C++ Net::connect方法代码示例

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);
}

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);
}

//////////////////////////////////////////////////////////////////////////////
// Max pooling - unpooling
//////////////////////////////////////////////////////////////////////////////
TEST(MaxPoolUnpool_Halide, Accuracy)
{
    LayerParams pool;
    pool.set("pool", "max");
    pool.set("kernel_w", 2);
    pool.set("kernel_h", 2);
    pool.set("stride_w", 2);
    pool.set("stride_h", 2);
    pool.set("pad_w", 0);
    pool.set("pad_h", 0);
    pool.type = "Pooling";
    pool.name = "testPool";

    LayerParams unpool;
    unpool.set("pool_k_w", 2);
    unpool.set("pool_k_h", 2);
    unpool.set("pool_stride_w", 2);
    unpool.set("pool_stride_h", 2);
    unpool.set("pool_pad_w", 0);
    unpool.set("pool_pad_h", 0);
    unpool.type = "MaxUnpool";
    unpool.name = "testUnpool";

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

    int unpoolId = net.addLayer(unpool.name, unpool.type, unpool);
    net.connect(poolId, 0, unpoolId, 0);
    net.connect(poolId, 1, unpoolId, 1);

    Mat input({1, 1, 4, 4}, CV_32F);
    randu(input, -1.0f, 1.0f);
    net.setInput(input);
    Mat outputDefault = net.forward("testUnpool").clone();

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

static void test(LayerParams& params, Mat& input)
{
    randu(input, -1.0f, 1.0f);

    Net net;
    int lid = net.addLayer(params.name, params.type, params);
    net.connect(0, 0, lid, 0);

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

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

    void addInput(const std::string &name, int layerId, int inNum, Net &dstNet)
    {
        int idx;
        for (idx = (int)addedBlobs.size() - 1; idx >= 0; idx--)
        {
            if (addedBlobs[idx].name == name)
                break;
        }

        if (idx < 0)
        {
            CV_Error(Error::StsObjectNotFound, "Can't find output blob \"" + name + "\"");
            return;
        }

        dstNet.connect(addedBlobs[idx].layerId, addedBlobs[idx].outNum, layerId, inNum);
    }

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