dnn: expand refactor with cv::broadcast for onnx models (#24295)

* add expand impl with cv::broadcast

* remove expandMid

* deduce shape from -1

* add constant folding

* handle input constant; handle input constant 1d

* add expand conformance tests; add checks to disallow shape of neg values; add early copy for unchanged total elements

* fix ExpandSubgraph

* dummy commit to trigger build

* dummy commit to trigger build 1

* remove conformance from test names

modules/dnn/include/opencv2/dnn/all_layers.hpp

@@ -1144,6 +1144,12 @@ CV__DNN_INLINE_NS_BEGIN
         static Ptr<GemmLayer> create(const LayerParams& params);
     };
 
+    class CV_EXPORTS ExpandLayer : public Layer
+    {
+    public:
+        static Ptr<ExpandLayer> create(const LayerParams &params);
+    };
+
 //! @}
 //! @}
 CV__DNN_INLINE_NS_END

modules/dnn/src/init.cpp

@@ -158,6 +158,7 @@ void initializeLayerFactory()
     CV_DNN_REGISTER_LAYER_CLASS(Reciprocal,     ReciprocalLayer);
     CV_DNN_REGISTER_LAYER_CLASS(Gather,         GatherLayer);
     CV_DNN_REGISTER_LAYER_CLASS(LayerNormalization, LayerNormLayer);
+    CV_DNN_REGISTER_LAYER_CLASS(Expand,         ExpandLayer);
 
     CV_DNN_REGISTER_LAYER_CLASS(Crop,           CropLayer);
     CV_DNN_REGISTER_LAYER_CLASS(Eltwise,        EltwiseLayer);

modules/dnn/src/layers/expand_layer.cpp

@@ -0,0 +1,149 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#include "../precomp.hpp"
+#include <opencv2/dnn/shape_utils.hpp>
+
+namespace cv { namespace dnn {
+
+class ExpandLayerImpl CV_FINAL : public ExpandLayer
+{
+public:
+    ExpandLayerImpl(const LayerParams &params) {
+        setParamsFrom(params);
+
+        // shape as param
+        CV_CheckTrue(params.has("shape"), "DNN/Expand: shape is required in Expand layer initialization");
+        DictValue param_shape = params.get("shape");
+        int ndims_shape = param_shape.size();
+        CV_CheckGT(ndims_shape, 0, "DNN/Expand: ndims of shape must be > 0");
+        target_shape.resize(ndims_shape);
+        for (int i = 0; i < ndims_shape; i++) {
+            target_shape[i] = param_shape.get<int>(i);
+        }
+
+        // FIXME: remove when 0d/1d mat is available
+        const_input_1d = params.get("const_input_1d", false);
+    }
+
+    virtual bool supportBackend(int backendId) CV_OVERRIDE {
+        return backendId == DNN_BACKEND_OPENCV;
+    }
+
+    virtual bool getMemoryShapes(const std::vector<MatShape> &inputs,
+                                 const int requiredOutputs,
+                                 std::vector<MatShape> &outputs,
+                                 std::vector<MatShape> &internals) const CV_OVERRIDE {
+        CV_CheckGE(inputs.size(), static_cast<size_t>(1), "DNN/Expand: one input at least");
+        CV_CheckLE(inputs.size(), static_cast<size_t>(2), "DNN/Expand: two input at most");
+        CV_CheckFalse(target_shape.empty(), "DNN/Expand: shape must known before memory is set");
+
+        MatShape input_shape = inputs[0]; // 1d tensor is represented as 2d mat, e.g. [3] -> [3, 1]
+        if (const_input_1d) {
+            input_shape = {inputs[0][0]};
+        }
+
+        auto& moreDimension = input_shape.size() > target_shape.size() ? input_shape : target_shape;
+        auto& lessDimension = input_shape.size() <= target_shape.size() ? input_shape : target_shape;
+
+        /*  Example:
+                             i = 3
+                               |
+            moreDimension: 1 2 3 4 5, assign non-aligned dimensions to output shape
+            lessDimension:     1 1 5, when dimension is aligned, check valid dimension (either equal or one of them is 1) and assign bigger one
+                               |
+                             j = 0 = i - (moreDimension.size() - lessDimension.size());
+        */
+        MatShape outputShape(moreDimension.size(), 1);
+        for (int i = 0; i < moreDimension.size(); i++) {
+            int d = moreDimension[i];
+            int j = i - (moreDimension.size() - lessDimension.size());
+            if (j >= 0) {
+                if (d == 1 || lessDimension[j] == 1 || // broadcast
+                    d == lessDimension[j]) {           // plain copy
+                    outputShape[i] = std::max(d, lessDimension[j]);
+                } else {
+                    CV_Error(Error::StsBadSize, cv::format("DNN/Expand: invalid dimension, d (%d) != d (%d)", moreDimension[i], lessDimension[j]));
+                }
+            } else {
+                outputShape[i] = d;
+            }
+        }
+        outputs.assign(1, outputShape);
+        return false;
+    }
+
+    virtual void finalize(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr) CV_OVERRIDE {
+        std::vector<Mat> inputs;
+        inputs_arr.getMatVector(inputs);
+
+        const auto &input = inputs[0];
+        auto input_shape = shape(input);
+        if (const_input_1d) {
+            input_shape = {input_shape[0]};
+        }
+
+        auto& moreDimension = input_shape.size() > target_shape.size() ? input_shape : target_shape;
+        auto& lessDimension = input_shape.size() <= target_shape.size() ? input_shape : target_shape;
+
+        MatShape final_target_shape(moreDimension.size(), 1);
+        for (int i = 0; i < moreDimension.size(); i++) {
+            int d = moreDimension[i];
+            int j = i - (moreDimension.size() - lessDimension.size());
+            if (j >= 0) {
+                final_target_shape[i] = std::max(lessDimension[j], d);
+            } else {
+                final_target_shape[i] = d;
+            }
+        }
+        target_shape.clear();
+        target_shape = std::move(final_target_shape);
+    }
+
+    void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE {
+        CV_TRACE_FUNCTION();
+        CV_TRACE_ARG_VALUE(name, "name", name.c_str());
+
+        if (inputs_arr.depth() == CV_16S)
+        {
+            forward_fallback(inputs_arr, outputs_arr, internals_arr);
+            return;
+        }
+
+        std::vector<Mat> inputs, outputs;
+        inputs_arr.getMatVector(inputs);
+        outputs_arr.getMatVector(outputs);
+
+        int target_shape_total = std::accumulate(target_shape.begin(), target_shape.end(), 1, std::multiplies<int>());
+        if (target_shape_total == inputs[0].total()) {
+            const char *data = inputs[0].ptr<const char>();
+            char *output = outputs[0].ptr<char>();
+            int step = target_shape_total * outputs[0].elemSize();
+            std::memcpy(output, data, step);
+            return;
+        }
+
+        if (const_input_1d) {
+            const char *data = inputs[0].ptr<const char>();
+            char *output = outputs[0].ptr<char>();
+            int step = target_shape.back() * outputs[0].elemSize();
+            int total = std::accumulate(target_shape.begin(), target_shape.end() - 1, 1, std::multiplies<int>());
+            for (int i = 0; i < total; i++) {
+                std::memcpy(output + i * step, data, step);
+            }
+        } else {
+            cv::broadcast(inputs[0], target_shape, outputs[0]);
+        }
+    }
+
+private:
+    MatShape target_shape;
+    bool const_input_1d;
+};
+
+Ptr<ExpandLayer> ExpandLayer::create(const LayerParams &params) {
+    return makePtr<ExpandLayerImpl>(params);
+}
+
+}}  // cv::dnn

modules/dnn/src/onnx/onnx_graph_simplifier.cpp

@@ -821,6 +821,16 @@ public:
     }
 };
 
+/*  Constant folding shape for Expand.
+
+    Before fusion:
+             +--------------------------------------------------------------+ (X)
+             |                                                              |
+    ConstantOfShape[input=[4]] -> Mul[B=-1] -> Equal[A=[2, -1, -1, -1]] -> Where[Y=[2, -1, -1, -1]] -> Expand
+             \                                                           \
+             value=[1]                                                   (condition)
+
+*/
 class ExpandSubgraph : public Subgraph
 {
 public:
@@ -837,6 +847,128 @@ public:
         addNodeToMatch("Expand", input, where);
         setFusedNode("Expand", input, shape);
     }
+
+    static int extractValue(const Ptr<ImportGraphWrapper>& net, int node_id, int64_t &val) {
+        Ptr<ImportNodeWrapper> node_wrapper = net->getNode(node_id);
+        opencv_onnx::NodeProto* node = node_wrapper.dynamicCast<ONNXNodeWrapper>()->node;
+
+        if (node->attribute_size() == 0) {
+            val = 0;
+            return 1;
+        } else if (node->attribute_size() == 1) {
+            opencv_onnx::AttributeProto attr = node->attribute(0);
+            if (attr.name() != "value") {
+                return 0;
+            }
+            Mat mat_value = getMatFromTensor(attr.t());
+            switch (mat_value.type()) {
+                case CV_32S: {
+                    val = static_cast<int64_t>(mat_value.at<int>());
+                } break;
+                default: return 0;
+            }
+            return 1;
+        }
+        return 0;
+    }
+
+    static std::vector<int64_t> extractConstant(const Ptr<ImportGraphWrapper>& net, int node_id, int input_id)
+    {
+        auto onnx_net = net.dynamicCast<ONNXGraphWrapper>();
+        int initializer_id = onnx_net->getInputInitializerId(node_id, input_id);
+        Mat mat_constant;
+        if (initializer_id != -1) // initializer
+        {
+            mat_constant = onnx_net->getMatFromInitializer(initializer_id);
+        }
+        else
+        {
+            const Ptr<ImportNodeWrapper> node = net->getNode(node_id);
+            int constant_id = getInputNodeId(net, node, input_id);
+            Ptr<ImportNodeWrapper> constant_ptr = net->getNode(constant_id);
+            opencv_onnx::NodeProto* constant_node = constant_ptr.dynamicCast<ONNXNodeWrapper>()->node;
+            opencv_onnx::TensorProto constant_proto = constant_node->attribute(0).t();
+            mat_constant = getMatFromTensor(constant_proto);
+        }
+
+        std::vector<int64_t> retvals{mat_constant.begin<int>(), mat_constant.end<int>()};
+        return retvals;
+    }
+
+    virtual bool match(const Ptr<ImportGraphWrapper>& net, int nodeId,
+                       std::vector<int>& matchedNodesIds,
+                       std::vector<int>& targetNodesIds) CV_OVERRIDE {
+        if (Subgraph::match(net, nodeId, matchedNodesIds, targetNodesIds)) {
+            int64_t value_ConstantOfShape;
+            if (!extractValue(net, matchedNodesIds[0], value_ConstantOfShape)) {
+                return false;
+            }
+            std::vector<int64_t> input_ConstantOfShape = extractConstant(net, matchedNodesIds[0], 0);
+            if (input_ConstantOfShape.size() != static_cast<size_t>(1)) {
+                return false;
+            }
+
+            auto B_Mul = extractConstant(net, matchedNodesIds[1], 1);
+            if (B_Mul.size() != static_cast<size_t>(1)) {
+                return false;
+            }
+
+            auto A_Equal = extractConstant(net, matchedNodesIds[2], 0);
+            if (A_Equal.size() != static_cast<size_t>(input_ConstantOfShape[0])) {
+                return false;
+            }
+
+            auto Y_Where = extractConstant(net, matchedNodesIds[3], 2);
+            if (Y_Where.size() != A_Equal.size()) {
+                return false;
+            }
+
+            // run ConstantOfShape
+            std::vector<int64_t> output_ConstantOfShape(std::accumulate(input_ConstantOfShape.begin(), input_ConstantOfShape.end(), static_cast<int64_t>(1), std::multiplies<int64_t>()), value_ConstantOfShape);
+            // run Mul
+            std::vector<int64_t> output_Mul = output_ConstantOfShape;
+            for (size_t i = 0; i < output_Mul.size(); i++) {
+                int64_t b = B_Mul[0];
+                output_Mul[i] *= b;
+            }
+            // run Equal
+            std::vector<bool> output_Equal(output_Mul.size());
+            for (int i = 0; i < output_Equal.size(); i++) {
+                if (A_Equal[i] == output_Mul[i]) {
+                    output_Equal[i] = true;
+                } else {
+                    output_Equal[i] = false;
+                }
+            }
+            // run Where
+            std::vector<int64_t> output_Where(output_Equal.size());
+            for (int i = 0; i < output_Where.size(); i++) {
+                if (output_Equal[i]) {
+                    output_Where[i] = output_ConstantOfShape[i];
+                } else {
+                    output_Where[i] = Y_Where[i];
+                }
+            }
+            shape = output_Where;
+
+            return true;
+        }
+        return false;
+    }
+
+    virtual void finalize(const Ptr<ImportGraphWrapper>& graph,
+                          const Ptr<ImportNodeWrapper>& fusedNode,
+                          std::vector<Ptr<ImportNodeWrapper> >& inputs) CV_OVERRIDE {
+        // replace values
+        opencv_onnx::NodeProto* node_shape = inputs[1].dynamicCast<ONNXNodeWrapper>()->node;
+        auto attr = node_shape->mutable_attribute()->Mutable(0);
+        auto tensor = attr->mutable_t();
+        tensor->clear_raw_data();
+        tensor->set_raw_data(std::string((const char*)(shape.data()), shape.size() * sizeof(int64_t)));
+    }
+
+protected:
+    std::vector<int64_t> shape;
 };
 
 class MishSubgraph : public Subgraph

modules/dnn/src/onnx/onnx_importer.cpp

@@ -93,8 +93,6 @@ class ONNXImporter
                   const opencv_onnx::NodeProto& node_proto);
     void setParamsDtype(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 lstm_extractConsts(LayerParams& layerParams, const opencv_onnx::NodeProto& lstm_proto, size_t idx, int* blobShape_, int size);
     void lstm_add_reshape(const std::string& input_name, const std::string& output_name, int* layerShape, size_t n);
     std::string lstm_add_slice(int index, const std::string& input_name, int* begin, int* end, size_t n);
@@ -657,37 +655,6 @@ void ONNXImporter::addLayer(LayerParams& layerParams,
     }
 }
 
-/** @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]);
-    }
-}
-
 void ONNXImporter::addConstant(const std::string& name, const Mat& blob)
 {
     CV_LOG_DEBUG(NULL, "DNN/ONNX: add constant '" << name << "' shape=" << toString(shape(blob)) << ": " << toString(blob));
@@ -2341,137 +2308,38 @@ void ONNXImporter::parseUnsqueeze(LayerParams& layerParams, const opencv_onnx::N
     addLayer(layerParams, node_proto);
 }
 
-void ONNXImporter::parseExpand(LayerParams& layerParams, const opencv_onnx::NodeProto& 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);
-    const std::string output_name = node_proto.output(0);
-    Mat newShapeMat = getBlob(input1);
-    MatShape targetShape(newShapeMat.ptr<int>(), newShapeMat.ptr<int>() + newShapeMat.total());
+    CV_CheckEQ(node_proto.input_size(), 2, "DNN/ONNXImporter-Expand: two inputs are required");
+    // input shape must be constant and it is passed as param to the layer
+    CV_CheckTrue(constBlobs.find(node_proto.input(1)) != constBlobs.end(),
+                 "DNN/ONNXImporter-Expand: input shape must be constant");
 
-    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
-    {
-        Mat blob = getBlob(input0);
-        if (constBlobsExtraInfo.find(node_proto.input(0)) != constBlobsExtraInfo.end() &&
-            getBlobExtraInfo(node_proto, 0).real_ndims == 1) {
-            inpShape = {(int)blob.total()};
-        } else {
-            inpShape = shape(blob);
-        }
+    Mat mat_input_shape = getBlob(node_proto, 1);
+    CV_CheckTypeEQ(mat_input_shape.depth(), CV_32S, "DNN/ONNXImporter-Expand: data type of input shape must be CV_32S");
+    for (int i = 0; i < mat_input_shape.total(); ++i) {
+        CV_Check(i, *(mat_input_shape.ptr<int>() + i) >= 0, "DNN/ONNXImporter-Expand: invalid shape dimension");
     }
+    layerParams.set("shape", DictValue::arrayInt(mat_input_shape.ptr<int>(), mat_input_shape.total()));
 
-    String srcName = input0;
-    // Unsqueeze and repeat along new axis
-    if (targetShape.size() > inpShape.size())
-    {
-        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;
+    if (constBlobs.find(node_proto.input(0)) != constBlobs.end()) {
+        bool const_input_1d = false;
+        if (constBlobsExtraInfo.find(node_proto.input(0)) != constBlobsExtraInfo.end()) {
+            if (getBlobExtraInfo(node_proto, 0).real_ndims == 1) {
+                const_input_1d = true;
             }
         }
-    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.empty())
-        {
-            addConstant(output_name, getBlob(node_proto, 0).reshape(1, targetShape));
-            return;
-        }
+        layerParams.set("const_input_1d", const_input_1d);
 
         Mat input = getBlob(node_proto, 0);
-        MatShape subTargetShape = inpShape;
-        for (auto broadcast_axis : broadcast_axes)
-        {
-            subTargetShape[broadcast_axis] = targetShape[broadcast_axis];
-            input = input.reshape(0, total(inpShape, 0, broadcast_axis));
-            Mat output = cv::repeat(input, 1, subTargetShape[broadcast_axis]);
-            input = output.reshape(0, subTargetShape);
-        }
-        addConstant(output_name, input);
+        std::vector<Mat> inputs, expanded;
+        inputs.push_back(input);
+        runLayer(layerParams, inputs, expanded);
+        CV_CheckEQ(expanded.size(), static_cast<size_t>(1), "DNN/Expand: only one output is expected when folding constant");
+        addConstant(node_proto.output(0), expanded[0]);
         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)
-    {
-        // FIXME: this will end up creating massive amount of Identity nodes for broadcasting,
-        //        for example, broadcast 1 to 256 needs 256 Identity nodes and 1 Concat node.
-        //        Possible improvement is to use "Scale".
-        expandMid(layerParams.name, node_proto, srcName, targetShape[broadcast_axes[0]]);
-
-        layerParams.set("axis", broadcast_axes[0]);
-        layerParams.type = "Concat";
-        node_proto.set_output(0, output_name);
-    }
-    else if (broadcast_axes.empty())
-    {
-        layerParams.type = "Identity";
-    }
-    else
-        CV_Error(Error::StsNotImplemented, "Unsupported Expand op");
     addLayer(layerParams, node_proto);
 }
 

modules/dnn/test/test_onnx_importer.cpp

@@ -987,9 +987,21 @@ TEST_P(Test_ONNX_layers, MatMulAdd)
 TEST_P(Test_ONNX_layers, Expand)
 {
     testONNXModels("expand");
+}
+
+TEST_P(Test_ONNX_layers, ExpandIdentity) {
     testONNXModels("expand_identity");
+}
+
+TEST_P(Test_ONNX_layers, ExpandBatch) {
     testONNXModels("expand_batch");
+}
+
+TEST_P(Test_ONNX_layers, ExpandChannels) {
     testONNXModels("expand_channels");
+}
+
+TEST_P(Test_ONNX_layers, ExpandNegBatch) {
     testONNXModels("expand_neg_batch");
 }
 
@@ -2681,6 +2693,27 @@ TEST_P(Test_ONNX_layers, Conformance_Gemm_transposeB) {
     testONNXModels("test_gemm_transposeB", pb, 0, 0, false, true, 2);
 }
 
+// Note: These tests are converted from onnx/onnx so that they have constant shape as input.
+// TODO: They can be moved into conformance tests once dynamic input is properly supported.
+TEST_P(Test_ONNX_layers, Expand_dim_changed) {
+    testONNXModels("test_expand_dim_changed", pb, 0, 0, false, true, 1);
+}
+TEST_P(Test_ONNX_layers, Expand_dim_unchanged) {
+    testONNXModels("test_expand_dim_unchanged", pb, 0, 0, false, true, 1);
+}
+TEST_P(Test_ONNX_layers, Expand_shape_model1) {
+    testONNXModels("test_expand_shape_model1", pb, 0, 0, false, true, 1);
+}
+TEST_P(Test_ONNX_layers, Expand_shape_model2) {
+    testONNXModels("test_expand_shape_model2", pb, 0, 0, false, true, 1);
+}
+TEST_P(Test_ONNX_layers, Expand_shape_model3) {
+    testONNXModels("test_expand_shape_model3", pb, 0, 0, false, true, 1);
+}
+TEST_P(Test_ONNX_layers, Expand_shape_model4) {
+    testONNXModels("test_expand_shape_model4", pb, 0, 0, false, true, 1);
+}
+
 INSTANTIATE_TEST_CASE_P(/**/, Test_ONNX_nets, dnnBackendsAndTargets());
 
 }} // namespace