using namespace cv::dnn;
using namespace testing;
-static void test(Mat& input, Net& net, Backend backendId, Target targetId)
+static void test(Mat& input, Net& net, Backend backendId, Target targetId, bool skipCheck = false)
{
DNNTestLayer::checkBackend(backendId, targetId);
randu(input, -1.0f, 1.0f);
net.setPreferableTarget(targetId);
Mat outputHalide = net.forward().clone();
+ if (skipCheck)
+ return;
+
double l1, lInf;
DNNTestLayer::getDefaultThresholds(backendId, targetId, &l1, &lInf);
normAssert(outputDefault, outputHalide, "", l1, lInf);
}
-static void test(LayerParams& params, Mat& input, Backend backendId, Target targetId)
+static void test(LayerParams& params, Mat& input, Backend backendId, Target targetId, bool skipCheck = false)
{
Net net;
net.addLayerToPrev(params.name, params.type, params);
- test(input, net, backendId, targetId);
+ test(input, net, backendId, targetId, skipCheck);
}
static testing::internal::ParamGenerator<tuple<Backend, Target> > dnnBackendsAndTargetsWithHalide()
if (backendId == DNN_BACKEND_INFERENCE_ENGINE && targetId == DNN_TARGET_MYRIAD)
throw SkipTestException("");
+ bool skipCheck = false;
if (cvtest::skipUnstableTests && backendId == DNN_BACKEND_OPENCV &&
(targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16) &&
kernel == Size(3, 1) && stride == Size(1, 1) && pad == Size(0, 1))
- throw SkipTestException("Skip unstable test");
+ skipCheck = true;
int sz[] = {outChannels, inChannels / group, kernel.height, kernel.width};
Mat weights(4, &sz[0], CV_32F);
}
int inpSz[] = {1, inChannels, inSize.height, inSize.width};
Mat input(4, &inpSz[0], CV_32F);
- test(lp, input, backendId, targetId);
+ test(lp, input, backendId, targetId, skipCheck);
+ if (skipCheck)
+ throw SkipTestException("Skip checks in unstable test");
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, Convolution, Combine(