C++ LayerParams::set方法代码示例

void testInPlaceActivation(LayerParams& lp)
{
    EXPECT_FALSE(lp.name.empty());

    LayerParams pool;
    pool.set("pool", "ave");
    pool.set("kernel_w", 2);
    pool.set("kernel_h", 2);
    pool.set("stride_w", 2);
    pool.set("stride_h", 2);
    pool.type = "Pooling";

    Net net;
    int poolId = net.addLayer(pool.name, pool.type, pool);
    net.connect(0, 0, poolId, 0);
    net.addLayerToPrev(lp.name, lp.type, lp);

    Mat input({1, kNumChannels, 10, 10}, CV_32F);
    randu(input, -1.0f, 1.0f);
    net.setInput(input);
    Mat outputDefault = net.forward(lp.name).clone();

    net.setInput(input);
    net.setPreferableBackend(DNN_BACKEND_HALIDE);
    Mat outputHalide = net.forward(lp.name).clone();
    normAssert(outputDefault, outputHalide);
}

TEST_P(Concat, Accuracy)
{
    Vec3i inSize = get<0>(GetParam());
    Vec3i numChannels = get<1>(GetParam());

    Net net;

    std::vector<int> convLayerIds;
    convLayerIds.reserve(numChannels.channels);
    for (int i = 0, n = numChannels.channels; i < n; ++i)
    {
        if (!numChannels[i])
            break;

        Mat weights({numChannels[i], inSize[0], 1, 1}, CV_32F);
        randu(weights, -1.0f, 1.0f);

        LayerParams convParam;
        convParam.set("kernel_w", 1);
        convParam.set("kernel_h", 1);
        convParam.set("num_output", numChannels[i]);
        convParam.set("bias_term", false);
        convParam.type = "Convolution";
        std::ostringstream ss;
        ss << "convLayer" << i;
        convParam.name = ss.str();
        convParam.blobs.push_back(weights);

        int layerId = net.addLayer(convParam.name, convParam.type, convParam);
        convLayerIds.push_back(layerId);
        net.connect(0, 0, layerId, 0);
    }

    LayerParams concatParam;
    concatParam.type = "Concat";
    concatParam.name = "testLayer";
    int concatId = net.addLayer(concatParam.name, concatParam.type, concatParam);
    net.connect(0, 0, concatId, 0);
    for (int i = 0; i < convLayerIds.size(); ++i)
    {
        net.connect(convLayerIds[i], 0, concatId, i + 1);
    }

    Mat input({1, inSize[0], inSize[1], inSize[2]}, CV_32F);
    randu(input, -1.0f, 1.0f);

    net.setInput(input);
    Mat outputDefault = net.forward(concatParam.name).clone();

    net.setPreferableBackend(DNN_BACKEND_HALIDE);
    Mat outputHalide = net.forward(concatParam.name).clone();
    normAssert(outputDefault, outputHalide);
}

TEST_P(AvePooling, Accuracy)
{
    int inChannels = get<0>(GetParam());
    Size outSize = get<1>(GetParam());;  // Input size will be computed from parameters.
    Size kernel = get<2>(GetParam());
    Size stride = get<3>(GetParam());

    const int inWidth = (outSize.width - 1) * stride.width + kernel.width;
    const int inHeight = (outSize.height - 1) * stride.height + kernel.height;

    LayerParams lp;
    lp.set("pool", "ave");
    lp.set("kernel_w", kernel.width);
    lp.set("kernel_h", kernel.height);
    lp.set("stride_w", stride.width);
    lp.set("stride_h", stride.height);
    lp.type = "Pooling";
    lp.name = "testLayer";

    Mat input({1, inChannels, inHeight, inWidth}, CV_32F);
    test(lp, input);
}

TEST_P(BatchNorm, Accuracy)
{
    bool hasWeights = get<0>(GetParam());
    bool hasBias = get<1>(GetParam());
    float epsilon = get<2>(GetParam());

    LayerParams lp;
    lp.set("has_weight", hasWeights);
    lp.set("has_bias", hasBias);
    lp.set("eps", epsilon);
    lp.type = "BatchNorm";
    lp.name = "testLayer";

    lp.blobs.reserve(4);
    for (int i = 0; i < 3; ++i)
        lp.blobs.push_back(Mat({kNumChannels}, CV_32F));
    if (hasBias || hasWeights)
        lp.blobs.push_back(Mat({kNumChannels}, CV_32F));

    for (Mat& m : lp.blobs)
        randu(m, 0.0f, 1.0f);

    testInPlaceActivation(lp);
}

    void populateNet(Net dstNet)
    {
        CV_TRACE_FUNCTION();

        int layersSize = net.layer_size();
        layerCounter.clear();
        addedBlobs.clear();
        addedBlobs.reserve(layersSize + 1);

        //setup input layer names
        std::vector<String> netInputs(net.input_size());
        {
            for (int inNum = 0; inNum < net.input_size(); inNum++)
            {
                addedBlobs.push_back(BlobNote(net.input(inNum), 0, inNum));
                netInputs[inNum] = net.input(inNum);
            }
        }

        for (int li = 0; li < layersSize; li++)
        {
            const caffe::LayerParameter &layer = net.layer(li);
            String name = layer.name();
            String type = layer.type();
            LayerParams layerParams;

            extractLayerParams(layer, layerParams);
            extractBinaryLayerParams(layer, layerParams);

            int repetitions = layerCounter[name]++;
            if (repetitions)
                name += String("_") + toString(repetitions);

            if (type == "Input")
            {
                for (int outNum = 0; outNum < layer.top_size(); outNum++)
                {
                    addOutput(layer, 0, outNum);
                    addedBlobs.back().outNum = netInputs.size();
                    netInputs.push_back(addedBlobs.back().name);
                }
                continue;
            }
            else if (type == "BatchNorm")
            {
                if (!layerParams.get<bool>("use_global_stats", true))
                {
                    CV_Assert_N(layer.bottom_size() == 1, layer.top_size() == 1);

                    LayerParams mvnParams;
                    mvnParams.set("eps", layerParams.get<float>("eps", 1e-5));
                    std::string mvnName = name + "/mvn";

                    int repetitions = layerCounter[mvnName]++;
                    if (repetitions)
                        mvnName += String("_") + toString(repetitions);

                    int mvnId = dstNet.addLayer(mvnName, "MVN", mvnParams);
                    addInput(layer.bottom(0), mvnId, 0, dstNet);
                    addOutput(layer, mvnId, 0);
                    net.mutable_layer(li)->set_bottom(0, layer.top(0));
                    layerParams.blobs[0].setTo(0);  // mean
                    layerParams.blobs[1].setTo(1);  // std
                }
            }
            else if ("ConvolutionDepthwise" == type)
            {
                type = "Convolution";
            }

            int id = dstNet.addLayer(name, type, layerParams);

            for (int inNum = 0; inNum < layer.bottom_size(); inNum++)
                addInput(layer.bottom(inNum), id, inNum, dstNet);

            for (int outNum = 0; outNum < layer.top_size(); outNum++)
                addOutput(layer, id, outNum);
        }
        dstNet.setInputsNames(netInputs);

        addedBlobs.clear();
    }

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";
            }
//.........这里部分代码省略.........

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();

        if(attribute_name == "kernel_shape")
        {
            CV_Assert(attribute_proto.ints_size() == 2);
            lp.set("kernel_h", saturate_cast<int32_t>(attribute_proto.ints(0)));
            lp.set("kernel_w", saturate_cast<int32_t>(attribute_proto.ints(1)));
        }
        else if(attribute_name == "strides")
        {
            CV_Assert(attribute_proto.ints_size() == 2);
            lp.set("stride_h", saturate_cast<int32_t>(attribute_proto.ints(0)));
            lp.set("stride_w", saturate_cast<int32_t>(attribute_proto.ints(1)));
        }
        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() == 4);
                lp.set("pad_t", saturate_cast<int32_t>(attribute_proto.ints(0)));
                lp.set("pad_l", saturate_cast<int32_t>(attribute_proto.ints(1)));
                lp.set("pad_b", saturate_cast<int32_t>(attribute_proto.ints(2)));
                lp.set("pad_r", saturate_cast<int32_t>(attribute_proto.ints(3)));
            }
        }
        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() == 2);
            lp.set("dilation_h",  saturate_cast<int32_t>(attribute_proto.ints(0)));
            lp.set("dilation_w",  saturate_cast<int32_t>(attribute_proto.ints(1)));
        }
        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)
        {
            const ::google::protobuf::RepeatedField< ::google::protobuf::int64> src = attribute_proto.ints();
            std::vector<int32_t> dst(attribute_proto.ints_size());
            convertInt64ToInt32(src, dst, attribute_proto.ints_size());
            lp.set(attribute_proto.name(), DictValue::arrayInt(&dst[0], attribute_proto.ints_size()));
        }
        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() || attribute_proto.strings_size() > 0 ||
                    attribute_proto.tensors_size() > 0 || attribute_proto.graphs_size() > 0)
        {
//.........这里部分代码省略.........