static void runTorchNet(String prefix, int targetId = DNN_TARGET_CPU, String outLayerName = "",
                        bool check2ndBlob = false, bool isBinary = false)
{
    String suffix = (isBinary) ? ".dat" : ".txt";

    Net net = readNetFromTorch(_tf(prefix + "_net" + suffix), isBinary);
    ASSERT_FALSE(net.empty());

    net.setPreferableBackend(DNN_BACKEND_DEFAULT);
    net.setPreferableTarget(targetId);

    Mat inp, outRef;
    ASSERT_NO_THROW( inp = readTorchBlob(_tf(prefix + "_input" + suffix), isBinary) );
    ASSERT_NO_THROW( outRef = readTorchBlob(_tf(prefix + "_output" + suffix), isBinary) );

    if (outLayerName.empty())
        outLayerName = net.getLayerNames().back();

    net.setInput(inp, "0");
    std::vector<Mat> outBlobs;
    net.forward(outBlobs, outLayerName);
    normAssert(outRef, outBlobs[0]);

    if (check2ndBlob)
    {
        Mat out2 = outBlobs[1];
        Mat ref2 = readTorchBlob(_tf(prefix + "_output_2" + suffix), isBinary);
        normAssert(out2, ref2);
    }
}
TEST_P(Test_Torch_nets, ENet_accuracy)
{
    Net net;
    {
        const string model = findDataFile("dnn/Enet-model-best.net", false);
        net = readNetFromTorch(model, true);
        ASSERT_TRUE(!net.empty());
    }

    net.setPreferableTarget(GetParam());

    Mat sample = imread(_tf("street.png", false));
    Mat inputBlob = blobFromImage(sample, 1./255);

    net.setInput(inputBlob, "");
    Mat out = net.forward();
    Mat ref = blobFromNPY(_tf("torch_enet_prob.npy", false));
    // Due to numerical instability in Pooling-Unpooling layers (indexes jittering)
    // thresholds for ENet must be changed. Accuracy of results was checked on
    // Cityscapes dataset and difference in mIOU with Torch is 10E-4%
    normAssert(ref, out, "", 0.00044, 0.44);

    const int N = 3;
    for (int i = 0; i < N; i++)
    {
        net.setInput(inputBlob, "");
        Mat out = net.forward();
        normAssert(ref, out, "", 0.00044, 0.44);
    }
}
示例#3
0
    void runTorchNet(const String& prefix, String outLayerName = "",
                     bool check2ndBlob = false, bool isBinary = false,
                     double l1 = 0.0, double lInf = 0.0)
    {
        String suffix = (isBinary) ? ".dat" : ".txt";

        Mat inp, outRef;
        ASSERT_NO_THROW( inp = readTorchBlob(_tf(prefix + "_input" + suffix), isBinary) );
        ASSERT_NO_THROW( outRef = readTorchBlob(_tf(prefix + "_output" + suffix), isBinary) );

        checkBackend(backend, target, &inp, &outRef);

        Net net = readNetFromTorch(_tf(prefix + "_net" + suffix), isBinary);
        ASSERT_FALSE(net.empty());

        net.setPreferableBackend(backend);
        net.setPreferableTarget(target);

        if (outLayerName.empty())
            outLayerName = net.getLayerNames().back();

        net.setInput(inp);
        std::vector<Mat> outBlobs;
        net.forward(outBlobs, outLayerName);
        l1 = l1 ? l1 : default_l1;
        lInf = lInf ? lInf : default_lInf;
        normAssert(outRef, outBlobs[0], "", l1, lInf);

        if (check2ndBlob && backend != DNN_BACKEND_INFERENCE_ENGINE)
        {
            Mat out2 = outBlobs[1];
            Mat ref2 = readTorchBlob(_tf(prefix + "_output_2" + suffix), isBinary);
            normAssert(out2, ref2, "", l1, lInf);
        }
    }
TEST_P(Reproducibility_ResNet50, Accuracy)
{
    Net net = readNetFromCaffe(findDataFile("dnn/ResNet-50-deploy.prototxt", false),
                               findDataFile("dnn/ResNet-50-model.caffemodel", false));

    int targetId = GetParam();
    net.setPreferableTarget(targetId);

    float l1 = (targetId == DNN_TARGET_OPENCL_FP16) ? 3e-5 : 1e-5;
    float lInf = (targetId == DNN_TARGET_OPENCL_FP16) ? 6e-3 : 1e-4;

    Mat input = blobFromImage(imread(_tf("googlenet_0.png")), 1.0f, Size(224,224), Scalar(), false);
    ASSERT_TRUE(!input.empty());

    net.setInput(input);
    Mat out = net.forward();

    Mat ref = blobFromNPY(_tf("resnet50_prob.npy"));
    normAssert(ref, out, "", l1, lInf);

    if (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16)
    {
        UMat out_umat;
        net.forward(out_umat);
        normAssert(ref, out_umat, "out_umat", l1, lInf);

        std::vector<UMat> out_umats;
        net.forward(out_umats);
        normAssert(ref, out_umats[0], "out_umat_vector", l1, lInf);
    }
}
示例#5
0
TEST_P(Test_TensorFlow_nets, MobileNet_SSD)
{
    std::string netPath = findDataFile("dnn/ssd_mobilenet_v1_coco.pb", false);
    std::string netConfig = findDataFile("dnn/ssd_mobilenet_v1_coco.pbtxt", false);
    std::string imgPath = findDataFile("dnn/street.png", false);

    Mat inp;
    resize(imread(imgPath), inp, Size(300, 300));
    inp = blobFromImage(inp, 1.0f / 127.5, Size(), Scalar(127.5, 127.5, 127.5), true);

    std::vector<String> outNames(3);
    outNames[0] = "concat";
    outNames[1] = "concat_1";
    outNames[2] = "detection_out";

    std::vector<Mat> target(outNames.size());
    for (int i = 0; i < outNames.size(); ++i)
    {
        std::string path = findDataFile("dnn/tensorflow/ssd_mobilenet_v1_coco." + outNames[i] + ".npy", false);
        target[i] = blobFromNPY(path);
    }

    Net net = readNetFromTensorflow(netPath, netConfig);

    net.setPreferableTarget(GetParam());

    net.setInput(inp);

    std::vector<Mat> output;
    net.forward(output, outNames);

    normAssert(target[0].reshape(1, 1), output[0].reshape(1, 1), "", 1e-5, 1.5e-4);
    normAssert(target[1].reshape(1, 1), output[1].reshape(1, 1), "", 1e-5, 3e-4);
    normAssert(target[2].reshape(1, 1), output[2].reshape(1, 1), "", 4e-5, 1e-2);
}
示例#6
0
// inp = cv.imread('opencv_extra/testdata/cv/ximgproc/sources/08.png')
// inp = inp[:,:,[2, 1, 0]].astype(np.float32).reshape(1, 512, 512, 3)
// outs = sess.run([sess.graph.get_tensor_by_name('feature_fusion/Conv_7/Sigmoid:0'),
//                  sess.graph.get_tensor_by_name('feature_fusion/concat_3:0')],
//                 feed_dict={'input_images:0': inp})
// scores = np.ascontiguousarray(outs[0].transpose(0, 3, 1, 2))
// geometry = np.ascontiguousarray(outs[1].transpose(0, 3, 1, 2))
// np.save('east_text_detection.scores.npy', scores)
// np.save('east_text_detection.geometry.npy', geometry)
TEST_P(Test_TensorFlow_nets, EAST_text_detection)
{
    checkBackend();
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE < 2018030000
    if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
        throw SkipTestException("Test is enabled starts from OpenVINO 2018R3");
#endif

    std::string netPath = findDataFile("dnn/frozen_east_text_detection.pb", false);
    std::string imgPath = findDataFile("cv/ximgproc/sources/08.png", false);
    std::string refScoresPath = findDataFile("dnn/east_text_detection.scores.npy", false);
    std::string refGeometryPath = findDataFile("dnn/east_text_detection.geometry.npy", false);

    Net net = readNet(findDataFile("dnn/frozen_east_text_detection.pb", false));

    net.setPreferableBackend(backend);
    net.setPreferableTarget(target);

    Mat img = imread(imgPath);
    Mat inp = blobFromImage(img, 1.0, Size(), Scalar(123.68, 116.78, 103.94), true, false);
    net.setInput(inp);

    std::vector<Mat> outs;
    std::vector<String> outNames(2);
    outNames[0] = "feature_fusion/Conv_7/Sigmoid";
    outNames[1] = "feature_fusion/concat_3";
    net.forward(outs, outNames);

    Mat scores = outs[0];
    Mat geometry = outs[1];

    // Scores are in range [0, 1]. Geometry values are in range [-0.23, 290]
    double l1_scores = default_l1, lInf_scores = default_lInf;
    double l1_geometry = default_l1, lInf_geometry = default_lInf;
    if (target == DNN_TARGET_OPENCL_FP16)
    {
        lInf_scores = 0.11;
        l1_geometry = 0.28; lInf_geometry = 5.94;
    }
    else if (target == DNN_TARGET_MYRIAD)
    {
        lInf_scores = 0.214;
        l1_geometry = 0.47; lInf_geometry = 15.34;
    }
    else
    {
        l1_geometry = 1e-4, lInf_geometry = 3e-3;
    }
    normAssert(scores, blobFromNPY(refScoresPath), "scores", l1_scores, lInf_scores);
    normAssert(geometry, blobFromNPY(refGeometryPath), "geometry", l1_geometry, lInf_geometry);
}
示例#7
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TEST_P(Test_TensorFlow_nets, Inception_v2_SSD)
{
    std::string proto = findDataFile("dnn/ssd_inception_v2_coco_2017_11_17.pbtxt", false);
    std::string model = findDataFile("dnn/ssd_inception_v2_coco_2017_11_17.pb", false);

    Net net = readNetFromTensorflow(model, proto);
    Mat img = imread(findDataFile("dnn/street.png", false));
    Mat blob = blobFromImage(img, 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), true, false);

    net.setPreferableTarget(GetParam());

    net.setInput(blob);
    // 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();
    out = out.reshape(1, out.total() / 7);

    Mat detections;
    for (int i = 0; i < out.rows; ++i)
    {
        if (out.at<float>(i, 2) > 0.5)
          detections.push_back(out.row(i).colRange(1, 7));
    }

    Mat ref = (Mat_<float>(5, 6) << 1, 0.90176028, 0.19872092, 0.36311883, 0.26461923, 0.63498729,
                                    3, 0.93569964, 0.64865261, 0.45906419, 0.80675775, 0.65708131,
                                    3, 0.75838411, 0.44668293, 0.45907149, 0.49459291, 0.52197015,
                                    10, 0.95932811, 0.38349164, 0.32528657, 0.40387636, 0.39165527,
                                    10, 0.93973452, 0.66561931, 0.37841269, 0.68074018, 0.42907384);
    normAssert(detections, ref);
}
TEST_P(Reproducibility_SqueezeNet_v1_1, Accuracy)
{
    Net net = readNetFromCaffe(findDataFile("dnn/squeezenet_v1.1.prototxt", false),
                               findDataFile("dnn/squeezenet_v1.1.caffemodel", false));

    int targetId = GetParam();
    net.setPreferableTarget(targetId);

    Mat input = blobFromImage(imread(_tf("googlenet_0.png")), 1.0f, Size(227,227), Scalar(), false);
    ASSERT_TRUE(!input.empty());

    Mat out;
    if (targetId == DNN_TARGET_OPENCL)
    {
        // Firstly set a wrong input blob and run the model to receive a wrong output.
        // Then set a correct input blob to check CPU->GPU synchronization is working well.
        net.setInput(input * 2.0f);
        out = net.forward();
    }
    net.setInput(input);
    out = net.forward();

    Mat ref = blobFromNPY(_tf("squeezenet_v1.1_prob.npy"));
    normAssert(ref, out);
}
示例#9
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TEST_P(Test_TensorFlow_nets, opencv_face_detector_uint8)
{
    std::string proto = findDataFile("dnn/opencv_face_detector.pbtxt", false);
    std::string model = findDataFile("dnn/opencv_face_detector_uint8.pb", false);

    Net net = readNetFromTensorflow(model, proto);
    Mat img = imread(findDataFile("gpu/lbpcascade/er.png", false));
    Mat blob = blobFromImage(img, 1.0, Size(), Scalar(104.0, 177.0, 123.0), false, false);

    net.setPreferableTarget(GetParam());

    net.setInput(blob);
    // 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();

    // References are from test for Caffe model.
    Mat ref = (Mat_<float>(6, 5) << 0.99520785, 0.80997437, 0.16379407, 0.87996572, 0.26685631,
                                    0.9934696, 0.2831718, 0.50738752, 0.345781, 0.5985168,
                                    0.99096733, 0.13629119, 0.24892329, 0.19756334, 0.3310290,
                                    0.98977017, 0.23901358, 0.09084064, 0.29902688, 0.1769477,
                                    0.97203469, 0.67965847, 0.06876482, 0.73999709, 0.1513494,
                                    0.95097077, 0.51901293, 0.45863652, 0.5777427, 0.5347801);
    normAssert(out.reshape(1, out.total() / 7).rowRange(0, 6).colRange(2, 7), ref, "", 2.8e-4, 3.4e-3);
}
// Check accuracy of style transfer models from https://github.com/jcjohnson/fast-neural-style
// th fast_neural_style.lua \
//   -input_image ~/opencv_extra/testdata/dnn/googlenet_1.png \
//   -output_image lena.png \
//   -median_filter 0 \
//   -image_size 0 \
//   -model models/eccv16/starry_night.t7
// th fast_neural_style.lua \
//   -input_image ~/opencv_extra/testdata/dnn/googlenet_1.png \
//   -output_image lena.png \
//   -median_filter 0 \
//   -image_size 0 \
//   -model models/instance_norm/feathers.t7
TEST_P(Test_Torch_nets, FastNeuralStyle_accuracy)
{
    std::string models[] = {"dnn/fast_neural_style_eccv16_starry_night.t7",
                            "dnn/fast_neural_style_instance_norm_feathers.t7"};
    std::string targets[] = {"dnn/lena_starry_night.png", "dnn/lena_feathers.png"};

    for (int i = 0; i < 2; ++i)
    {
        const string model = findDataFile(models[i], false);
        Net net = readNetFromTorch(model);

        net.setPreferableTarget(GetParam());

        Mat img = imread(findDataFile("dnn/googlenet_1.png", false));
        Mat inputBlob = blobFromImage(img, 1.0, Size(), Scalar(103.939, 116.779, 123.68), false);

        net.setInput(inputBlob);
        Mat out = net.forward();

        // Deprocessing.
        getPlane(out, 0, 0) += 103.939;
        getPlane(out, 0, 1) += 116.779;
        getPlane(out, 0, 2) += 123.68;
        out = cv::min(cv::max(0, out), 255);

        Mat ref = imread(findDataFile(targets[i]));
        Mat refBlob = blobFromImage(ref, 1.0, Size(), Scalar(), false);

        normAssert(out, refBlob, "", 0.5, 1.1);
    }
}
TEST(Test_Caffe, multiple_inputs)
{
    const string proto = findDataFile("dnn/layers/net_input.prototxt", false);
    Net net = readNetFromCaffe(proto);

    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);
}
示例#12
0
TEST_P(Test_Torch_nets, OpenFace_accuracy)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE < 2018030000
    if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
        throw SkipTestException("Test is enabled starts from OpenVINO 2018R3");
#endif
    checkBackend();
    if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16)
        throw SkipTestException("");

    const string model = findDataFile("dnn/openface_nn4.small2.v1.t7", false);
    Net net = readNetFromTorch(model);

    net.setPreferableBackend(backend);
    net.setPreferableTarget(target);

    Mat sample = imread(findDataFile("cv/shared/lena.png", false));
    Mat sampleF32(sample.size(), CV_32FC3);
    sample.convertTo(sampleF32, sampleF32.type());
    sampleF32 /= 255;
    resize(sampleF32, sampleF32, Size(96, 96), 0, 0, INTER_NEAREST);

    Mat inputBlob = blobFromImage(sampleF32, 1.0, Size(), Scalar(), /*swapRB*/true);

    net.setInput(inputBlob);
    Mat out = net.forward();

    Mat outRef = readTorchBlob(_tf("net_openface_output.dat"), true);
    normAssert(out, outRef, "", default_l1, default_lInf);
}
示例#13
0
TEST(Test_Darknet, read_yolo_voc_stream)
{
    Mat ref;
    Mat sample = imread(_tf("dog416.png"));
    Mat inp = blobFromImage(sample, 1.0/255, Size(416, 416), Scalar(), true, false);
    const std::string cfgFile = findDataFile("dnn/yolo-voc.cfg", false);
    const std::string weightsFile = findDataFile("dnn/yolo-voc.weights", false);
    // Import by paths.
    {
        Net net = readNetFromDarknet(cfgFile, weightsFile);
        net.setInput(inp);
        net.setPreferableBackend(DNN_BACKEND_OPENCV);
        ref = net.forward();
    }
    // Import from bytes array.
    {
        std::string cfg, weights;
        readFileInMemory(cfgFile, cfg);
        readFileInMemory(weightsFile, weights);

        Net net = readNetFromDarknet(&cfg[0], cfg.size(), &weights[0], weights.size());
        net.setInput(inp);
        net.setPreferableBackend(DNN_BACKEND_OPENCV);
        Mat out = net.forward();
        normAssert(ref, out);
    }
}
TEST(Reproducibility_FCN, Accuracy)
{
    Net net;
    {
        const string proto = findDataFile("dnn/fcn8s-heavy-pascal.prototxt", false);
        const string model = findDataFile("dnn/fcn8s-heavy-pascal.caffemodel", false);
        net = readNetFromCaffe(proto, model);
        ASSERT_FALSE(net.empty());
    }

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

    std::vector<int> layerIds;
    std::vector<size_t> weights, blobs;
    net.getMemoryConsumption(shape(1,3,227,227), layerIds, weights, blobs);

    net.setInput(blobFromImage(sample, 1.0f, Size(500, 500), Scalar(), false), "data");
    Mat out = net.forward("score");

    Mat refData = imread(_tf("caffe_fcn8s_prob.png"), IMREAD_ANYDEPTH);
    int shape[] = {1, 21, 500, 500};
    Mat ref(4, shape, CV_32FC1, refData.data);

    normAssert(ref, out);
}
示例#15
0
TEST_P(Test_TensorFlow_nets, MobileNet_SSD)
{
    checkBackend();
    if ((backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_CPU) ||
        (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16))
        throw SkipTestException("");

    std::string netPath = findDataFile("dnn/ssd_mobilenet_v1_coco.pb", false);
    std::string netConfig = findDataFile("dnn/ssd_mobilenet_v1_coco.pbtxt", false);
    std::string imgPath = findDataFile("dnn/street.png", false);

    Mat inp;
    resize(imread(imgPath), inp, Size(300, 300));
    inp = blobFromImage(inp, 1.0f / 127.5, Size(), Scalar(127.5, 127.5, 127.5), true);

    std::vector<String> outNames(3);
    outNames[0] = "concat";
    outNames[1] = "concat_1";
    outNames[2] = "detection_out";

    std::vector<Mat> refs(outNames.size());
    for (int i = 0; i < outNames.size(); ++i)
    {
        std::string path = findDataFile("dnn/tensorflow/ssd_mobilenet_v1_coco." + outNames[i] + ".npy", false);
        refs[i] = blobFromNPY(path);
    }

    Net net = readNetFromTensorflow(netPath, netConfig);
    net.setPreferableBackend(backend);
    net.setPreferableTarget(target);

    net.setInput(inp);

    std::vector<Mat> output;
    net.forward(output, outNames);

    normAssert(refs[0].reshape(1, 1), output[0].reshape(1, 1), "", 1e-5, 1.5e-4);
    normAssert(refs[1].reshape(1, 1), output[1].reshape(1, 1), "", 1e-5, 3e-4);
    normAssertDetections(refs[2], output[2], "", 0.2);
}
示例#16
0
TEST(Test_TensorFlow, Mask_RCNN)
{
    std::string proto = findDataFile("dnn/mask_rcnn_inception_v2_coco_2018_01_28.pbtxt", false);
    std::string model = findDataFile("dnn/mask_rcnn_inception_v2_coco_2018_01_28.pb", false);

    Net net = readNetFromTensorflow(model, proto);
    Mat img = imread(findDataFile("dnn/street.png", false));
    Mat refDetections = blobFromNPY(path("mask_rcnn_inception_v2_coco_2018_01_28.detection_out.npy"));
    Mat refMasks = blobFromNPY(path("mask_rcnn_inception_v2_coco_2018_01_28.detection_masks.npy"));
    Mat blob = blobFromImage(img, 1.0f, Size(800, 800), Scalar(), true, false);

    net.setPreferableBackend(DNN_BACKEND_OPENCV);

    net.setInput(blob);

    // Mask-RCNN predicts bounding boxes and segmentation masks.
    std::vector<String> outNames(2);
    outNames[0] = "detection_out_final";
    outNames[1] = "detection_masks";

    std::vector<Mat> outs;
    net.forward(outs, outNames);

    Mat outDetections = outs[0];
    Mat outMasks = outs[1];
    normAssertDetections(refDetections, outDetections, "", /*threshold for zero confidence*/1e-5);

    // Output size of masks is NxCxHxW where
    // N - number of detected boxes
    // C - number of classes (excluding background)
    // HxW - segmentation shape
    const int numDetections = outDetections.size[2];

    int masksSize[] = {1, numDetections, outMasks.size[2], outMasks.size[3]};
    Mat masks(4, &masksSize[0], CV_32F);

    std::vector<cv::Range> srcRanges(4, cv::Range::all());
    std::vector<cv::Range> dstRanges(4, cv::Range::all());

    outDetections = outDetections.reshape(1, outDetections.total() / 7);
    for (int i = 0; i < numDetections; ++i)
    {
        // Get a class id for this bounding box and copy mask only for that class.
        int classId = static_cast<int>(outDetections.at<float>(i, 1));
        srcRanges[0] = dstRanges[1] = cv::Range(i, i + 1);
        srcRanges[1] = cv::Range(classId, classId + 1);
        outMasks(srcRanges).copyTo(masks(dstRanges));
    }
    cv::Range topRefMasks[] = {Range::all(), Range(0, numDetections), Range::all(), Range::all()};
    normAssert(masks, refMasks(&topRefMasks[0]));
}
示例#17
0
TEST_P(Test_Torch_nets, ENet_accuracy)
{
    checkBackend();
    if (backend == DNN_BACKEND_INFERENCE_ENGINE ||
        (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16))
        throw SkipTestException("");

    Net net;
    {
        const string model = findDataFile("dnn/Enet-model-best.net", false);
        net = readNetFromTorch(model, true);
        ASSERT_TRUE(!net.empty());
    }

    net.setPreferableBackend(backend);
    net.setPreferableTarget(target);

    Mat sample = imread(_tf("street.png", false));
    Mat inputBlob = blobFromImage(sample, 1./255, Size(), Scalar(), /*swapRB*/true);

    net.setInput(inputBlob, "");
    Mat out = net.forward();
    Mat ref = blobFromNPY(_tf("torch_enet_prob.npy", false));
    // Due to numerical instability in Pooling-Unpooling layers (indexes jittering)
    // thresholds for ENet must be changed. Accuracy of results was checked on
    // Cityscapes dataset and difference in mIOU with Torch is 10E-4%
    normAssert(ref, out, "", 0.00044, /*target == DNN_TARGET_CPU ? 0.453 : */0.552);
    normAssertSegmentation(ref, out);

    const int N = 3;
    for (int i = 0; i < N; i++)
    {
        net.setInput(inputBlob, "");
        Mat out = net.forward();
        normAssert(ref, out, "", 0.00044, /*target == DNN_TARGET_CPU ? 0.453 : */0.552);
        normAssertSegmentation(ref, out);
    }
}
示例#18
0
// Check accuracy of style transfer models from https://github.com/jcjohnson/fast-neural-style
// th fast_neural_style.lua \
//   -input_image ~/opencv_extra/testdata/dnn/googlenet_1.png \
//   -output_image lena.png \
//   -median_filter 0 \
//   -image_size 0 \
//   -model models/eccv16/starry_night.t7
// th fast_neural_style.lua \
//   -input_image ~/opencv_extra/testdata/dnn/googlenet_1.png \
//   -output_image lena.png \
//   -median_filter 0 \
//   -image_size 0 \
//   -model models/instance_norm/feathers.t7
TEST_P(Test_Torch_nets, FastNeuralStyle_accuracy)
{
    checkBackend();

#if defined(INF_ENGINE_RELEASE)
#if INF_ENGINE_RELEASE <= 2018050000
    if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL)
        throw SkipTestException("");
#endif
#endif

    std::string models[] = {"dnn/fast_neural_style_eccv16_starry_night.t7",
                            "dnn/fast_neural_style_instance_norm_feathers.t7"};
    std::string targets[] = {"dnn/lena_starry_night.png", "dnn/lena_feathers.png"};

    for (int i = 0; i < 2; ++i)
    {
        const string model = findDataFile(models[i], false);
        Net net = readNetFromTorch(model);

        net.setPreferableBackend(backend);
        net.setPreferableTarget(target);

        Mat img = imread(findDataFile("dnn/googlenet_1.png", false));
        Mat inputBlob = blobFromImage(img, 1.0, Size(), Scalar(103.939, 116.779, 123.68), false);

        net.setInput(inputBlob);
        Mat out = net.forward();

        // Deprocessing.
        getPlane(out, 0, 0) += 103.939;
        getPlane(out, 0, 1) += 116.779;
        getPlane(out, 0, 2) += 123.68;
        out = cv::min(cv::max(0, out), 255);

        Mat ref = imread(findDataFile(targets[i]));
        Mat refBlob = blobFromImage(ref, 1.0, Size(), Scalar(), false);

        if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)
        {
            double normL1 = cvtest::norm(refBlob, out, cv::NORM_L1) / refBlob.total();
            if (target == DNN_TARGET_MYRIAD)
                EXPECT_LE(normL1, 4.0f);
            else
                EXPECT_LE(normL1, 0.6f);
        }
        else
            normAssert(out, refBlob, "", 0.5, 1.1);
    }
}
示例#19
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TEST(Test_TensorFlow, two_inputs)
{
    Net net = readNet(path("two_inputs_net.pbtxt"));
    net.setPreferableBackend(DNN_BACKEND_OPENCV);

    Mat firstInput(2, 3, CV_32FC1), secondInput(2, 3, CV_32FC1);
    randu(firstInput, -1, 1);
    randu(secondInput, -1, 1);

    net.setInput(firstInput, "first_input");
    net.setInput(secondInput, "second_input");
    Mat out = net.forward();

    normAssert(out, firstInput + secondInput);
}
TEST(Reproducibility_DenseNet_121, Accuracy)
{
    const string proto = findDataFile("dnn/DenseNet_121.prototxt", false);
    const string model = findDataFile("dnn/DenseNet_121.caffemodel", false);

    Mat inp = imread(_tf("dog416.png"));
    inp = blobFromImage(inp, 1.0 / 255, Size(224, 224));
    Mat ref = blobFromNPY(_tf("densenet_121_output.npy"));

    Net net = readNetFromCaffe(proto, model);

    net.setInput(inp);
    Mat out = net.forward();

    normAssert(out, ref);
}
示例#21
0
    void testDarknetLayer(const std::string& name, bool hasWeights = false)
    {
        std::string cfg = findDataFile("dnn/darknet/" + name + ".cfg", false);
        std::string model = "";
        if (hasWeights)
            model = findDataFile("dnn/darknet/" + name + ".weights", false);
        Mat inp = blobFromNPY(findDataFile("dnn/darknet/" + name + "_in.npy", false));
        Mat ref = blobFromNPY(findDataFile("dnn/darknet/" + name + "_out.npy", false));

        checkBackend(&inp, &ref);

        Net net = readNet(cfg, model);
        net.setPreferableBackend(backend);
        net.setPreferableTarget(target);
        net.setInput(inp);
        Mat out = net.forward();
        normAssert(out, ref, "", default_l1, default_lInf);
    }
TEST(Reproducibility_AlexNet_fp16, Accuracy)
{
    const float l1 = 1e-5;
    const float lInf = 3e-3;

    const string proto = findDataFile("dnn/bvlc_alexnet.prototxt", false);
    const string model = findDataFile("dnn/bvlc_alexnet.caffemodel", false);

    shrinkCaffeModel(model, "bvlc_alexnet.caffemodel_fp16");
    Net net = readNetFromCaffe(proto, "bvlc_alexnet.caffemodel_fp16");

    Mat sample = imread(findDataFile("dnn/grace_hopper_227.png", false));

    net.setInput(blobFromImage(sample, 1.0f, Size(227, 227), Scalar(), false));
    Mat out = net.forward();
    Mat ref = blobFromNPY(findDataFile("dnn/caffe_alexnet_prob.npy", false));
    normAssert(ref, out, "", l1, lInf);
}
    void testONNXModels(const String& basename, const Extension ext = npy,
                        const double l1 = 0, const float lInf = 0, const bool useSoftmax = false,
                        bool checkNoFallbacks = true)
    {
        String onnxmodel = _tf("models/" + basename + ".onnx");
        Mat inp, ref;
        if (ext == npy) {
            inp = blobFromNPY(_tf("data/input_" + basename + ".npy"));
            ref = blobFromNPY(_tf("data/output_" + basename + ".npy"));
        }
        else if (ext == pb) {
            inp = readTensorFromONNX(_tf("data/input_" + basename + ".pb"));
            ref = readTensorFromONNX(_tf("data/output_" + basename + ".pb"));
        }
        else
            CV_Error(Error::StsUnsupportedFormat, "Unsupported extension");

        checkBackend(&inp, &ref);
        Net net = readNetFromONNX(onnxmodel);
        ASSERT_FALSE(net.empty());

        net.setPreferableBackend(backend);
        net.setPreferableTarget(target);

        net.setInput(inp);
        Mat out = net.forward("");

        if (useSoftmax)
        {
            LayerParams lp;
            Net netSoftmax;
            netSoftmax.addLayerToPrev("softmaxLayer", "SoftMax", lp);
            netSoftmax.setPreferableBackend(DNN_BACKEND_OPENCV);

            netSoftmax.setInput(out);
            out = netSoftmax.forward();

            netSoftmax.setInput(ref);
            ref = netSoftmax.forward();
        }
        normAssert(ref, out, "", l1 ? l1 : default_l1, lInf ? lInf : default_lInf);
        if (checkNoFallbacks)
            expectNoFallbacksFromIE(net);
    }
示例#24
0
TEST(Test_TensorFlow, inception_accuracy)
{
    Net net;
    {
        const string model = findDataFile("dnn/tensorflow_inception_graph.pb", false);
        net = readNetFromTensorflow(model);
        ASSERT_FALSE(net.empty());
    }
    net.setPreferableBackend(DNN_BACKEND_OPENCV);

    Mat sample = imread(_tf("grace_hopper_227.png"));
    ASSERT_TRUE(!sample.empty());
    Mat inputBlob = blobFromImage(sample, 1.0, Size(224, 224), Scalar(), /*swapRB*/true);

    net.setInput(inputBlob, "input");
    Mat out = net.forward("softmax2");

    Mat ref = blobFromNPY(_tf("tf_inception_prob.npy"));

    normAssert(ref, out);
}
示例#25
0
TEST(Test_TensorFlow, inception_accuracy)
{
    Net net;
    {
        const string model = findDataFile("dnn/tensorflow_inception_graph.pb", false);
        net = readNetFromTensorflow(model);
        ASSERT_FALSE(net.empty());
    }

    Mat sample = imread(_tf("grace_hopper_227.png"));
    ASSERT_TRUE(!sample.empty());
    resize(sample, sample, Size(224, 224));
    Mat inputBlob = blobFromImage(sample);

    net.setInput(inputBlob, "input");
    Mat out = net.forward("softmax2");

    Mat ref = blobFromNPY(_tf("tf_inception_prob.npy"));

    normAssert(ref, out);
}
// https://github.com/richzhang/colorization
TEST(Reproducibility_Colorization, Accuracy)
{
    const float l1 = 3e-5;
    const float lInf = 3e-3;

    Mat inp = blobFromNPY(_tf("colorization_inp.npy"));
    Mat ref = blobFromNPY(_tf("colorization_out.npy"));
    Mat kernel = blobFromNPY(_tf("colorization_pts_in_hull.npy"));

    const string proto = findDataFile("dnn/colorization_deploy_v2.prototxt", false);
    const string model = findDataFile("dnn/colorization_release_v2.caffemodel", false);
    Net net = readNetFromCaffe(proto, model);

    net.getLayer(net.getLayerId("class8_ab"))->blobs.push_back(kernel);
    net.getLayer(net.getLayerId("conv8_313_rh"))->blobs.push_back(Mat(1, 313, CV_32F, 2.606));

    net.setInput(inp);
    Mat out = net.forward();

    normAssert(out, ref, "", l1, lInf);
}
TEST_P(Test_Torch_nets, OpenFace_accuracy)
{
    const string model = findDataFile("dnn/openface_nn4.small2.v1.t7", false);
    Net net = readNetFromTorch(model);

    net.setPreferableTarget(GetParam());

    Mat sample = imread(findDataFile("cv/shared/lena.png", false));
    Mat sampleF32(sample.size(), CV_32FC3);
    sample.convertTo(sampleF32, sampleF32.type());
    sampleF32 /= 255;
    resize(sampleF32, sampleF32, Size(96, 96), 0, 0, INTER_NEAREST);

    Mat inputBlob = blobFromImage(sampleF32);

    net.setInput(inputBlob);
    Mat out = net.forward();

    Mat outRef = readTorchBlob(_tf("net_openface_output.dat"), true);
    normAssert(out, outRef);
}
TEST_P(Test_ONNX_nets, Alexnet)
{
    applyTestTag(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB);
    const String model =  _tf("models/alexnet.onnx");

    Net net = readNetFromONNX(model);
    ASSERT_FALSE(net.empty());

    net.setPreferableBackend(backend);
    net.setPreferableTarget(target);

    Mat inp = imread(_tf("../grace_hopper_227.png"));
    Mat ref = blobFromNPY(_tf("../caffe_alexnet_prob.npy"));
    checkBackend(&inp, &ref);

    net.setInput(blobFromImage(inp, 1.0f, Size(227, 227), Scalar(), false));
    ASSERT_FALSE(net.empty());
    Mat out = net.forward();

    normAssert(out, ref, "", default_l1,  default_lInf);
    expectNoFallbacksFromIE(net);
}
TEST_P(Reproducibility_MobileNet_SSD, Accuracy)
{
    const string proto = findDataFile("dnn/MobileNetSSD_deploy.prototxt", false);
    const string model = findDataFile("dnn/MobileNetSSD_deploy.caffemodel", false);
    Net net = readNetFromCaffe(proto, model);
    int targetId = GetParam();
    const float l1 = (targetId == DNN_TARGET_OPENCL_FP16) ? 1.5e-4 : 1e-5;
    const float lInf = (targetId == DNN_TARGET_OPENCL_FP16) ? 4e-4 : 1e-4;

    net.setPreferableTarget(targetId);

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

    Mat inp = blobFromImage(sample, 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), false);
    net.setInput(inp);
    Mat out = net.forward();

    const float scores_diff = (targetId == DNN_TARGET_OPENCL_FP16) ? 4e-4 : 1e-5;
    const float boxes_iou_diff = (targetId == DNN_TARGET_OPENCL_FP16) ? 5e-3 : 1e-4;
    Mat ref = blobFromNPY(_tf("mobilenet_ssd_caffe_out.npy"));
    normAssertDetections(ref, out, "", 0.0, scores_diff, boxes_iou_diff);

    // Check that detections aren't preserved.
    inp.setTo(0.0f);
    net.setInput(inp);
    out = net.forward();
    out = out.reshape(1, out.total() / 7);

    const int numDetections = out.rows;
    ASSERT_NE(numDetections, 0);
    for (int i = 0; i < numDetections; ++i)
    {
        float confidence = out.ptr<float>(i)[2];
        ASSERT_EQ(confidence, 0);
    }

    // Check batching mode.
    ref = ref.reshape(1, numDetections);
    inp = blobFromImages(std::vector<Mat>(2, sample), 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), false);
    net.setInput(inp);
    Mat outBatch = net.forward();

    // Output blob has a shape 1x1x2Nx7 where N is a number of detection for
    // a single sample in batch. The first numbers of detection vectors are batch id.
    outBatch = outBatch.reshape(1, outBatch.total() / 7);
    EXPECT_EQ(outBatch.rows, 2 * numDetections);
    normAssert(outBatch.rowRange(0, numDetections), ref, "", l1, lInf);
    normAssert(outBatch.rowRange(numDetections, 2 * numDetections).colRange(1, 7), ref.colRange(1, 7),
               "", l1, lInf);
}
TEST_P(Test_ONNX_nets, Googlenet)
{
    if (backend == DNN_BACKEND_INFERENCE_ENGINE)
        throw SkipTestException("");

    const String model = _tf("models/googlenet.onnx");

    Net net = readNetFromONNX(model);
    ASSERT_FALSE(net.empty());

    net.setPreferableBackend(backend);
    net.setPreferableTarget(target);

    std::vector<Mat> images;
    images.push_back( imread(_tf("../googlenet_0.png")) );
    images.push_back( imread(_tf("../googlenet_1.png")) );
    Mat inp = blobFromImages(images, 1.0f, Size(), Scalar(), false);
    Mat ref = blobFromNPY(_tf("../googlenet_prob.npy"));
    checkBackend(&inp, &ref);

    net.setInput(inp);
    ASSERT_FALSE(net.empty());
    Mat out = net.forward();

    normAssert(ref, out, "", default_l1,  default_lInf);
    expectNoFallbacksFromIE(net);
}