_proto.input(0)) != constBlobs.end())
    {
        // Constant input.
        Mat input = getBlob(node_proto, 0);

        std::vector<int> dims;
        for (int j = 0; j < input.dims; j++) {
            dims.push_back(input.size[j]);
        }
        CV_Assert(axes.getIntValue(axes.size()-1) <= dims.size());
        for (int j = 0; j < axes.size(); j++) {
            dims.insert(dims.begin() + axes.getIntValue(j), 1);
        }

        Mat out = input.reshape(0, dims);
        addConstant(layerParams.name, out);
        return;
    }

    // Variable input.
    if (axes.size() != 1)
        CV_Error(Error::StsNotImplemented, "Multidimensional unsqueeze");

    int depth = layerParams.get<int>("depth", CV_32F);

    MatShape inpShape = outShapes[node_proto.input(0)];
    int axis = axes.getIntValue(0);
    CV_Assert(0 <= axis && axis <= inpShape.size());
    std::vector<int> outShape = inpShape;
    outShape.insert(outShape.begin() + axis, 1);
    layerParams.type = (depth == CV_8S) ? "ReshapeInt8" : "Reshape";
    layerParams.set("dim", DictValue::arrayInt(&outShape[0], outShape.size()));
    if (hasDynamicShapes)
    {
        std::vector<int> dynamicAxes;
        std::vector<int> inputIndices;
        for (int index = 0; index < outShape.size(); ++index) {
            if (index != axis)
                dynamicAxes.push_back(index);
        }
        for (int index = 0; index < inpShape.size(); ++index)
            inputIndices.push_back(index);
        layerParams.set("dynamic_axes", DictValue::arrayInt(dynamicAxes.data(), dynamicAxes.size()));
        layerParams.set("input_indices", DictValue::arrayInt(inputIndices.data(), inputIndices.size()));
    }
    addLayer(layerParams, node_proto);
}

void ONNXImporter::parseExpand(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto_)
{
    opencv_onnx::NodeProto node_proto = node_proto_;
    CV_CheckEQ(node_proto.input_size(), 2, "");
    const std::string& input0 = node_proto.input(0);
    const std::string& input1 = node_proto.input(1);
    Mat newShapeMat = getBlob(input1);
    MatShape targetShape(newShapeMat.ptr<int>(), newShapeMat.ptr<int>() + newShapeMat.total());

    MatShape inpShape;
    bool haveVariables = constBlobs.find(input0) == constBlobs.end();
    if (haveVariables)
    {
        IterShape_t shapeIt = outShapes.find(input0);
        CV_Assert(shapeIt != outShapes.end());
        inpShape = shapeIt->second;
    }
    else
    {
        inpShape = shape(getBlob(input0));
    }

    String srcName = input0;
    // Unsqueeze and repeat along new axis
    if (targetShape.size() == inpShape.size() + 1)
    {
        inpShape.insert(inpShape.begin(), targetShape.size() - inpShape.size(), 1);
        for (int i = 0; i < targetShape.size(); i++)
        {
            if (abs(targetShape[i]) == 1)
                targetShape[i] = inpShape[i];
        }
        if (haveVariables)
        {
            LayerParams reshapeLp;
            reshapeLp.name = layerParams.name + "/reshape";
            reshapeLp.type = "Reshape";
            CV_Assert(layer_id.find(reshapeLp.name) == layer_id.end());
            reshapeLp.set("dim", DictValue::arrayInt(&inpShape[0], inpShape.size()));

            opencv_onnx::NodeProto proto;
            proto.add_input(node_proto.input(0));
            proto.add_output(reshapeLp.name);
            addLayer(reshapeLp, proto);
            srcName = reshapeLp.name;
        }
    }
    CV_CheckEQ(inpShape.size(), targetShape.size(), "Unsupported Expand op with different dims");

    std::vector<int> broadcast_axes;
    // shapes aren't right-aligned here because targetShape.size() == inpShape.size()
    for (int i = 0; i < targetShape.size(); i++)
    {
        if (targetShape[i] != inpShape[i])
        {
            if (inpShape[i] == 1)
            {
                broadcast_axes.push_back(i);
            }
            else if (targetShape[i] != 1)
            {
                CV_Error(Error::StsError, format("Could not be broadcast by axis: %d", i));
            }
        }
    }

    if (!haveVariables)
    {
        if (broadcast_axes.size() != 1)
            CV_Error(Error::StsNotImplemented, "Expand op doesn't support multiple axes for constant input");

        Mat input = getBlob(node_proto, 0);
        input = input.reshape(0, total(inpShape, 0, broadcast_axes[0]));
        Mat output = cv::repeat(input, 1, targetShape[broadcast_axes[0]]);
        output = output.reshape(0, targetShape);
        addConstant(layerParams.name, output);
        return;
    }

    if (broadcast_axes.size() == 2 &&
        broadcast_axes[0] == broadcast_axes[1] - 1 && broadcast_axes[1] == inpShape.size() - 1)
    {
        LayerParams constParams;
        constParams.name = layerParams.name + "/const";
        CV_Assert(layer_id.find(constParams.name) == layer_id.end());
        constParams.type = "Const";

        Mat inp = Mat::ones(newShapeMat.total(), newShapeMat.ptr<int>(), CV_32F);
        constParams.blobs.push_back(inp);

        opencv_onnx::NodeProto proto;
        proto.add_output(constParams.name);
        addLayer(constParams, proto);

        layerParams.type = "Scale";
        layerParams.set("bias_term", false);
        node_proto.set_input(0, constParams.name);
        node_proto.set_input(1, srcName);
    }
    else if (broadcast_axes.size() == 1 && broadcast_axes[0] <= 1)
    {
        expandMid(layerParams.name, node_proto, srcName, targetShape[broadcast_axes[0]]);

        layerParams.set("axis", broadcast_axes[0]);
        layerParams.type = "Concat";
        node_proto.set_output(0, layerParams.name);
    }
    else if (broadcast_axes.empty())
    {
        layerParams.type = "Identity";
    }
    else
        CV_Error(Error::StsNotImplemented, "Unsupported Expand op");
    addLayer(layerParams, node_proto);
}

void ONNXImporter::parseReshape(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{
    CV_Assert(node_proto.input_size() == 2 || layerParams.has("shape"));
    int depth = layerParams.get<int>("depth", CV_32F);
    layerParams.type += (depth == CV_8S) ? "Int8" : "";

    if (node_proto.input_size() == 2) {
        Mat blob = getBlob(node_proto, 1);
        CV_Assert(blob.type() == CV_32SC1);

        layerParams.set("dim", DictValue::arrayInt<int*>(blob.ptr<int>(), blob.total()));

        if (layer_id.find(node_proto.input(0)) == layer_id.end()) {
            std::vector<Mat> inputs(1, getBlob(node_proto, 0)), outputs;
            runLayer(layerParams, inputs, outputs);
            addConstant(layerParams.name, outputs[0]);
            return;
        }
    }
    else {
        DictValue shape = layerParams.get("shape");
        std::vector<int> dim;
        for (int j = 0; j < shape.size(); j++) {
            dim.push_back(shape.getIntValue(j));
        }

        if (layer_id.find(node_proto.input(0)) == layer_id.end()) {
            Mat input = getBlob(node_proto, 0);
            Mat out = input.reshape(0, dim);
            addConstant(layerParams.name, out);
            return;
        }
        replaceLayerParam(layerParams, "shape", "dim");
    }
    addLayer(layerParams, node_proto);
}

void ONNXImporter::parsePad(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{
    int depth = layerParams.get<int>("depth", CV_32F);
    layerParams.type = (depth == CV_8S) ? "PaddingInt8" : "Padding";
    replaceLayerParam(layerParams, "mode", "type");
    if (node_proto.input_size() == 3 || node_proto.input_size() == 2)
    {
        // Paddings are in order begin0, begin1, .. beginN, end0, end1, ..., endN.
        // We need to shuffle it to begin0, end0, begin1, end1, ...
        Mat paddings = getBlob(node_proto, 1).reshape(1, 2);
        paddings = paddings.t();
        layerParams.set("paddings", DictValue::arrayInt(paddings.ptr<int>(), paddings.total()));

        if (node_proto.input_size() == 3)
        {
            Mat value = getBlob(node_proto, 2);
            float padValue = (depth == CV_8S) ? (float)value.ptr<int8_t>()[0] : value.ptr<float>()[0];
            layerParams.set("value", padValue);
        }
    }
    addLayer(layerParams, node_proto);
}

void ONNXImporter::parseShape(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{
    CV_Assert(node_proto.input_size() == 1);
    IterShape_t shapeIt = outShapes.find(node_proto.input(0));
    CV_Assert(shapeIt != outShapes.end());
    const MatShape& inpShape = shapeIt->second;

    Mat shapeMat(inpShape.size(), 1, CV_32S);
    for (int j = 0; j < inpShape.size(); ++j)
        shapeMat.at<int>(j) = inpShape[j];
    shapeMat.dims = 1;

    addConstant(layerParams.name, shapeMat);
}

void ONNXImporter::parseCast(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{
    if (constBlobs.find(node_proto.input(0)) != constBlobs.end())
    {
        Mat blob = getBlob(node_proto, 0);
        int type;
        switch (layerParams.get<int>("to"))
        {
            case opencv_onnx::TensorProto_DataType_FLOAT:   type = CV_32F; break;
            case opencv_onnx::TensorProto_DataType_UINT8:   type = CV_8U; break;
            case opencv_onnx::TensorProto_DataType_UINT16:  type = CV_16U; break;
            case opencv_onnx::TensorProto_DataType_FLOAT16: type = CV_16S; break;
            case opencv_onnx::TensorProto_DataType_INT8:
            case opencv_onnx::TensorProto_DataType_INT16:
            case opencv_onnx::TensorProto_DataType_INT32:
            case opencv_onnx::TensorProto_DataType_INT64:   type = CV_32S; break;
            default: type = blob.type();
        }
        Mat dst;
        blob.convertTo(dst, type);
        dst.dims = blob.dims;
        addConstant(layerParams.name, dst);
        return;
    }
    else
        layerParams.type = "Identity";
    addLayer(layerParams, node_proto);
}

void ONNXImporter::parseConstantFill(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{
    int depth = CV_32F;
    float fill_value;
    if (!layerParams.blobs.empty())
    {
        CV_Assert(!layerParams.has("value"));
        depth = layerParams.blobs[0].depth();
        Mat floats;
        layerParams.blobs[0].convertTo(floats, CV_32F);
        fill_value = floats.at<float>(0, 0);
    }
    else
        fill_value = layerParams.get("value", 0);

    MatShape inpShape = getBlob(node_proto, 0);
    for (int i = 0; i < inpShape.size(); i++)
        CV_CheckGT(inpShape[i], 0, "");
    Mat tensor(inpShape.size(), &inpShape[0], depth, Scalar(fill_value));
    addConstant(layerParams.name, tensor);
}

void ONNXImporter::parseGather(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto_)
{
    opencv_onnx::NodeProto node_proto = node_proto_;
    CV_Assert(node_proto.input_size() == 2);
    Mat indexMat = getBlob(node_proto, 1);
    CV_Assert_N(indexMat.type() == CV_32S, indexMat.total() == 1);
    int index = indexMat.at<int>(0);
    int axis = layerParams.get<int>("axis", 0);

    if ((constBlobs.find(node_proto.input(0)) != constBlobs.end()))
    {
        Mat input = getBlob(node_proto, 0);
        Mat out;
        std::vector<cv::Range> ranges(input.dims, Range::all());
        ranges[axis] = Range(index, index + 1);

        out = input(ranges);
        MatShape outShape = shape(out);
        if (outShape.size() > 1)
        {
            outShape.erase(outShape.begin() + axis);
            out.reshape(0, outShape);
        } else {
            out.dims = 1;
        }
        addConstant(layerParams.name, out);
        return;
    }
    else
    {
        IterShape_t shapeIt = outShapes.find(node_proto.input(0));
        CV_Assert(shapeIt != outShapes.end());
        MatShape inpShape = shapeIt->second;

        LayerParams sliceLp;
        sliceLp.type = "Slice";
        sliceLp.name = inpShape.size() > 1 ? layerParams.name + "/slice" : layerParams.name;
        std::vector<int> begin(inpShape.size(), 0);
        std::vector<int> end(inpShape.size(), -1);
        begin[axis] = index;
        end[axis] = index + 1;

        cv::dnn::DictValue paramBegin = cv::dnn::DictValue::arrayInt(begin.data(), begin.size());
        cv::dnn::DictValue paramEnd = cv::dnn::DictValue::arrayInt(end.data(), end.size());
        sliceLp.set("begin", paramBegin);
        sliceLp.set("end", paramEnd);
        sliceLp.set("has_dynamic_shapes", hasDynamicShapes);

        if (inpShape.size() > 1)
        {
            opencv_onnx::NodeProto proto;
            proto.add_input(node_proto.input(0));
            proto.add_output(sliceLp.name);
            addLayer(sliceLp, proto);

            inpShape.erase(inpShape.begin() + axis);
            layerParams.type = "Reshape";
            layerParams.set("axis", 0);
            layerParams.set("dim", DictValue::arrayInt(&inpShape[0], inpShape.size()));
            if (hasDynamicShapes)
            {
                std::vector<int> dynamicAxes;
                std::vector<int> inputIndices;
                for (int index = 0; index < inpShape.size(); ++index)
                    dynamicAxes.push_back(index);
                for (int index = 0; index < inpShape.size(); ++index)
                    inputIndices.push_back(index);
                layerParams.set("dynamic_axes", DictValue::arrayInt(dynamicAxes.data(), dynamicAxes.size()));
                layerParams.set("input_indices", DictValue::arrayInt(inputIndices.data(), inputIndices.size()));
            }
            node_proto.set_input(0, sliceLp.name);
        }
        else
        {
            layerParams = sliceLp;
        }
    }
    addLayer(layerParams, node_proto);
}

void ONNXImporter::parseConcat(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{
    bool hasVariableInps = false;
    for (int i = 0; i < node_proto.input_size(); ++i)
    {
        if (layer_id.find(node_proto.input(i)) != layer_id.end())
        {
            hasVariableInps = true;
            break;
        }
    }

    if (!hasVariableInps)
    {
        std::vector<Mat> inputs(node_proto.input_size()), concatenated;
        // Due constant folding we can get inputs with different number of dimensions
        // Insert the missing dimension to inputs
        MatShape inputShape;
        for (size_t i = 0; i < inputs.size(); ++i)
        {
            inputs[i] = getBlob(node_proto, i);
            if (inputs[i].size.dims() > inputShape.size())
            {
                inputShape = shape(inputs[i]);
            }
        }

        // Concat-1 has default value for axis is 1: https://github.com/onnx/onnx/blob/master/docs/Changelog.md#Concat-1
        int axis = layerParams.get<int>("axis", 1);
        for (size_t i = 0; i < inputs.size(); ++i)
        {
            MatShape targetShape = inputShape;
            targetShape[axis] = shape(inputs[i])[axis];
            CV_CheckEQ(total(targetShape), total(shape(inputs[i])), "");
            inputs[i] = inputs[i].reshape(0, targetShape);
        }
        runLayer(layerParams, inputs, concatenated);

        CV_Assert(concatenated.size() == 1);
        addConstant(layerParams.name, concatenated[0]);
        return;
    }
    else
    {
        for (int i = 0; i < node_proto.input_size(); ++i)
        {
            if (constBlobs.find(node_proto.input(i)) != constBlobs.end())
            {
                LayerParams constParams;
                constParams.name = node_proto.input(i);
                constParams.type = "Const";
                constParams.blobs.push_back(getBlob(node_proto, i));

                opencv_onnx::NodeProto proto;
                proto.add_output(constParams.name);
                addLayer(constParams, proto);
            }
        }
    }
    addLayer(layerParams, node_proto);
}

void ONNXImporter::parseResize(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{
    for (int i = 1; i < node_proto.input_size(); i++)
        CV_Assert(layer_id.find(node_proto.input(i)) == layer_id.end());

    int depth = layerParams.get<int>("depth", CV_32F);
    layerParams.type += (depth == CV_8S) ? "Int8" : "";

    if (layerParams.has("coordinate_transformation_mode"))
    {
        String interp_mode = layerParams.get<String>("coordinate_transformation_mode");
        CV_Assert_N(interp_mode != "tf_crop_and_resize", interp_mode != "tf_half_pixel_for_nn");

        layerParams.set("align_corners", interp_mode == "align_corners");
        if (layerParams.get<String>("mode") == "linear")
        {
            layerParams.set("mode", interp_mode == "pytorch_half_pixel" || interp_mode == "half_pixel" ?
                                    "opencv_linear" : "bilinear");
        }
    }
    if (layerParams.get<String>("mode") == "linear" && framework_name == "pytorch")
        layerParams.set("mode", "opencv_linear");

    // input = [X, scales], [X, roi, scales] or [x, roi, scales, sizes]
    int foundScaleId = hasDynamicShapes ? node_proto.input_size() - 1
                                        : node_proto.input_size() > 2 ? 2 : 1;

    Mat scales = getBlob(node_proto, foundScaleId);
    if (scales.total() == 4)
    {
        layerParams.set("zoom_factor_y", scales.at<float>(2));
        layerParams.set("zoom_factor_x", scales.at<float>(3));
    }
    else
    {
        const std::string& inputLast = node_proto.input(node_proto.input_size() - 1);
        if (constBlobs.find(inputLast) != constBlobs.end())
        {
            Mat shapes = getBlob(inputLast);
            CV_CheckEQ(shapes.size[0], 4, "");
            CV_CheckEQ(shapes.size[1], 1, "");
            CV_CheckDepth(shapes.depth(), shapes.depth() == CV_32S || shapes.depth() == CV_32F, "");
            if (shapes.depth() == CV_32F)
                shapes.convertTo(shapes, CV_32S);
            layerParams.set("width", shapes.at<int>(3));
            layerParams.set("height", shapes.at<int>(2));
        }
    }
    replaceLayerParam(layerParams, "mode", "interpolation");
    addLayer(layerParams, node_proto);
}

void ONNXImporter::parseUpsample(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{
    //fused from Resize Subgraph
    if (layerParams.has("coordinate_transformation_mode"))
    {
        String interp_mode = layerParams.get<String>("coordinate_transformation_mode");
        CV_Assert_N(interp_mode != "tf_crop_and_resize", interp_mode != "tf_half_pixel_for_nn");

        layerParams.set("align_corners", interp_mode == "align_corners");
        if (layerParams.get<String>("mode") == "linear")
        {
            layerParams.set("mode", interp_mode == "pytorch_half_pixel" ?
                                    "opencv_linear" : "bilinear");
        }
    }
    if (layerParams.get<String>("mode") == "linear" && framework_name == "pytorch")
        layerParams.set("mode", "opencv_linear");

    layerParams.type = "Resize";
    if (layerParams.has("scales"))
    {
        // Pytorch layer
        DictValue scales = layerParams.get("scales");
        CV_Assert(scales.size() == 4);
        layerParams.set("zoom_factor_y", scales.getIntValue(2));
        layerParams.set("zoom_factor_x", scales.getIntValue(3));
    }
    else if (layerParams.has("height_scale") && layerParams.has("width_scale"))
    {
        // Caffe2 layer
        replaceLayerParam(layerParams, "height_scale", "zoom_factor_y");
        replaceLayerParam(layerParams, "width_scale", "zoom_factor_x");
    }
    else
    {
        // scales as input
        const std::string& input1 = node_proto.input(1);
        if (constBlobs.find(input1) != constBlobs.end())
        {
            Mat scales = getBlob(input1);
            CV_Assert(scales.total() == 4);
            layerParams.set("zoom_factor_y", scales.at<float>(2));
            layerParams.set("zoom_factor_x", scales.at<float>(3));
        }
    }
    replaceLayerParam(layerParams, "mode", "interpolation");
    addLayer(layerParams, node_proto);
}

void ONNXImporter::parseSoftMax(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{
    const std::string& layer_type = node_proto.op_type();
    layerParams.type = "Softmax";
    layerParams.set("log_softmax", layer_type == "LogSoftmax");
    addLayer(layerParams, node_proto);
}

void ONNXImporter::parseDetectionOutput(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto_)
{
    opencv_onnx::NodeProto node_proto = node_proto_;
    CV_CheckEQ(node_proto.input_size(), 3, "");
    if (constBlobs.find(node_proto.input(2)) != constBlobs.end())
    {
        Mat priors = getBlob(node_proto, 2);

        LayerParams constParams;
        constParams.name = layerParams.name + "/priors";
        constParams.type = "Const";
        constParams.blobs.push_back(priors);

        opencv_onnx::NodeProto priorsProto;
        priorsProto.add_output(constParams.name);
        addLayer(constParams, priorsProto);

        node_proto.set_input(2, constParams.name);
    }
    addLayer(layerParams, node_proto);
}

void ONNXImporter::parseCumSum(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{
    layerParams.type = "CumSum";

    // Get axis.
    const std::string& input1 = node_proto.input(1);

    if (constBlobs.find(input1) != constBlobs.end())
    {
        Mat axis_blob = getBlob(input1);
        CV_Assert(axis_blob.total() == 1u);
        layerParams.set("axis", axis_blob.at<int>(0));
    }

    addLayer(layerParams, 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())
            layerParams.blobs.push_back(getBlob(node_proto, j));
    }
    addLayer(layerParams, node_proto);
}

void ONNXImporter::parseQuantDequant(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{
    CV_Assert(node_proto.input_size() == 3);
    layerParams.type = (node_proto.op_type() == "QuantizeLinear") ? "Quantize" : "Dequantize";

    if (node_proto.op_type() == "DequantizeLinear")
    {
        Mat scale = getBlob(node_proto, 1);
        Mat zeropoint = getBlob(node_proto, 2);

        layerParams.set("scales", DictValue::arrayReal(scale.ptr<float>(), 1));
        layerParams.set("zeropoints", DictValue::arrayInt(zeropoint.ptr<int8_t>(), 1));
    }
    addLayer(layerParams, node_proto);
}

void ONNXImporter::parseQConv(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{
    int ninputs = node_proto.input_size();
    CV_Assert(ninputs == 8 || ninputs == 9);

    Mat inp_sc = getBlob(node_proto, 1);
    Mat inp_zp = getBlob(node_proto, 2);

    Mat weights = getBlob(node_proto, 3);
    int outCn = weights.size[0];
    Mat w_scale = getBlob(node_proto, 4);
    CV_Assert(w_scale.total() == 1 || w_scale.total() == outCn);
    Mat wt_sc = (w_scale.total() == outCn) ? w_scale : Mat(1, outCn, CV_32F, Scalar(w_scale.at<float>(0)));

    Mat out_sc = getBlob(node_proto, 6);
    Mat bias = (ninputs == 9) ? getBlob(node_proto, 8) : Mat::zeros(1, outCn, CV_32S);

    Mat weights_2d = weights.reshape(1, outCn);
    Mat biasFused(1, outCn, CV_32S);
    Mat outputMultiplier(1, outCn, CV_32F);
    for (int i = 0; i < outCn; i++)
    {
        biasFused.at<int>(i) = bias.at<int>(i) - inp_zp.at<int8_t>(0)*(cv::sum(weights_2d.row(i))[0]);
        outputMultiplier.at<float>(i) = (inp_sc.at<float>(0) * wt_sc.at<float>(i)) / out_sc.at<float>(0);
    }

    layerParams.type = "ConvolutionInt8";
    layerParams.set("num_output", outCn);
    layerParams.set("input_zeropoint", inp_zp.at<int8_t>(0));
    layerParams.blobs.push_back(weights);
    layerParams.blobs.push_back(biasFused);
    layerParams.blobs.push_back(outputMultiplier);
    addLayer(layerParams, node_proto);
}

void ONNXImporter::parseQMatMul(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{
    int ninputs = node_proto.input_size();
    CV_Assert(ninputs == 8);

    if (constBlobs.find(node_proto.input(3)) == constBlobs.end())
        CV_Error(Error::StsNotImplemented, "Variable weights is not supported");

    int firstInpDims = outShapes[node_proto.input(0)].size();

    Mat inp_sc = getBlob(node_proto, 1);
    Mat inp_zp = getBlob(node_proto, 2);

    Mat weights = getBlob(node_proto, 3).t();
    int outCn = weights.size[0];
    int secondInpDims = weights.dims;

    Mat w_scale = getBlob(node_proto, 4);
    CV_Assert(w_scale.total() == 1 || w_scale.total() == outCn);
    Mat wt_sc = (w_scale.total() == outCn) ? w_scale : Mat(1, outCn, CV_32F, Scalar(w_scale.at<float>(0)));
    Mat out_sc = getBlob(node_proto, 6);

    Mat bias(1, outCn, CV_32S);
    Mat outputMultiplier(1, outCn, CV_32F);
    for (int i = 0; i < outCn; i++)
    {
        bias.at<int>(i) = -inp_zp.at<int8_t>(0)*(cv::sum(weights.row(i))[0]);
        outputMultiplier.at<float>(i) = (inp_sc.at<float>(0) * wt_sc.at<float>(i)) / out_sc.at<float>(0);
    }

    layerParams.type = "InnerProductInt8";
    layerParams.set("num_output", outCn);
    layerParams.set("axis", firstInpDims - secondInpDims + 1);
    layerParams.blobs.push_back(weights);
    layerParams.blobs.push_back(bias);
    layerParams.blobs.push_back(outputMultiplier);
    addLayer(layerParams, node_proto);
}

void ONNXImporter::parseQEltwise(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto_)
{
    opencv_onnx::NodeProto node_proto = node_proto_;
    CV_Assert(node_proto.input_size() == 8);
    std::string op = (node_proto.op_type() == "QLinearAdd") ? "sum" : "prod";
    int constId = -1;
    for (int i = 0; i < 4; i += 3)
    {
        if (constBlobs.find(node_proto.input(i)) != constBlobs.end())
            constId = i;
    }

    Mat inp_0_sc = getBlob(node_proto, 1);
    Mat inp_0_zp = getBlob(node_proto, 2);

    Mat inp_1_sc = getBlob(node_proto, 4);
    Mat inp_1_zp = getBlob(node_proto, 5);

    // Set 2nd input as the const input
    if (constId == 0)
    {
        cv::swap(inp_0_sc, inp_1_sc);
        cv::swap(inp_0_zp, inp_1_zp);
    }

    float out_sc = getBlob(node_proto, 6).at<float>(0);
    int8_t out_zp = getBlob(node_proto, 7).at<int8_t>(0);

    std::vector<float> inp_scales = {inp_0_sc.at<float>(0), inp_1_sc.at<float>(0)};
    std::vector<int8_t> inp_zps = {inp_0_zp.at<int8_t>(0), inp_1_zp.at<int8_t>(0)};

    std::vector<float> coeffs;
    float offset;
    if (op == "sum")
    {
        coeffs = {inp_scales[0]/out_sc, inp_scales[1]/out_sc};
        offset = out_zp - coeffs[0]*inp_zps[0] - coeffs[1]*inp_zps[1];
    }
    else
    {
        coeffs = {inp_scales[0]/out_sc, inp_scales[1]};
        offset = out_zp;
    }

    if (constId != -1)
    {
        Mat blob = getBlob(node_proto, constId);
        if (blob.total() == 1)
        {
            float val = inp_scales[1] * (blob.at<int8_t>(0) - inp_zps[1]);
            float scale = inp_scales[0] / out_sc;
            if (op == "prod")
                scale *= val;

            float shift = out_zp - scale*inp_zps[0];
            if (op == "sum")
                shift += (val/out_sc);

            LayerParams rescaleParams;
            rescaleParams.name = layerParams.name;
            rescaleParams.type = "Requantize";
            rescaleParams.set("depth", CV_8S);
            rescaleParams.set("scale", scale);
            rescaleParams.set("shift", shift);
            addLayer(rescaleParams, node_proto);
            return;
        }
        else
        {
            MatShape inpShape = outShapes[node_proto.input(3 - constId)];
            if (blob.dims == 2)
                blob = blob.t();

            if (shape(blob) == inpShape)
            {
                LayerParams constParams;
                constParams.name = layerParams.name + "/const";
                constParams.type = "ConstInt8";
                constParams.set("depth", CV_8S);
                constParams.set("scales", DictValue::arrayReal(inp_1_sc.ptr<float>(), 1));
                constParams.set("zeropoints", DictValue::arrayInt(inp_1_zp.ptr<int8_t>(), 1));
                constParams.blobs.push_back(blob);

                int id = dstNet.addLayer(constParams.name, constParams.type, CV_8S, constParams);
                layer_id.insert(std::make_pair(constParams.name, LayerInfo(id, 0)));
                outShapes[constParams.name] = shape(blob);
                node_proto.set_input(constId, constParams.name);

                layerParams.type = "EltwiseInt8";
                layerParams.set("operation", op);
                layerParams.set("coeff", DictValue::arrayReal(coeffs.data(), coeffs.size()));
                layerParams.set("offset", offset);
            }
            else
            {
                layerParams.type = "ScaleInt8";
                layerParams.set("bias_term", op == "sum");
                int axis = 1;
                for (int i = 0; i < graph_proto.initializer_size(); 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();
                        break;
                    }
                }
                layerParams.set("axis", axis);
                blob = blob.reshape(1, 1);
                Mat blob_dequantized;
                blob.convertTo(blob_dequantized, CV_32F, inp_scales[1], -(inp_scales[1] * inp_zps[1]));
                layerParams.blobs.push_back(blob_dequantized);
                layerParams.set("input_scales", DictValue::arrayReal(inp_scales.data(), inp_scales.size()));
            }
        }
    }
    else if (outShapes[node_proto.input(0)] == outShapes[node_proto.input(3)])
    {
        layerParams.type = "EltwiseInt8";
        layerParams.set("operation", op);
        layerParams.set("coeff", DictValue::arrayReal(coeffs.data(), coeffs.size()));
        layerParams.set("offset", offset);
    }
    else
    {
        layerParams.type = "ScaleInt8";
        layerParams.set("bias_term", op == "sum");
        layerParams.set("input_scales", DictValue::arrayReal(inp_scales.data(), inp_scales.size()));
    }

    layerParams.set("input_zeropoints", DictValue::arrayInt(inp_zps.data(), inp_zps.size()));
    addLayer(layerParams, node_proto);
}

void ONNXImporter::parseQLeakyRelu(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{
    CV_Assert(node_proto.input_size() == 5);

    float slope = layerParams.get<float>("alpha");
    float inp_sc = getBlob(node_proto, 1).at<float>(0);
    int8_t inp_zp = getBlob(node_proto, 2).at<int8_t>(0);
    float out_sc = getBlob(node_proto, 3).at<float>(0);
    int8_t out_zp = getBlob(node_proto, 4).at<int8_t>(0);

    Mat lookUpTable(1, 256, CV_8S);
    int8_t* table = lookUpTable.ptr<int8_t>();
    for (int i = -128; i < 128; i++)
    {
        float x = inp_sc*(i - inp_zp);
        float y = x >= 0.f ? x : slope*x;
        int quantized = out_zp + cvRound(y/out_sc);
        table[i+128] = saturate_cast<int8_t>(quantized);
    }

    layerParams.type = "ReLUInt8";
    layerParams.blobs.push_back(lookUpTable);
    addLayer(layerParams, node_proto);
}

void ONNXImporter::parseQSigmoid(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{
    CV_Assert(node_proto.input_size() == 5);

    float inp_sc = getBlob(node_proto, 1).at<float>(0);
    int8_t inp_zp = getBlob(node_proto, 2).at<int8_t>(0);
    float out_sc = getBlob(node_proto, 3).at<float>(0);
    int8_t out_zp = getBlob(node_proto, 4).at<int8_t>(0);

    Mat lookUpTable(1, 256, CV_8S);
    int8_t* table = lookUpTable.ptr<int8_t>();
    for (int i = -128; i < 128; i++)
    {
        float x = inp_sc*(i - inp_zp);
        float y = 1.f/(1.f + std::exp(-x));
        int quantized = out_zp + cvRound(y/out_sc);
        table[i+128] = saturate_cast<int8_t>(quantized);
    }

    layerParams.type = "SigmoidInt8";
    layerParams.blobs.push_back(lookUpTable);
    addLayer(layerParams, node_proto);
}

void ONNXImporter::parseQAvgPool(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{
    CV_Assert(node_proto.input_size() == 5);
    float inp_sc = getBlob(node_proto, 1).at<float>(0);
    int8_t inp_zp = getBlob(node_proto, 2).at<int8_t>(0);
    float out_sc = getBlob(node_proto, 3).at<float>(0);

    layerParams.type = "PoolingInt8";
    layerParams.set("pool", "ave");
    layerParams.set("global_pooling", node_proto.op_type() == "QLinearGlobalAveragePool");
    layerParams.set("multiplier", inp_sc/out_sc);
    layerParams.set("input_zeropoint", inp_zp);
    addLayer(layerParams, node_proto);
}

void ONNXImporter::parseQConcat(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto_)
{
    opencv_onnx::NodeProto node_proto = node_proto_;
    layerParams.type = "ConcatInt8";
    int num_inputs = node_proto.input_size();

    float out_scale = getBlob(node_proto, 0).at<float>(0);
    int out_zp = getBlob(node_proto, 1).at<int8_t>(0);

    for (int i = 2; i < num_inputs; i += 3)
    {
        float inp_scale = getBlob(node_proto, i + 1).at<float>(0);
        int inp_zp = getBlob(node_proto, i + 2).at<int8_t>(0);

        if (inp_scale != out_scale || inp_zp != out_zp)
        {
            float scale = inp_scale/out_scale;
            float shift = out_zp - scale*inp_zp;

            if (constBlobs.find(node_proto.input(i)) != constBlobs.end())
            {
                Mat blob = getBlob(node_proto, i);
                Mat blob_rescaled;
                blob.convertTo(blob_rescaled, CV_8S, scale, shift);
                constBlobs[node_proto.input(i)] = blob_rescaled;
            }
            else
            {
                LayerParams rescaleParams;
                rescaleParams.name = node_proto.input(i) + "/rescale";
                rescaleParams.type = "Requantize";
                rescaleParams.set("depth", CV_8S);
                rescaleParams.set("scale", scale);
                rescaleParams.set("shift", shift);

                opencv_onnx::NodeProto proto;
                proto.add_input(node_proto.input(i));
                proto.add_output(rescaleParams.name);
                addLayer(rescaleParams, proto);
                node_proto.set_input(i, rescaleParams.name);
            }
        }
    }

    bool hasVariableInps = false;
    for (int i = 2; i < num_inputs; i += 3)
    {
        if (layer_id.find(node_proto.input(i)) != layer_id.end())
        {
            hasVariableInps = true;
            break;
        }
    }

    if (!hasVariableInps)
    {
        std::vector<Mat> inputs, concatenated;
        MatShape inputShape;
        for (size_t i = 2; i < num_inputs; i += 3)
        {
            Mat blob = getBlob(node_proto, i);
            if (blob.size.dims() > inputShape.size())
            {
                inputShape = shape(blob);
            }
            inputs.push_back(blob);
        }

        int axis = layerParams.get<int>("axis", 1);
        for (size_t i = 0; i < inputs.size(); ++i)
        {
            MatShape targetShape = inputShape;
            targetShape[axis] = shape(inputs[i])[axis];
            CV_CheckEQ(total(targetShape), total(shape(inputs[i])), "");
            inputs[i] = inputs[i].reshape(0, targetShape);
        }
        runLayer(layerParams, inputs, concatenated);
        CV_Assert(concatenated.size() == 1);
        addConstant(layerParams.name, concatenated[0]);
        return;
    }
    else
    {
        for (int i = 2; i < num_inputs; i += 3)
        {
            if (constBlobs.find(node_proto.input(i)) != constBlobs.end())
            {
                LayerParams constParams;
                constParams.name = node_proto.input(i);
                constParams.type = "ConstInt8";
                constParams.blobs.push_back(getBlob(node_proto, i));
                constParams.set("depth", CV_8S);

                opencv_onnx::NodeProto proto;
                proto.add_output(constParams.name);
                addLayer(constParams, proto);
            }
        }
    }
    addLayer(layerParams, node_proto);
}

const ONNXImporter::DispatchMap ONNXImporter::buildDispatchMap()
{
    DispatchMap dispatch;

    dispatch["ArgMax"] = dispatch["ArgMin"] = &ONNXImporter::parseArg;
    dispatch["MaxUnpool"] = &ONNXImporter::parseMaxUnpool;
    dispatch["MaxPool"] = &ONNXImporter::parseMaxPool;
    dispatch["AveragePool"] = &ONNXImporter::parseAveragePool;
    dispatch["GlobalAveragePool"] = dispatch["GlobalMaxPool"] = dispatch["ReduceMean"] = dispatch["ReduceSum"] =
            dispatch["ReduceMax"] = &ONNXImporter::parseReduce;
    dispatch["Slice"] = &ONNXImporter::parseSlice;
    dispatch["Split"] = &ONNXImporter::parseSplit;
    dispatch["Add"] = dispatch["Sum"] = dispatch["Sub"] = &ONNXImporter::parseBias;
    dispatch["Pow"] = &ONNXImporter::parsePow;
    dispatch["Min"] = dispatch["Max"] = &ONNXImporter::parseMinMax;
    dispatch["Neg"] = &ONNXImporter::parseNeg;
    dispatch["Constant"] = &ONNXImporter::parseConstant;
    dispatch["LSTM"] = &ONNXImporter::parseLSTM;
    dispatch["GRU"] = &ONNXImporter::parseGRU;
    dispatch["ImageScaler"] = &ONNXImporter::parseImageScaler;
    dispatch["Clip"] = &ONNXImporter::parseClip;
    dispatch["LeakyRelu"] = &ONNXImporter::parseLeakyRelu;
    dispatch["Relu"] = &ONNXImporter::parseRelu;
    dispatch["Elu"] = &ONNXImporter::parseElu;
    dispatch["Tanh"] = &ONNXImporter::parseTanh;
    dispatch["Abs"] = &ONNXImporter::parseAbs;
    dispatch["Equal"] = dispatch["Greater"] = dispatch["Less"] = &ONNXImporter::parseCompare;
    dispatch["PRelu"] = &ONNXImporter::parsePRelu;
    dispatch["LRN"] = &ONNXImporter::parseLRN;
    dispatch["InstanceNormalization"] = &ONNXImporter::parseInstanceNormalization;
    dispatch["BatchNormalization"] = &ONNXImporter::parseBatchNormalization;
    dispatch["Gemm"] = &ONNXImporter::parseGemm;
    dispatch["MatMul"] = &ONNXImporter::parseMatMul;
    dispatch["Mul"] = dispatch["Div"] = &ONNXImporter::parseMul;
    dispatch["Conv"] = &ONNXImporter::parseConv;
    dispatch["ConvTranspose"] = &ONNXImporter::parseConvTranspose;
    dispatch["Transpose"] = &ONNXImporter::parseTranspose;
    dispatch["Squeeze"] = &ONNXImporter::parseSqueeze;
    dispatch["Flatten"] = &ONNXImporter::parseFlatten;
    dispatch["Unsqueeze"] = &ONNXImporter::parseUnsqueeze;
    dispatch["Expand"] = &ONNXImporter::parseExpand;
    dispatch["Reshape"] = &ONNXImporter::parseReshape;
    dispatch["Pad"] = &ONNXImporter::parsePad;
    dispatch["Shape"] = &ONNXImporter::parseShape;
    dispatch["Cast"] = &ONNXImporter::parseCast;
    dispatch["ConstantFill"] = dispatch["ConstantOfShape"] = &ONNXImporter::parseConstantFill;
    dispatch["Gather"] = &ONNXImporter::parseGather;
    dispatch["Concat"] = &ONNXImporter::parseConcat;
    dispatch["Resize"] = &ONNXImporter::parseResize;
    dispatch["Upsample"] = &ONNXImporter::parseUpsample;
    dispatch["SoftMax"] = dispatch["LogSoftmax"] = &ONNXImporter::parseSoftMax;
    dispatch["DetectionOutput"] = &ONNXImporter::parseDetectionOutput;
    dispatch["CumSum"] = &ONNXImporter::parseCumSum;
    dispatch["QuantizeLinear"] = dispatch["DequantizeLinear"] = &ONNXImporter::parseQuantDequant;
    dispatch["QLinearConv"] = &ONNXImporter::parseQConv;
    dispatch["QLinearMatMul"] = &ONNXImporter::parseQMatMul;
    dispatch["QLinearAdd"] = dispatch["QLinearMul"] = &ONNXImporter::parseQEltwise;
    dispatch["QLinearLeakyRelu"] = &ONNXImporter::parseQLeakyRelu;
    dispatch["QLinearSigmoid"] = &ONNXImporter::parseQSigmoid;
    dispatch["QLinearAveragePool"] = dispatch["QLinearGlobalAveragePool"] = &ONNXImporter::parseQAvgPool;
    dispatch["QLinearConcat"] = &ONNXImporter::parseQConcat;

    return dispatch;
}

Net readNetFromONNX(const String& onnxFile)
{
    return detail::readNetDiagnostic<ONNXImporter>(onnxFile.c_str());
}

Net readNetFromONNX(const char* buffer, size_t sizeBuffer)
{
    return detail::readNetDiagnostic<ONNXImporter>(buffer, sizeBuffer);
}

Net readNetFromONNX(const std::vector<uchar>& buffer)
{
    return readNetFromONNX(reinterpret_cast<const char*>(buffer.data()), buffer.size());
}

Mat readTensorFromONNX(const String& path)
{
    std::fstream input(path.c_str(), std::ios::in | std::ios::binary);
    if (!input)
    {
        CV_Error(Error::StsBadArg, cv::format("Can't read ONNX file: %s", path.c_str()));
    }

    opencv_onnx::TensorProto tensor_proto = opencv_onnx::TensorProto();
    if (!tensor_proto.ParseFromIstream(&input))
    {
        CV_Error(Error::StsUnsupportedFormat, cv::format("Failed to parse ONNX data: %s", path.c_str()));
    }
    Mat mat = getMatFromTensor(tensor_proto);
    releaseONNXTensor(tensor_proto);
    return mat;
}

CV__DNN_INLINE_NS_END
}} // namespace

#endif