Ptr<Layer> createLayerFromCaffe<EltwiseLayer>(LayerParams& params)
{
    EltwiseLayer::EltwiseOp op = EltwiseLayer::SUM;
    if (params.has("operation"))
    {
        String operation = params.get<String>("operation").toLowerCase();
        if (operation == "prod")
            op = EltwiseLayer::PROD;
        else if (operation == "sum")
            op = EltwiseLayer::SUM;
        else if (operation == "max")
            op = EltwiseLayer::MAX;
        else
            CV_Error(cv::Error::StsBadArg, "Unknown operaticon type \"" + operation + "\"");
    }

    std::vector<int> coeffs;
    if (params.has("coeff"))
    {
        DictValue paramCoeff = params.get("coeff");
        coeffs.resize(paramCoeff.size(), 1);
        for (int i = 0; i < paramCoeff.size(); i++)
        {
            coeffs[i] = paramCoeff.get<int>(i);
        }
    }
    return Ptr<Layer>(EltwiseLayer::create(op, coeffs));
}
    bool getParameterDict(const LayerParams &params,
                          const std::string &parameterName,
                          DictValue& result)
    {
        if (!params.has(parameterName))
        {
            return false;
        }

        result = params.get(parameterName);
        return true;
    }
Exemple #3
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    ReshapeLayerImpl(const LayerParams& params)
    {
        setParamsFrom(params);
        int axis = params.get<int>("axis", 0);
        int numAxes = params.get<int>("num_axes", -1);
        CV_Assert(numAxes >= -1);
        newShapeRange = (numAxes == -1) ? Range(axis, INT_MAX) : Range(axis, axis + numAxes);

        newShapeDesc.clear();
        if (params.has("dim"))
        {
            const DictValue &paramShape = params.get("dim");
            int i, dims = paramShape.size();
            newShapeDesc.resize(dims);
            for (i = 0; i < dims; i++)
                newShapeDesc[i] = paramShape.get<int>(i);
        }
    }
Ptr<Layer> createLayerFromCaffe<SliceLayer>(LayerParams& params)
{
    int axis = params.get<int>("axis", 1);

    if (!params.has("slice_point"))
    {
        return Ptr<Layer>(SliceLayer::create(axis));
    }
    else
    {
        const DictValue &indicesValue = params.get("slice_point");
        std::vector<int> sliceIndices(indicesValue.size());
        for (int i = 0; i < indicesValue.size(); i++)
            sliceIndices[i] = indicesValue.get<int>(i);

        return Ptr<Layer>(SliceLayer::create(axis, sliceIndices));
    }
}
Exemple #5
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    PaddingLayerImpl(const LayerParams &params)
    {
        setParamsFrom(params);
        paddingValue = params.get<float>("value", 0);
        inputDims = params.get<int>("input_dims", -1);
        paddingType = params.get<String>("type", "constant");

        CV_Assert(params.has("paddings"));
        const DictValue& paddingsParam = params.get("paddings");
        CV_Assert((paddingsParam.size() & 1) == 0);

        paddings.resize(paddingsParam.size() / 2);
        for (int i = 0; i < paddings.size(); ++i)
        {
            paddings[i].first = paddingsParam.get<int>(i * 2);  // Pad before.
            paddings[i].second = paddingsParam.get<int>(i * 2 + 1);  // Pad after.
            CV_Assert(paddings[i].first >= 0, paddings[i].second >= 0);
        }
    }
    PriorBoxLayerImpl(const LayerParams &params)
    {
        setParamsFrom(params);
        _minSize = getParameter<unsigned>(params, "min_size");
        CV_Assert(_minSize > 0);

        _flip = getParameter<bool>(params, "flip");
        _clip = getParameter<bool>(params, "clip");

        _aspectRatios.clear();
        _aspectRatios.push_back(1.);

        getAspectRatios(params);
        getVariance(params);

        _numPriors = _aspectRatios.size();

        _maxSize = -1;
        if (params.has("max_size"))
        {
            _maxSize = params.get("max_size").get<float>(0);
            CV_Assert(_maxSize > _minSize);

            _numPriors += 1;
        }

        if (params.has("step_h") || params.has("step_w")) {
          CV_Assert(!params.has("step"));
          _stepY = getParameter<float>(params, "step_h");
          CV_Assert(_stepY > 0.);
          _stepX = getParameter<float>(params, "step_w");
          CV_Assert(_stepX > 0.);
        } else if (params.has("step")) {
          const float step = getParameter<float>(params, "step");
          CV_Assert(step > 0);
          _stepY = step;
          _stepX = step;
        } else {
          _stepY = 0;
          _stepX = 0;
        }
    }
Ptr<Layer> createLayerFromCaffe<ReshapeLayer>(LayerParams &params)
{
    int axis = params.get<int>("axis", 0);
    int numAxes = params.get<int>("num_axes", -1);
    bool enableReordering = params.get<bool>("reorder_dims", false);
    CV_Assert(numAxes >= -1);
    Range applyingRange = (numAxes == -1) ? Range(axis, INT_MAX) : Range(axis, axis + numAxes);

    Shape newShape;
    if (params.has("dim"))
    {
        const DictValue &paramShape = params.get("dim");
        newShape = Shape::all(paramShape.size());
        for (int i = 0; i < paramShape.size(); i++)
            newShape[i] = paramShape.get<int>(i);
    }
    else
        newShape = Shape::all(0);

    return Ptr<Layer>(ReshapeLayer::create(newShape, applyingRange, enableReordering));
}
    PriorBoxLayerImpl(const LayerParams &params)
        : _boxWidth(0), _boxHeight(0)
    {
        setParamsFrom(params);
        _minSize = getParameter<float>(params, "min_size", 0, false, 0);
        _flip = getParameter<bool>(params, "flip", 0, false, true);
        _clip = getParameter<bool>(params, "clip", 0, false, true);
        _bboxesNormalized = getParameter<bool>(params, "normalized_bbox", 0, false, true);

        _scales.clear();
        _aspectRatios.clear();

        getAspectRatios(params);
        getVariance(params);
        getParams("scales", params, &_scales);
        getParams("width", params, &_widths);
        getParams("height", params, &_heights);
        _explicitSizes = !_widths.empty();
        CV_Assert(_widths.size() == _heights.size());

        if (_explicitSizes)
        {
            CV_Assert(_aspectRatios.empty(), !params.has("min_size"), !params.has("max_size"));
            _numPriors = _widths.size();
        }
        else
        {
            CV_Assert(!_aspectRatios.empty(), _minSize > 0);
            _numPriors = _aspectRatios.size() + 1;  // + 1 for an aspect ratio 1.0
        }

        _maxSize = -1;
        if (params.has("max_size"))
        {
            _maxSize = params.get("max_size").get<float>(0);
            CV_Assert(_maxSize > _minSize);

            _numPriors += 1;
        }

        if (params.has("step_h") || params.has("step_w")) {
          CV_Assert(!params.has("step"));
          _stepY = getParameter<float>(params, "step_h");
          CV_Assert(_stepY > 0.);
          _stepX = getParameter<float>(params, "step_w");
          CV_Assert(_stepX > 0.);
        } else if (params.has("step")) {
          const float step = getParameter<float>(params, "step");
          CV_Assert(step > 0);
          _stepY = step;
          _stepX = step;
        } else {
          _stepY = 0;
          _stepX = 0;
        }
        if (params.has("offset_h") || params.has("offset_w"))
        {
            CV_Assert(!params.has("offset"), params.has("offset_h"), params.has("offset_w"));
            getParams("offset_h", params, &_offsetsY);
            getParams("offset_w", params, &_offsetsX);
            CV_Assert(_offsetsX.size() == _offsetsY.size());
            _numPriors *= std::max((size_t)1, 2 * (_offsetsX.size() - 1));
        }
        else
        {
            float offset = getParameter<float>(params, "offset", 0, false, 0.5);
            _offsetsX.assign(1, offset);
            _offsetsY.assign(1, offset);
        }
    }
Exemple #9
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void ONNXImporter::populateNet(Net dstNet)
{
    CV_Assert(model_proto.has_graph());
    opencv_onnx::GraphProto graph_proto = model_proto.graph();
    std::map<std::string, Mat> constBlobs = getGraphTensors(graph_proto);
    // List of internal blobs shapes.
    std::map<std::string, MatShape> outShapes;
    // 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)
    {
        opencv_onnx::ValueInfoProto valueInfoProto = graph_proto.input(i);
        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();
        }
        outShapes[valueInfoProto.name()] = inpShape;
    }

    std::string framework_name;
    if (model_proto.has_producer_name()) {
        framework_name = model_proto.producer_name();
    }

    // create map with network inputs (without const blobs)
    std::map<std::string, LayerInfo> layer_id;
    std::map<std::string, LayerInfo>::iterator layerId;
    std::map<std::string, MatShape>::iterator shapeIt;
    // 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);

    int layersSize = graph_proto.node_size();
    LayerParams layerParams;
    opencv_onnx::NodeProto node_proto;

    for(int li = 0; li < layersSize; li++)
    {
        node_proto = graph_proto.node(li);
        layerParams = getLayerParams(node_proto);
        CV_Assert(node_proto.output_size() >= 1);
        layerParams.name = node_proto.output(0);

        std::string layer_type = node_proto.op_type();
        layerParams.type = layer_type;


        if (layer_type == "MaxPool")
        {
            layerParams.type = "Pooling";
            layerParams.set("pool", "MAX");
            layerParams.set("ceil_mode", isCeilMode(layerParams));
        }
        else if (layer_type == "AveragePool")
        {
            layerParams.type = "Pooling";
            layerParams.set("pool", "AVE");
            layerParams.set("ceil_mode", isCeilMode(layerParams));
            layerParams.set("ave_pool_padded_area", framework_name == "pytorch");
        }
        else if (layer_type == "GlobalAveragePool")
        {
            layerParams.type = "Pooling";
            layerParams.set("pool", "AVE");
            layerParams.set("global_pooling", true);
        }
        else if (layer_type == "Add" || layer_type == "Sum")
        {
            if (layer_id.find(node_proto.input(1)) == layer_id.end())
            {
                Mat blob = getBlob(node_proto, constBlobs, 1);
                blob = blob.reshape(1, 1);
                if (blob.total() == 1) {
                    layerParams.type = "Power";
                    layerParams.set("shift", blob.at<float>(0));
                }
                else {
                    layerParams.type = "Scale";
                    layerParams.set("bias_term", true);
                    layerParams.blobs.push_back(blob);
                }
            }
            else {
                layerParams.type = "Eltwise";
            }
        }
        else if (layer_type == "Sub")
        {
            Mat blob = getBlob(node_proto, constBlobs, 1);
            if (blob.total() == 1) {
                layerParams.type = "Power";
                layerParams.set("shift", -blob.at<float>(0));
            }
            else {
                layerParams.type = "Scale";
                layerParams.set("has_bias", true);
                layerParams.blobs.push_back(-1.0f * blob.reshape(1, 1));
            }
        }
        else if (layer_type == "Div")
        {
            Mat blob = getBlob(node_proto, constBlobs, 1);
            CV_Assert_N(blob.type() == CV_32F, blob.total());
            if (blob.total() == 1)
            {
                layerParams.set("scale", 1.0f / blob.at<float>(0));
                layerParams.type = "Power";
            }
            else
            {
                layerParams.type = "Scale";
                divide(1.0, blob, blob);
                layerParams.blobs.push_back(blob);
                layerParams.set("bias_term", false);
            }
        }
        else if (layer_type == "Constant")
        {
            CV_Assert(node_proto.input_size() == 0);
            CV_Assert(layerParams.blobs.size() == 1);
            constBlobs.insert(std::make_pair(layerParams.name, layerParams.blobs[0]));
            continue;
        }
        else if (layer_type == "ImageScaler")
        {
            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";
            }
        }
        else if (layer_type == "LeakyRelu")
        {
            layerParams.type = "ReLU";
            replaceLayerParam(layerParams, "alpha", "negative_slope");
        }
        else if (layer_type == "LRN")
        {
            replaceLayerParam(layerParams, "size", "local_size");
        }
        else if (layer_type == "BatchNormalization")
        {
            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, constBlobs, 3);
            Mat stdData =  getBlob(node_proto, constBlobs, 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, constBlobs, 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, constBlobs, 2)); // biasData
            } else {
                layerParams.set("has_bias", false);
            }
        }
        else if (layer_type == "Gemm")
        {
            CV_Assert(node_proto.input_size() >= 2);
            layerParams.type = "InnerProduct";
            Mat weights = getBlob(node_proto, constBlobs, 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, constBlobs, 2);
                layerParams.blobs.push_back(bias);
            }

            layerParams.set("num_output", layerParams.blobs[0].size[ind_num_out]);
            layerParams.set("bias_term", node_proto.input_size() == 3);
        }
        else if (layer_type == "MatMul")
        {
            CV_Assert(node_proto.input_size() == 2);
            layerParams.type = "InnerProduct";
            Mat blob = getBlob(node_proto, constBlobs, 1);
            layerParams.blobs.push_back(blob.t());
            layerParams.set("bias_term", false);
            layerParams.set("num_output", layerParams.blobs[0].size[0]);
        }
        else if (layer_type == "Mul")
        {
            CV_Assert(node_proto.input_size() == 2);
            if (layer_id.find(node_proto.input(1)) == layer_id.end()) {
                Mat blob = getBlob(node_proto, constBlobs, 1);
                blob = blob.reshape(1, 1);
                if (blob.total() == 1) {
                    layerParams.set("scale", blob.at<float>(0));
                    layerParams.type = "Power";
                }
                else {
                    layerParams.blobs.push_back(blob);
                    layerParams.type = "Scale";
                }
            }
            else {
                layerParams.type = "Eltwise";
                layerParams.set("operation", "prod");
            }
        }
        else if (layer_type == "Conv")
        {
            CV_Assert(node_proto.input_size() >= 2);
            layerParams.type = "Convolution";
            for (int j = 1; j < node_proto.input_size(); j++) {
                layerParams.blobs.push_back(getBlob(node_proto, constBlobs, j));
            }
            layerParams.set("num_output", layerParams.blobs[0].size[0]);
            layerParams.set("bias_term", node_proto.input_size() == 3);
        }
        else if (layer_type == "ConvTranspose")
        {
            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, constBlobs, j));
            }
            layerParams.set("num_output", layerParams.blobs[0].size[1]);
            layerParams.set("bias_term", node_proto.input_size() == 3);
        }
        else if (layer_type == "Transpose")
        {
            layerParams.type = "Permute";
            replaceLayerParam(layerParams, "perm", "order");
        }
        else if (layer_type == "Unsqueeze")
        {
            CV_Assert(node_proto.input_size() == 1);
            Mat input = getBlob(node_proto, constBlobs, 0);

            DictValue axes = layerParams.get("axes");
            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);
            constBlobs.insert(std::make_pair(layerParams.name, out));
            continue;
        }
        else if (layer_type == "Reshape")
        {
            CV_Assert(node_proto.input_size() == 2 || layerParams.has("shape"));

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

                if (layer_id.find(node_proto.input(0)) == layer_id.end()) {
                    Mat input = getBlob(node_proto, constBlobs, 0);
                    Mat out = input.reshape(0, static_cast<std::vector<int> >(blob));
                    constBlobs.insert(std::make_pair(layerParams.name, out));
                    continue;
                }
                layerParams.set("dim", DictValue::arrayInt<int*>(
                            blob.ptr<int>(), blob.total() ));
            }
            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, constBlobs, 0);
                    Mat out = input.reshape(0, dim);
                    constBlobs.insert(std::make_pair(layerParams.name, out));
                    continue;
                }
                replaceLayerParam(layerParams, "shape", "dim");
            }
        }
        else if (layer_type == "Pad")
        {
            layerParams.type = "Padding";
        }
        else if (layer_type == "Shape")
        {
            CV_Assert(node_proto.input_size() == 1);
            shapeIt = outShapes.find(node_proto.input(0));
            CV_Assert(shapeIt != outShapes.end());
            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;

            constBlobs.insert(std::make_pair(layerParams.name, shapeMat));
            continue;
        }
        else if (layer_type == "Gather")
        {
            CV_Assert(node_proto.input_size() == 2);
            CV_Assert(layerParams.has("axis"));
            Mat input = getBlob(node_proto, constBlobs, 0);
            Mat indexMat = getBlob(node_proto, constBlobs, 1);
            CV_Assert_N(indexMat.type() == CV_32S, indexMat.total() == 1);
            int index = indexMat.at<int>(0);
            int axis = layerParams.get<int>("axis");

            std::vector<cv::Range> ranges(input.dims, Range::all());
            ranges[axis] = Range(index, index + 1);

            Mat out = input(ranges);
            constBlobs.insert(std::make_pair(layerParams.name, out));
            continue;
        }
        else if (layer_type == "Concat")
        {
            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;
                for (size_t i = 0; i < inputs.size(); ++i)
                {
                    inputs[i] = getBlob(node_proto, constBlobs, i);
                }
                Ptr<Layer> concat = ConcatLayer::create(layerParams);
                runLayer(concat, inputs, concatenated);

                CV_Assert(concatenated.size() == 1);
                constBlobs.insert(std::make_pair(layerParams.name, concatenated[0]));
                continue;
            }
        }
        else
        {
            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, constBlobs, j));
            }
         }

         int id = dstNet.addLayer(layerParams.name, layerParams.type, layerParams);
         layer_id.insert(std::make_pair(layerParams.name, LayerInfo(id, 0)));


         std::vector<MatShape> layerInpShapes, layerOutShapes, layerInternalShapes;
         for (int j = 0; j < node_proto.input_size(); j++) {
             layerId = layer_id.find(node_proto.input(j));
             if (layerId != layer_id.end()) {
                 dstNet.connect(layerId->second.layerId, layerId->second.outputId, id, j);
                 // Collect input shapes.
                 shapeIt = outShapes.find(node_proto.input(j));
                 CV_Assert(shapeIt != outShapes.end());
                 layerInpShapes.push_back(shapeIt->second);
             }
         }

         // Compute shape of output blob for this layer.
         Ptr<Layer> layer = dstNet.getLayer(id);
         layer->getMemoryShapes(layerInpShapes, 0, layerOutShapes, layerInternalShapes);
         CV_Assert(!layerOutShapes.empty());
         outShapes[layerParams.name] = layerOutShapes[0];
     }
 }
    PriorBoxLayerImpl(const LayerParams &params)
    {
        setParamsFrom(params);
        _minSize = getParameter<float>(params, "min_size", 0, false, 0);
        _flip = getParameter<bool>(params, "flip", 0, false, true);
        _clip = getParameter<bool>(params, "clip", 0, false, true);
        _bboxesNormalized = getParameter<bool>(params, "normalized_bbox", 0, false, true);

        _aspectRatios.clear();

        getAspectRatios(params);
        getVariance(params);

        _maxSize = -1;
        if (params.has("max_size"))
        {
            _maxSize = params.get("max_size").get<float>(0);
            CV_Assert(_maxSize > _minSize);
        }

        std::vector<float> widths, heights;
        getParams("width", params, &widths);
        getParams("height", params, &heights);
        _explicitSizes = !widths.empty();
        CV_Assert(widths.size() == heights.size());

        if (_explicitSizes)
        {
            CV_Assert(_aspectRatios.empty());
            CV_Assert(!params.has("min_size"));
            CV_Assert(!params.has("max_size"));
            _boxWidths = widths;
            _boxHeights = heights;
        }
        else
        {
            CV_Assert(_minSize > 0);
            _boxWidths.resize(1 + (_maxSize > 0 ? 1 : 0) + _aspectRatios.size());
            _boxHeights.resize(_boxWidths.size());
            _boxWidths[0] = _boxHeights[0] = _minSize;

            int i = 1;
            if (_maxSize > 0)
            {
                // second prior: aspect_ratio = 1, size = sqrt(min_size * max_size)
                _boxWidths[i] = _boxHeights[i] = sqrt(_minSize * _maxSize);
                i += 1;
            }

            // rest of priors
            for (size_t r = 0; r < _aspectRatios.size(); ++r)
            {
                float arSqrt = sqrt(_aspectRatios[r]);
                _boxWidths[i + r] = _minSize * arSqrt;
                _boxHeights[i + r] = _minSize / arSqrt;
            }
        }
        CV_Assert(_boxWidths.size() == _boxHeights.size());
        _numPriors = _boxWidths.size();

        if (params.has("step_h") || params.has("step_w")) {
          CV_Assert(!params.has("step"));
          _stepY = getParameter<float>(params, "step_h");
          CV_Assert(_stepY > 0.);
          _stepX = getParameter<float>(params, "step_w");
          CV_Assert(_stepX > 0.);
        } else if (params.has("step")) {
          const float step = getParameter<float>(params, "step");
          CV_Assert(step > 0);
          _stepY = step;
          _stepX = step;
        } else {
          _stepY = 0;
          _stepX = 0;
        }
        if (params.has("offset_h") || params.has("offset_w"))
        {
            CV_Assert(!params.has("offset"), params.has("offset_h"), params.has("offset_w"));
            getParams("offset_h", params, &_offsetsY);
            getParams("offset_w", params, &_offsetsX);
            CV_Assert(_offsetsX.size() == _offsetsY.size());
            _numPriors *= std::max((size_t)1, 2 * (_offsetsX.size() - 1));
        }
        else
        {
            float offset = getParameter<float>(params, "offset", 0, false, 0.5);
            _offsetsX.assign(1, offset);
            _offsetsY.assign(1, offset);
        }
    }