runTensorFlowNet("l2_normalize_3d");
}
-typedef testing::TestWithParam<Target> Test_TensorFlow_nets;
+class Test_TensorFlow_nets : public DNNTestLayer {};
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);
outNames[1] = "concat_1";
outNames[2] = "detection_out";
- std::vector<Mat> target(outNames.size());
+ 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);
- target[i] = blobFromNPY(path);
+ refs[i] = blobFromNPY(path);
}
Net net = readNetFromTensorflow(netPath, netConfig);
- net.setPreferableBackend(DNN_BACKEND_OPENCV);
- net.setPreferableTarget(GetParam());
+ net.setPreferableBackend(backend);
+ net.setPreferableTarget(target);
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);
- normAssertDetections(target[2], output[2], "", 0.2);
+ 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);
}
TEST_P(Test_TensorFlow_nets, Inception_v2_SSD)
{
+ checkBackend();
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);
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.setPreferableBackend(DNN_BACKEND_OPENCV);
- net.setPreferableTarget(GetParam());
+ net.setPreferableBackend(backend);
+ net.setPreferableTarget(target);
net.setInput(blob);
// Output has shape 1x1xNx7 where N - number of detections.
0, 3, 0.75838411, 0.44668293, 0.45907149, 0.49459291, 0.52197015,
0, 10, 0.95932811, 0.38349164, 0.32528657, 0.40387636, 0.39165527,
0, 10, 0.93973452, 0.66561931, 0.37841269, 0.68074018, 0.42907384);
- normAssertDetections(ref, out, "", 0.5);
+ double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 5e-3 : default_l1;
+ double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.025 : default_lInf;
+ normAssertDetections(ref, out, "", 0.5, scoreDiff, iouDiff);
}
TEST_P(Test_TensorFlow_nets, Inception_v2_Faster_RCNN)
{
+ checkBackend();
+ if (backend == DNN_BACKEND_INFERENCE_ENGINE ||
+ (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16))
+ throw SkipTestException("");
+
std::string proto = findDataFile("dnn/faster_rcnn_inception_v2_coco_2018_01_28.pbtxt", false);
std::string model = findDataFile("dnn/faster_rcnn_inception_v2_coco_2018_01_28.pb", false);
Net net = readNetFromTensorflow(model, proto);
- net.setPreferableBackend(DNN_BACKEND_OPENCV);
+ net.setPreferableBackend(backend);
+ net.setPreferableTarget(target);
Mat img = imread(findDataFile("dnn/dog416.png", false));
Mat blob = blobFromImage(img, 1.0f / 127.5, Size(800, 600), Scalar(127.5, 127.5, 127.5), true, false);
TEST_P(Test_TensorFlow_nets, opencv_face_detector_uint8)
{
+ checkBackend();
+ if (backend == DNN_BACKEND_INFERENCE_ENGINE &&
+ (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD))
+ throw SkipTestException("");
+
std::string proto = findDataFile("dnn/opencv_face_detector.pbtxt", false);
std::string model = findDataFile("dnn/opencv_face_detector_uint8.pb", false);
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.setPreferableBackend(DNN_BACKEND_OPENCV);
- net.setPreferableTarget(GetParam());
-
+ net.setPreferableBackend(backend);
+ net.setPreferableTarget(target);
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]
0, 1, 0.98977017, 0.23901358, 0.09084064, 0.29902688, 0.1769477,
0, 1, 0.97203469, 0.67965847, 0.06876482, 0.73999709, 0.1513494,
0, 1, 0.95097077, 0.51901293, 0.45863652, 0.5777427, 0.5347801);
- normAssertDetections(ref, out, "", 0.9, 3.4e-3, 1e-2);
+ double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 4e-3 : 3.4e-3;
+ double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.017 : 1e-2;
+ normAssertDetections(ref, out, "", 0.9, scoreDiff, iouDiff);
}
// inp = cv.imread('opencv_extra/testdata/cv/ximgproc/sources/08.png')
// np.save('east_text_detection.geometry.npy', geometry)
TEST_P(Test_TensorFlow_nets, EAST_text_detection)
{
+ checkBackend();
+ if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)
+ throw SkipTestException("");
+
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);
Net net = readNet(findDataFile("dnn/frozen_east_text_detection.pb", false));
- net.setPreferableTarget(GetParam());
+ 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);
normAssert(geometry, blobFromNPY(refGeometryPath), "geometry", 1e-4, 3e-3);
}
-INSTANTIATE_TEST_CASE_P(/**/, Test_TensorFlow_nets, availableDnnTargets());
+INSTANTIATE_TEST_CASE_P(/**/, Test_TensorFlow_nets, dnnBackendsAndTargets());
TEST_P(Test_TensorFlow_layers, fp16_weights)
{