Merge pull request #18902 from mpashchenkov:mp/onnx-const-input

G-API: ONNX. Const input

* Added const input for ONNX backend

* Returned initMatrixRandu, added some comments, rebase

modules/gapi/src/backends/ie/giebackend.cpp

@@ -263,14 +263,6 @@ struct IEUnit {
             // Still, constant data is to set only once.
             this_request.SetBlob(p.first, wrapIE(p.second.first, p.second.second));
         }
-        // Bind const data to infer request
-        for (auto &&p : params.const_inputs) {
-            // FIXME: SetBlob is known to be inefficient,
-            // it is worth to make a customizable "initializer" and pass the
-            // cv::Mat-wrapped blob there to support IE's optimal "GetBlob idiom"
-            // Still, constant data is to set only once.
-            this_request.SetBlob(p.first, wrapIE(p.second.first, p.second.second));
-        }
 
         return {this_plugin, this_network, this_request};
     }

modules/gapi/src/backends/onnx/gonnxbackend.cpp

@@ -16,6 +16,7 @@
 #include <opencv2/gapi/gframe.hpp>
 
 #include "api/gbackend_priv.hpp" // FIXME: Make it part of Backend SDK!
+#include "logger.hpp"
 
 namespace {
 struct ONNXCallContext;
@@ -30,12 +31,35 @@ enum TensorPosition : int {
     OUTPUT
 };
 
+static std::string pdims(const std::vector<int64_t> &dims) {
+    std::stringstream ss;
+    auto it = dims.begin();
+    ss << *it++;
+    for (; it != dims.end(); ++it) {
+        ss << '/' << *it;
+    }
+    return ss.str();
+}
+
 struct TensorInfo {
     TensorInfo() = default;
     explicit TensorInfo(const Ort::TensorTypeAndShapeInfo& info)
         : dims(info.GetShape())
         , type(info.GetElementType())
         , is_dynamic(std::find(dims.begin(), dims.end(), -1) != dims.end()) {
+
+        // Double-check if the tensor is really dynamic
+        // Allow N to be -1
+        if (is_dynamic
+            && dims[0] == -1
+            && dims.size() > 1
+            && std::find(dims.begin() + 1, dims.end(), -1) == dims.end()) {
+
+            GAPI_LOG_WARNING(NULL, "Promoting N=-1 to N=1 for tensor " << pdims(dims));
+            dims[0] = 1;
+            is_dynamic = false;
+        }
+
         if (!is_dynamic) {
             size = std::accumulate(dims.begin(),
                                    dims.end(),
@@ -81,6 +105,7 @@ class ONNXCompiled {
     std::vector<TensorInfo> in_tensor_info;
     std::vector<TensorInfo> out_tensor_info;
     bool is_dynamic = false;
+    bool is_postproc = false;
 
     // G-API <Net> description
     gapi::onnx::detail::ParamDesc params;
@@ -95,6 +120,7 @@ class ONNXCompiled {
     void Run(const std::vector<cv::Mat>& ins,
              const std::vector<cv::Mat>& outs);
 
+    std::vector<std::string> in_names_without_const;
 public:
     explicit ONNXCompiled(const gapi::onnx::detail::ParamDesc &pp);
 
@@ -142,6 +168,7 @@ inline int toCV(ONNXTensorElementDataType prec) {
     switch (prec) {
     case ONNX_TENSOR_ELEMENT_DATA_TYPE_UINT8: return CV_8U;
     case ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT: return CV_32F;
+    case ONNX_TENSOR_ELEMENT_DATA_TYPE_INT32: return CV_32S;
     default: GAPI_Assert(false && "Unsupported data type");
     }
     return -1;
@@ -308,6 +335,8 @@ inline Ort::Value createTensor(const Ort::MemoryInfo& memory_info,
         return createTensor<uint8_t>(memory_info, tensor_params, data);
     case ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT:
         return createTensor<float>(memory_info, tensor_params, data);
+    case ONNX_TENSOR_ELEMENT_DATA_TYPE_INT32:
+        return createTensor<int32_t>(memory_info, tensor_params, data);
     default:
         GAPI_Assert(false && "Unsupported data type");
     }
@@ -523,7 +552,6 @@ namespace onnx {
 
 ONNXCompiled::ONNXCompiled(const gapi::onnx::detail::ParamDesc &pp)
     : params(pp) {
-
     // Validate input parameters before allocating any resources
     if (params.num_in > 1u && params.num_in != params.input_names.size()) {
         cv::util::throw_error(std::logic_error("Please specify input layer names for "
@@ -553,6 +581,7 @@ ONNXCompiled::ONNXCompiled(const gapi::onnx::detail::ParamDesc &pp)
                                            "Please provide a custom post-processing function "
                                            "(.cfgPostProc) in network parameters"));
     }
+    is_postproc = (params.custom_post_proc != nullptr);
 
     // Update parameters based on session information
     if (params.num_in == 1u && params.input_names.empty()) {
@@ -563,8 +592,6 @@ ONNXCompiled::ONNXCompiled(const gapi::onnx::detail::ParamDesc &pp)
     }
 
     // Validate what is supported currently
-    GAPI_Assert(params.const_inputs.empty()
-                && "Const inputs are not currently supported");
     GAPI_Assert(std::all_of(in_tensor_info.begin(),
                             in_tensor_info.end(),
                             [](const cv::gimpl::onnx::TensorInfo &p) {
@@ -593,6 +620,17 @@ ONNXCompiled::ONNXCompiled(const gapi::onnx::detail::ParamDesc &pp)
         }
     }
 
+    if (!params.const_inputs.empty()) {
+        // Form input names order without const input names
+        in_names_without_const.clear();
+        std::copy_if(params.input_names.begin(), params.input_names.end(),
+                     std::back_inserter(in_names_without_const),
+                     [&](const std::string& name) {
+                        const auto it = params.const_inputs.find(name);
+                        return it == params.const_inputs.end();
+                     });
+    }
+
     // Pre-allocate vectors (not buffers) for runtime info
     in_data.resize(params.num_in);
     out_data.resize(params.num_out);
@@ -626,9 +664,9 @@ std::vector<TensorInfo> ONNXCompiled::getTensorInfo(TensorPosition pos) {
 }
 
 cv::GMatDesc ONNXCompiled::outMeta(int idx) const {
-    if (is_dynamic) {
+    if (is_dynamic || is_postproc) {
         GAPI_Assert(!params.out_metas.empty()
-                    && "Metadata must be specified if NN has dynamic inputs!");
+                    && "Metadata must be specified if NN has dynamic inputs or post-processing function is used!");
         return params.out_metas.at(idx);
     }
     const auto ort_idx = getIdxByName(out_tensor_info, params.output_names[idx]);
@@ -678,9 +716,12 @@ void ONNXCompiled::Run(const std::vector<cv::Mat>& ins,
                        const std::vector<cv::Mat>& outs) {
     std::vector<Ort::Value> in_tensors, out_tensors;
 
-    auto in_run_names  = getCharNames(params.input_names);
+    // Layer names order for run
-
+    auto input_names = (in_names_without_const.empty() && params.const_inputs.empty())
-    for (const auto it : ade::util::indexed(params.input_names)) {
+                       ? params.input_names
+                       : in_names_without_const;
+    // Creates tensors for unique names that don't contain constant input
+    for (const auto it : ade::util::indexed(input_names)) {
         auto i         = ade::util::index(it);
         auto in_name   = ade::util::value(it);
         const auto idx = getIdxByName(in_tensor_info, in_name);
@@ -689,7 +730,19 @@ void ONNXCompiled::Run(const std::vector<cv::Mat>& ins,
                                              ins[i]));
     }
 
-    if (!is_dynamic) {
+    for (auto &&c_in_pair : params.const_inputs) {
+        const auto idx = getIdxByName(in_tensor_info, c_in_pair.first);
+        in_tensors.emplace_back(createTensor(this_memory_info,
+                                             in_tensor_info[idx],
+                                             c_in_pair.second.first));
+        // Puts const input names in sequence for Run
+        // ONNXRuntime can match input tensors to CNN inputs by names
+        input_names.emplace_back(c_in_pair.first);
+    }
+    GAPI_Assert(input_names.size() == this_session.GetInputCount());
+
+    auto in_run_names  = getCharNames(input_names);
+    if (!is_dynamic && !is_postproc) {
         // Easy path - just run the session which is bound to G-API's
         // internal data
         for (auto i : ade::util::iota(params.output_names.size())) {
@@ -701,7 +754,7 @@ void ONNXCompiled::Run(const std::vector<cv::Mat>& ins,
         this_session.Run(Ort::RunOptions{nullptr},
                          in_run_names.data(),
                          &in_tensors.front(),
-                         params.input_names.size(),
+                         input_names.size(),
                          out_run_names.data(),
                          &out_tensors.front(),
                          params.output_names.size());
@@ -716,7 +769,7 @@ void ONNXCompiled::Run(const std::vector<cv::Mat>& ins,
         auto outputs = this_session.Run(Ort::RunOptions{nullptr},
                                         in_run_names.data(),
                                         &in_tensors.front(),
-                                        params.input_names.size(),
+                                        input_names.size(),
                                         out_names.data(),
                                         out_names.size());
         std::unordered_map<std::string, cv::Mat> onnx_outputs;

modules/gapi/test/infer/gapi_infer_onnx_test.cpp

@@ -81,8 +81,24 @@ cv::Mat initMatrixRandU(const int type, const cv::Size& sz_in) {
 namespace opencv_test
 {
 namespace {
+void initTestDataPath()
+{
+#ifndef WINRT
+    static bool initialized = false;
+    if (!initialized)
+    {
+        // Since G-API has no own test data (yet), it is taken from the common space
+        const char* testDataPath = getenv("OPENCV_TEST_DATA_PATH");
+        if (testDataPath) {
+            cvtest::addDataSearchPath(testDataPath);
+        }
+        initialized = true;
+    }
+#endif // WINRT
+}
+
 // FIXME: taken from the DNN module
-void normAssert(const cv::InputArray& ref, const cv::InputArray& test,
+void normAssert(cv::InputArray& ref, cv::InputArray& test,
                 const char *comment /*= ""*/,
                 const double l1 = 0.00001, const double lInf = 0.0001) {
     const double normL1 = cvtest::norm(ref, test, cv::NORM_L1) / ref.getMat().total();
@@ -109,6 +125,7 @@ inline int toCV(const ONNXTensorElementDataType prec) {
     switch (prec) {
     case ONNX_TENSOR_ELEMENT_DATA_TYPE_UINT8: return CV_8U;
     case ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT: return CV_32F;
+    case ONNX_TENSOR_ELEMENT_DATA_TYPE_INT32: return CV_32S;
     default: GAPI_Assert(false && "Unsupported data type");
     }
     return -1;
@@ -126,46 +143,97 @@ inline std::vector<const char*> getCharNames(const std::vector<std::string>& nam
     return out_vec;
 }
 
-inline void copyToOut(const cv::Mat& in, cv::Mat& out) {
+template<typename T>
-    GAPI_Assert(in.depth() == CV_32F);
+void copyToOut(const cv::Mat& in, cv::Mat& out) {
-    GAPI_Assert(in.size == out.size);
+    const size_t size = std::min(out.total(), in.total());
-    const float* const inptr = in.ptr<float>();
+    std::copy(in.begin<T>(), in.begin<T>() + size, out.begin<T>());
-    float* const optr = out.ptr<float>();
+    if (size < out.total()) {
-    const int size = in.total();
+        T* const optr = out.ptr<T>();
-    for (int i = 0; i < size; ++i) {
+        optr[size] = static_cast<T>(-1); // end data mark
-        optr[i] = inptr[i];
     }
 }
 
 void remapYolo(const std::unordered_map<std::string, cv::Mat> &onnx,
-                      std::unordered_map<std::string, cv::Mat> &gapi) {
+                     std::unordered_map<std::string, cv::Mat> &gapi) {
     GAPI_Assert(onnx.size() == 1u);
     GAPI_Assert(gapi.size() == 1u);
     // Result from Run method
     const cv::Mat& in = onnx.begin()->second;
+    GAPI_Assert(in.depth() == CV_32F);
     // Configured output
     cv::Mat& out = gapi.begin()->second;
     // Simple copy
-    copyToOut(in, out);
+    copyToOut<float>(in, out);
 }
 
-void remapSsdPorts(const std::unordered_map<std::string, cv::Mat> &onnx,
+void remapYoloV3(const std::unordered_map<std::string, cv::Mat> &onnx,
-                           std::unordered_map<std::string, cv::Mat> &gapi) {
+                       std::unordered_map<std::string, cv::Mat> &gapi) {
-    // Result from Run method
-    const cv::Mat& in_num     = onnx.at("num_detections:0");
-    const cv::Mat& in_boxes   = onnx.at("detection_boxes:0");
-    const cv::Mat& in_scores  = onnx.at("detection_scores:0");
-    const cv::Mat& in_classes = onnx.at("detection_classes:0");
-    // Configured outputs
-    cv::Mat& out_boxes   = gapi.at("out1");
-    cv::Mat& out_classes = gapi.at("out2");
-    cv::Mat& out_scores  = gapi.at("out3");
-    cv::Mat& out_num     = gapi.at("out4");
     // Simple copy for outputs
-    copyToOut(in_num, out_num);
+    const cv::Mat& in_boxes = onnx.at("yolonms_layer_1/ExpandDims_1:0");
-    copyToOut(in_boxes, out_boxes);
+    const cv::Mat& in_scores = onnx.at("yolonms_layer_1/ExpandDims_3:0");
-    copyToOut(in_scores, out_scores);
+    const cv::Mat& in_indices = onnx.at("yolonms_layer_1/concat_2:0");
-    copyToOut(in_classes, out_classes);
+    GAPI_Assert(in_boxes.depth() == CV_32F);
+    GAPI_Assert(in_scores.depth() == CV_32F);
+    GAPI_Assert(in_indices.depth() == CV_32S);
+
+    cv::Mat& out_boxes = gapi.at("out1");
+    cv::Mat& out_scores = gapi.at("out2");
+    cv::Mat& out_indices = gapi.at("out3");
+
+    copyToOut<float>(in_boxes, out_boxes);
+    copyToOut<float>(in_scores, out_scores);
+    copyToOut<int32_t>(in_indices, out_indices);
+}
+
+void remapToIESSDOut(const std::vector<cv::Mat> &detections,
+                           cv::Mat &ssd_output) {
+    for (const auto &det_el : detections) {
+        GAPI_Assert(det_el.depth() == CV_32F);
+        GAPI_Assert(!det_el.empty());
+    }
+
+    // SSD-MobilenetV1 structure check
+    ASSERT_EQ(detections[0].total(), 1u);
+    ASSERT_EQ(detections[2].total(), detections[0].total() * 100);
+    ASSERT_EQ(detections[2].total(), detections[3].total());
+    ASSERT_EQ((detections[2].total() * 4), detections[1].total());
+
+    const int num_objects = static_cast<int>(detections[0].ptr<float>()[0]);
+    GAPI_Assert(num_objects <= (ssd_output.size[2] - 1));
+    const float *in_boxes = detections[1].ptr<float>();
+    const float *in_scores = detections[2].ptr<float>();
+    const float *in_classes = detections[3].ptr<float>();
+    float *ptr = ssd_output.ptr<float>();
+
+    for (int i = 0; i < num_objects; i++) {
+        ptr[0] = 0.f;                 // "image_id"
+        ptr[1] = in_classes[i];       // "label"
+        ptr[2] = in_scores[i];        // "confidence"
+        ptr[3] = in_boxes[4 * i + 1]; // left
+        ptr[4] = in_boxes[4 * i + 0]; // top
+        ptr[5] = in_boxes[4 * i + 3]; // right
+        ptr[6] = in_boxes[4 * i + 2]; // bottom
+
+        ptr      += 7;
+        in_boxes += 4;
+    }
+
+    if (num_objects < ssd_output.size[2] - 1) {
+        // put a -1 mark at the end of output blob if there is space left
+        ptr[0] = -1.f;
+    }
+}
+
+void remapSSDPorts(const std::unordered_map<std::string, cv::Mat> &onnx,
+                         std::unordered_map<std::string, cv::Mat> &gapi) {
+    // Assemble ONNX-processed outputs back to a single 1x1x200x7 blob
+    // to preserve compatibility with OpenVINO-based SSD pipeline
+    const cv::Mat &num_detections = onnx.at("num_detections:0");
+    const cv::Mat &detection_boxes = onnx.at("detection_boxes:0");
+    const cv::Mat &detection_scores = onnx.at("detection_scores:0");
+    const cv::Mat &detection_classes = onnx.at("detection_classes:0");
+    cv::Mat &ssd_output = gapi.at("detection_output");
+    remapToIESSDOut({num_detections, detection_boxes, detection_scores, detection_classes}, ssd_output);
 }
 
 class ONNXtest : public ::testing::Test {
@@ -177,18 +245,17 @@ public:
     cv::Mat in_mat1;
 
     ONNXtest() {
+        initTestDataPath();
         env = Ort::Env(ORT_LOGGING_LEVEL_WARNING, "test");
         memory_info = Ort::MemoryInfo::CreateCpu(OrtArenaAllocator, OrtMemTypeDefault);
         out_gapi.resize(1);
         out_onnx.resize(1);
-        // FIXME: All tests chek "random" image
+        // FIXME: It should be an image from own (gapi) directory in opencv extra
-        // Ideally it should be a real image
+        in_mat1 = cv::imread(findDataFile("cv/dpm/cat.png"));
-        in_mat1 = initMatrixRandU(CV_8UC3, cv::Size{640, 480});
     }
 
     template<typename T>
-    void infer(const std::vector<cv::Mat>& ins,
+    void infer(const std::vector<cv::Mat>& ins, std::vector<cv::Mat>& outs) {
-                     std::vector<cv::Mat>& outs) {
         // Prepare session
         session = Ort::Session(env, model_path.data(), session_options);
         num_in = session.GetInputCount();
@@ -241,10 +308,15 @@ public:
     template<typename T>
     void infer(const cv::Mat& in, cv::Mat& out) {
         std::vector<cv::Mat> result;
-        infer<T>({in}, result);
+        infer<T>(std::vector<cv::Mat>{in}, result);
         GAPI_Assert(result.size() == 1u);
         out = result.front();
     }
+    // One input overload
+    template<typename T>
+    void infer(const cv::Mat& in, std::vector<cv::Mat>& outs) {
+        infer<T>(std::vector<cv::Mat>{in}, outs);
+    }
 
     void validate() {
         GAPI_Assert(!out_gapi.empty() && !out_onnx.empty());
@@ -275,6 +347,12 @@ public:
     const cv::Scalar mean = { 0.485, 0.456, 0.406 };
     const cv::Scalar std  = { 0.229, 0.224, 0.225 };
 
+    // Rois for InferList, InferList2
+    const std::vector<cv::Rect> rois = {
+        cv::Rect(cv::Point{ 0,   0}, cv::Size{80, 120}),
+        cv::Rect(cv::Point{50, 100}, cv::Size{250, 360}),
+    };
+
     void preprocess(const cv::Mat& src, cv::Mat& dst) {
         const int new_h = 224;
         const int new_w = 224;
@@ -317,6 +395,55 @@ public:
         dst = dst.reshape(1, {1, 1, new_h, new_w});
     }
 };
+
+class ONNXWithRemap : public ONNXtest {
+public:
+    // You can specify any size of the outputs, since we don't know infer result
+    // Tests validate a range with results and don't compare empty space
+    void validate() {
+        GAPI_Assert(!out_gapi.empty() && !out_onnx.empty());
+        ASSERT_EQ(out_gapi.size(), out_onnx.size());
+        const auto size = out_onnx.size();
+        for (size_t i = 0; i < size; ++i) {
+            float* op = out_onnx.at(i).ptr<float>();
+            float* gp = out_gapi.at(i).ptr<float>();
+            const auto out_size = std::min(out_onnx.at(i).total(), out_gapi.at(i).total());
+            GAPI_Assert(out_size != 0u);
+            for (size_t d_idx = 0; d_idx < out_size; ++d_idx) {
+                if (gp[d_idx] == -1) {
+                    break; // end of detections
+                }
+                ASSERT_EQ(op[d_idx], gp[d_idx]);
+            }
+        }
+    }
+};
+
+class ONNXYoloV3MultiInput : public ONNXWithRemap {
+public:
+    std::vector<cv::Mat> ins;
+
+private:
+    virtual void SetUp() {
+        const int yolo_in_h = 416;
+        const int yolo_in_w = 416;
+        cv::Mat yolov3_input, shape, prep_mat;
+        cv::resize(in_mat1, yolov3_input, cv::Size(yolo_in_w, yolo_in_h));
+        shape.create(cv::Size(2, 1), CV_32F);
+        float* ptr = shape.ptr<float>();
+        ptr[0] = in_mat1.cols;
+        ptr[1] = in_mat1.rows;
+        preprocess(yolov3_input, prep_mat);
+        ins = {prep_mat, shape};
+    }
+
+    void preprocess(const cv::Mat& src, cv::Mat& dst) {
+        cv::Mat cvt;
+        src.convertTo(cvt, CV_32F, 1.f / 255.f);
+        toCHW(cvt, dst);
+        dst = dst.reshape(1, {1, 3, 416, 416});
+    }
+};
 } // anonymous namespace
 
 TEST_F(ONNXClassificationTest, Infer)
@@ -341,15 +468,12 @@ TEST_F(ONNXClassificationTest, Infer)
     validate();
 }
 
-TEST_F(ONNXtest, InferTensor)
+TEST_F(ONNXClassificationTest, InferTensor)
 {
     useModel("classification/squeezenet/model/squeezenet1.0-9");
     // Create tensor
-    // FIXME: Test cheks "random" image
+    cv::Mat tensor;
-    // Ideally it should be a real image
+    preprocess(in_mat1, tensor);
-    const cv::Mat rand_mat = initMatrixRandU(CV_32FC3, cv::Size{224, 224});
-    const std::vector<int> dims = {1, rand_mat.channels(), rand_mat.rows, rand_mat.cols};
-    const cv::Mat tensor(dims, CV_32F, rand_mat.data);
     // ONNX_API code
     infer<float>(tensor, out_onnx.front());
     // G_API code
@@ -368,7 +492,7 @@ TEST_F(ONNXtest, InferTensor)
 TEST_F(ONNXClassificationTest, InferROI)
 {
     useModel("classification/squeezenet/model/squeezenet1.0-9");
-    const cv::Rect ROI(cv::Point{0, 0}, cv::Size{250, 250});
+    const auto ROI = rois.at(1);
     // ONNX_API code
     cv::Mat roi_mat;
     preprocess(in_mat1(ROI), roi_mat);
@@ -392,10 +516,6 @@ TEST_F(ONNXClassificationTest, InferROI)
 TEST_F(ONNXClassificationTest, InferROIList)
 {
     useModel("classification/squeezenet/model/squeezenet1.0-9");
-    const std::vector<cv::Rect> rois = {
-        cv::Rect(cv::Point{ 0,   0}, cv::Size{80, 120}),
-        cv::Rect(cv::Point{50, 100}, cv::Size{250, 360}),
-    };
     // ONNX_API code
     out_onnx.resize(rois.size());
     for (size_t i = 0; i < rois.size(); ++i) {
@@ -422,10 +542,6 @@ TEST_F(ONNXClassificationTest, InferROIList)
 TEST_F(ONNXClassificationTest, Infer2ROIList)
 {
     useModel("classification/squeezenet/model/squeezenet1.0-9");
-    const std::vector<cv::Rect> rois = {
-        cv::Rect(cv::Point{ 0,   0}, cv::Size{80, 120}),
-        cv::Rect(cv::Point{50, 100}, cv::Size{250, 360}),
-    };
     // ONNX_API code
     out_onnx.resize(rois.size());
     for (size_t i = 0; i < rois.size(); ++i) {
@@ -449,27 +565,26 @@ TEST_F(ONNXClassificationTest, Infer2ROIList)
     validate();
 }
 
-TEST_F(ONNXtest, InferDynamicInputTensor)
+TEST_F(ONNXWithRemap, InferDynamicInputTensor)
 {
     useModel("object_detection_segmentation/tiny-yolov2/model/tinyyolov2-8");
     // Create tensor
-    // FIXME: Test cheks "random" image
+    cv::Mat cvt, rsz, tensor;
-    // Ideally it should be a real image
+    cv::resize(in_mat1, rsz, cv::Size{416, 416});
-    const cv::Mat rand_mat = initMatrixRandU(CV_32FC3, cv::Size{416, 416});
+    rsz.convertTo(cvt, CV_32F, 1.f / 255.f);
-    const std::vector<int> dims = {1, rand_mat.channels(), rand_mat.rows, rand_mat.cols};
+    toCHW(cvt, tensor);
-    cv::Mat tensor(dims, CV_32F, rand_mat.data);
+    tensor = tensor.reshape(1, {1, 3, 416, 416});
-    const cv::Mat in_tensor = tensor / 255.f;
     // ONNX_API code
-    infer<float>(in_tensor, out_onnx.front());
+    infer<float>(tensor, out_onnx.front());
     // G_API code
     G_API_NET(YoloNet, <cv::GMat(cv::GMat)>, "YoloNet");
     cv::GMat in;
     cv::GMat out = cv::gapi::infer<YoloNet>(in);
     cv::GComputation comp(cv::GIn(in), cv::GOut(out));
-    auto net = cv::gapi::onnx::Params<YoloNet>{model_path}
+    auto net = cv::gapi::onnx::Params<YoloNet>{ model_path }
         .cfgPostProc({cv::GMatDesc{CV_32F, {1, 125, 13, 13}}}, remapYolo)
         .cfgOutputLayers({"out"});
-    comp.apply(cv::gin(in_tensor),
+    comp.apply(cv::gin(tensor),
                cv::gout(out_gapi.front()),
                cv::compile_args(cv::gapi::networks(net)));
     // Validate
@@ -497,28 +612,26 @@ TEST_F(ONNXGRayScaleTest, InferImage)
     validate();
 }
 
-TEST_F(ONNXtest, InferMultOutput)
+TEST_F(ONNXWithRemap, InferMultiOutput)
 {
     useModel("object_detection_segmentation/ssd-mobilenetv1/model/ssd_mobilenet_v1_10");
     // ONNX_API code
     const auto prep_mat = in_mat1.reshape(1, {1, in_mat1.rows, in_mat1.cols, in_mat1.channels()});
-    infer<uint8_t>({prep_mat}, out_onnx);
+    infer<uint8_t>(prep_mat, out_onnx);
+    cv::Mat onnx_conv_out({1, 1, 200, 7}, CV_32F);
+    remapToIESSDOut({out_onnx[3], out_onnx[0], out_onnx[2], out_onnx[1]}, onnx_conv_out);
+    out_onnx.clear();
+    out_onnx.push_back(onnx_conv_out);
     // G_API code
-    using SSDOut = std::tuple<cv::GMat, cv::GMat, cv::GMat, cv::GMat>;
+    G_API_NET(MobileNet, <cv::GMat(cv::GMat)>, "ssd_mobilenet");
-    G_API_NET(MobileNet, <SSDOut(cv::GMat)>, "ssd_mobilenet");
     cv::GMat in;
-    cv::GMat out1, out2, out3, out4;
+    cv::GMat out = cv::gapi::infer<MobileNet>(in);
-    std::tie(out1, out2, out3, out4) = cv::gapi::infer<MobileNet>(in);
+    cv::GComputation comp(cv::GIn(in), cv::GOut(out));
-    cv::GComputation comp(cv::GIn(in), cv::GOut(out1, out2, out3, out4));
+    auto net = cv::gapi::onnx::Params<MobileNet>{ model_path }
-    auto net = cv::gapi::onnx::Params<MobileNet>{model_path}
+        .cfgOutputLayers({"detection_output"})
-        .cfgOutputLayers({"out1", "out2", "out3", "out4"})
+        .cfgPostProc({cv::GMatDesc{CV_32F, {1, 1, 200, 7}}}, remapSSDPorts);
-        .cfgPostProc({cv::GMatDesc{CV_32F, {1, 100, 4}},
-                      cv::GMatDesc{CV_32F, {1, 100}},
-                      cv::GMatDesc{CV_32F, {1, 100}},
-                      cv::GMatDesc{CV_32F, {1, 1}}}, remapSsdPorts);
-    out_gapi.resize(num_out);
     comp.apply(cv::gin(in_mat1),
-               cv::gout(out_gapi[0], out_gapi[1], out_gapi[2], out_gapi[3]),
+               cv::gout(out_gapi.front()),
                cv::compile_args(cv::gapi::networks(net)));
     // Validate
     validate();
@@ -733,6 +846,71 @@ TEST_F(ONNXMediaFrameTest, InferList2YUV)
     // Validate
     validate();
 }
+
+TEST_F(ONNXYoloV3MultiInput, InferConstInput)
+{
+    useModel("object_detection_segmentation/yolov3/model/yolov3-10");
+    // ONNX_API code
+    infer<float>(ins, out_onnx);
+    // G_API code
+    using OUT = std::tuple<cv::GMat, cv::GMat, cv::GMat>;
+    G_API_NET(YoloNet, <OUT(cv::GMat)>, "yolov3");
+    auto net = cv::gapi::onnx::Params<YoloNet>{model_path}
+        .constInput("image_shape", ins[1])
+        .cfgInputLayers({"input_1"})
+        .cfgOutputLayers({"out1", "out2", "out3"})
+        .cfgPostProc({cv::GMatDesc{CV_32F, {1, 10000, 4}},
+                      cv::GMatDesc{CV_32F, {1, 80, 10000}},
+                      cv::GMatDesc{CV_32S, {5, 3}}}, remapYoloV3);
+    cv::GMat in, out1, out2, out3;
+    std::tie(out1, out2, out3) = cv::gapi::infer<YoloNet>(in);
+    cv::GComputation comp(cv::GIn(in), cv::GOut(out1, out2, out3));
+    out_gapi.resize(num_out);
+    comp.apply(cv::gin(ins[0]),
+               cv::gout(out_gapi[0], out_gapi[1], out_gapi[2]),
+               cv::compile_args(cv::gapi::networks(net)));
+    // Validate
+    validate();
+}
+
+TEST_F(ONNXYoloV3MultiInput, InferBSConstInput)
+{
+    // This test checks the case when a const input is used
+    // and all input layer names are specified.
+    // Const input has the advantage. It is expected behavior.
+    useModel("object_detection_segmentation/yolov3/model/yolov3-10");
+    // Tensor with incorrect image size
+    // is used for check case when InputLayers and constInput have same names
+    cv::Mat bad_shape;
+    bad_shape.create(cv::Size(2, 1), CV_32F);
+    float* ptr = bad_shape.ptr<float>();
+    ptr[0] = 590;
+    ptr[1] = 12;
+    // ONNX_API code
+    infer<float>(ins, out_onnx);
+    // G_API code
+    using OUT = std::tuple<cv::GMat, cv::GMat, cv::GMat>;
+    G_API_NET(YoloNet, <OUT(cv::GMat, cv::GMat)>, "yolov3");
+    auto net = cv::gapi::onnx::Params<YoloNet>{model_path}
+    // Data from const input will be used to infer
+        .constInput("image_shape", ins[1])
+    // image_shape - const_input has same name
+        .cfgInputLayers({"input_1", "image_shape"})
+        .cfgOutputLayers({"out1", "out2", "out3"})
+        .cfgPostProc({cv::GMatDesc{CV_32F, {1, 10000, 4}},
+                      cv::GMatDesc{CV_32F, {1, 80, 10000}},
+                      cv::GMatDesc{CV_32S, {5, 3}}}, remapYoloV3);
+    cv::GMat in1, in2, out1, out2, out3;
+    std::tie(out1, out2, out3) = cv::gapi::infer<YoloNet>(in1, in2);
+    cv::GComputation comp(cv::GIn(in1, in2), cv::GOut(out1, out2, out3));
+    out_gapi.resize(num_out);
+    comp.apply(cv::gin(ins[0], bad_shape),
+               cv::gout(out_gapi[0], out_gapi[1], out_gapi[2]),
+               cv::compile_args(cv::gapi::networks(net)));
+    // Validate
+    validate();
+}
+
 } // namespace opencv_test
 
 #endif //  HAVE_ONNX