opencv/modules/dnn/test/test_caffe_importer.cpp

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#include "test_precomp.hpp"
#include "npy_blob.hpp"
#include <opencv2/dnn/shape_utils.hpp>
#include <set>

namespace opencv_test { namespace {

template<typename TString>
static std::string _tf(TString filename)
{
    return findDataFile(std::string("dnn/") + filename);
}

class Test_Caffe_nets : public DNNTestLayer
{
public:
    void testFaster(const std::string& proto, const std::string& model, const Mat& ref,
                    double scoreDiff = 0.0, double iouDiff = 0.0)
    {
        checkBackend();
        Net net = readNet(findDataFile("dnn/" + proto),
                          findDataFile("dnn/" + model, false));
        net.setPreferableBackend(backend);
        net.setPreferableTarget(target);

        if (target == DNN_TARGET_CPU_FP16)
            net.enableWinograd(false);

        Mat img = imread(findDataFile("dnn/dog416.png"));
        resize(img, img, Size(800, 600));
        Mat blob = blobFromImage(img, 1.0, Size(), Scalar(102.9801, 115.9465, 122.7717), false, false);
        Mat imInfo = (Mat_<float>(1, 3) << img.rows, img.cols, 1.6f);

        net.setInput(blob);
        net.setInput(imInfo, "im_info");
        // Output has shape 1x1xNx7 where N - number of detections.
        // An every detection is a vector of values [id, classId, confidence, left, top, right, bottom]
        Mat out = net.forward();
        scoreDiff = scoreDiff ? scoreDiff : default_l1;
        iouDiff = iouDiff ? iouDiff : default_lInf;
        normAssertDetections(ref, out, ("model name: " + model).c_str(), 0.8, scoreDiff, iouDiff);
    }
};

TEST(Reproducibility_SSD, Accuracy)
{
    applyTestTag(
        CV_TEST_TAG_MEMORY_512MB,
        CV_TEST_TAG_DEBUG_VERYLONG
    );

    // The classic engine importer no longer carries the Caffe-SSD specific
    // handling (LpNormalization/DetectionOutput); this model is supported on
    // the new engine only.
    auto engine_forced = static_cast<cv::dnn::EngineType>(
        cv::utils::getConfigurationParameterSizeT("OPENCV_FORCE_DNN_ENGINE", cv::dnn::ENGINE_AUTO));
    if (engine_forced == cv::dnn::ENGINE_CLASSIC)
    {
        applyTestTag(CV_TEST_TAG_DNN_SKIP_PARSER);
        return;
    }

    Net net = readNetFromONNX(findDataFile("dnn/onnx/models/ssd_vgg16.onnx", false));
    ASSERT_FALSE(net.empty());
    net.setPreferableBackend(DNN_BACKEND_OPENCV);

    Mat sample = imread(_tf("street.png"));
    ASSERT_TRUE(!sample.empty());

    if (sample.channels() == 4)
        cvtColor(sample, sample, COLOR_BGRA2BGR);

    Mat in_blob = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false);
    net.setInput(in_blob);

    Mat out = net.forward();

    Mat ref = blobFromNPY(_tf("ssd_out.npy"));
    normAssertDetections(ref, out, "", 0.06, 1e-4, 0.18);
}

TEST(Test_Caffe, multiple_inputs)
{
    const string model = findDataFile("dnn/layers/net_input.onnx");
    Net net = readNetFromONNX(model);
    net.setPreferableBackend(DNN_BACKEND_OPENCV);

    Mat first_image(10, 11, CV_32FC3);
    Mat second_image(10, 11, CV_32FC3);
    randu(first_image, -1, 1);
    randu(second_image, -1, 1);

    first_image = blobFromImage(first_image);
    second_image = blobFromImage(second_image);

    Mat first_image_blue_green = slice(first_image, Range::all(), Range(0, 2), Range::all(), Range::all());
    Mat first_image_red = slice(first_image, Range::all(), Range(2, 3), Range::all(), Range::all());
    Mat second_image_blue_green = slice(second_image, Range::all(), Range(0, 2), Range::all(), Range::all());
    Mat second_image_red = slice(second_image, Range::all(), Range(2, 3), Range::all(), Range::all());

    net.setInput(first_image_blue_green, "old_style_input_blue_green");
    net.setInput(first_image_red, "different_name_for_red");
    net.setInput(second_image_blue_green, "input_layer_blue_green");
    net.setInput(second_image_red, "old_style_input_red");
    Mat out = net.forward();

    normAssert(out, first_image + second_image);
}

INSTANTIATE_TEST_CASE_P(/**/, Test_Caffe_nets, dnnBackendsAndTargets());

}} // namespace