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Fix for opencv face detector ocl test

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Signed-off-by: Li Peng <peng.li@intel.com>
This commit is contained in:
Li Peng 2018-02-22 21:50:43 +08:00
parent c524f669c7
commit e7d35d51fa
3 changed files with 27 additions and 10 deletions
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12
modules/dnn/src/layers/normalize_bbox_layer.cpp
View File

@ -105,6 +105,18 @@ public:
float norm = pow(absSum, 1.0f / pnorm); float norm = pow(absSum, 1.0f / pnorm);
multiply(src, 1.0f / norm, dst); multiply(src, 1.0f / norm, dst);
} }
else
{
Mat norm;
reduce(buffer, norm, 0, REDUCE_SUM);
norm += epsilon;
// compute inverted norm to call multiply instead divide
cv::pow(norm, -1.0f / pnorm, norm);
repeat(norm, channels, 1, buffer);
multiply(src, buffer, dst);
}
if (!blobs.empty()) if (!blobs.empty())
{ {

4
modules/dnn/test/test_backends.cpp
View File

@ -222,9 +222,7 @@ TEST_P(DNNTestNetwork, OpenFace)
TEST_P(DNNTestNetwork, opencv_face_detector) TEST_P(DNNTestNetwork, opencv_face_detector)
{ {
if (backend == DNN_BACKEND_HALIDE || if (backend == DNN_BACKEND_HALIDE) throw SkipTestException("");
backend == DNN_BACKEND_DEFAULT && target == DNN_TARGET_OPENCL)
throw SkipTestException("");
Mat img = imread(findDataFile("gpu/lbpcascade/er.png", false)); Mat img = imread(findDataFile("gpu/lbpcascade/er.png", false));
Mat inp = blobFromImage(img, 1.0, Size(), Scalar(104.0, 177.0, 123.0), false, false); Mat inp = blobFromImage(img, 1.0, Size(), Scalar(104.0, 177.0, 123.0), false, false);
processNet("dnn/opencv_face_detector.caffemodel", "dnn/opencv_face_detector.prototxt", processNet("dnn/opencv_face_detector.caffemodel", "dnn/opencv_face_detector.prototxt",

21
modules/dnn/test/test_caffe_importer.cpp
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@ -456,16 +456,21 @@ TEST(Test_Caffe, multiple_inputs)
normAssert(out, first_image + second_image); normAssert(out, first_image + second_image);
} }
typedef testing::TestWithParam<std::string> opencv_face_detector; CV_ENUM(DNNTarget, DNN_TARGET_CPU, DNN_TARGET_OPENCL)
typedef testing::TestWithParam<tuple<std::string, DNNTarget> > opencv_face_detector;
TEST_P(opencv_face_detector, Accuracy) TEST_P(opencv_face_detector, Accuracy)
{ {
std::string proto = findDataFile("dnn/opencv_face_detector.prototxt", false); std::string proto = findDataFile("dnn/opencv_face_detector.prototxt", false);
std::string model = findDataFile(GetParam(), false); std::string model = findDataFile(get<0>(GetParam()), false);
dnn::Target targetId = (dnn::Target)(int)get<1>(GetParam());
Net net = readNetFromCaffe(proto, model); Net net = readNetFromCaffe(proto, model);
Mat img = imread(findDataFile("gpu/lbpcascade/er.png", 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); Mat blob = blobFromImage(img, 1.0, Size(), Scalar(104.0, 177.0, 123.0), false, false);
net.setPreferableBackend(DNN_BACKEND_DEFAULT);
net.setPreferableTarget(targetId);
net.setInput(blob); net.setInput(blob);
// Output has shape 1x1xNx7 where N - number of detections. // Output has shape 1x1xNx7 where N - number of detections.
// An every detection is a vector of values [id, classId, confidence, left, top, right, bottom] // An every detection is a vector of values [id, classId, confidence, left, top, right, bottom]
@ -479,11 +484,13 @@ TEST_P(opencv_face_detector, Accuracy)
0.95097077, 0.51901293, 0.45863652, 0.5777427, 0.5347801); 0.95097077, 0.51901293, 0.45863652, 0.5777427, 0.5347801);
normAssert(out.reshape(1, out.total() / 7).rowRange(0, 6).colRange(2, 7), ref); normAssert(out.reshape(1, out.total() / 7).rowRange(0, 6).colRange(2, 7), ref);
} }
INSTANTIATE_TEST_CASE_P(Test_Caffe, opencv_face_detector, Values( INSTANTIATE_TEST_CASE_P(Test_Caffe, opencv_face_detector,
"dnn/opencv_face_detector.caffemodel", Combine(
"dnn/opencv_face_detector_fp16.caffemodel" Values("dnn/opencv_face_detector.caffemodel",
)); "dnn/opencv_face_detector_fp16.caffemodel"),
Values(DNN_TARGET_CPU, DNN_TARGET_OPENCL)
)
);
TEST(Test_Caffe, FasterRCNN_and_RFCN) TEST(Test_Caffe, FasterRCNN_and_RFCN)
{ {