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Merge pull request #12061 from alalek:dnn_test_skip_checks_only

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pull/12046/head
Alexander Alekhin 7 years ago
parent 9137e2d635 dd8701c1a0
commit 2f0fc920dd
1 changed files with 40 additions and 34 deletions
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  1. 74
      modules/dnn/test/test_halide_layers.cpp

74
modules/dnn/test/test_halide_layers.cpp
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@ -16,7 +16,7 @@ using namespace cv;
using namespace cv::dnn;
using namespace testing;
static void test(Mat& input, Net& net, int backendId, int 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);
@ -29,16 +29,19 @@ static void test(Mat& input, Net& net, int backendId, int targetId)
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, int backendId, int 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()
@ -101,16 +104,17 @@ TEST_P(Convolution, Accuracy)
Size pad = get<4>(GetParam());
Size dilation = get<5>(GetParam());
bool hasBias = get<6>(GetParam());
int backendId = get<0>(get<7>(GetParam()));
int targetId = get<1>(get<7>(GetParam()));
Backend backendId = get<0>(get<7>(GetParam()));
Target targetId = get<1>(get<7>(GetParam()));
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);
@ -139,7 +143,9 @@ TEST_P(Convolution, Accuracy)
}
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(
@ -171,8 +177,8 @@ TEST_P(Deconvolution, Accuracy)
Size stride = Size(get<5>(GetParam())[0], get<5>(GetParam())[1]);
Size adjPad = Size(get<5>(GetParam())[2], get<5>(GetParam())[3]);
bool hasBias = get<6>(GetParam());
int backendId = get<0>(get<7>(GetParam()));
int targetId = get<1>(get<7>(GetParam()));
Backend backendId = get<0>(get<7>(GetParam()));
Target targetId = get<1>(get<7>(GetParam()));
if (backendId == DNN_BACKEND_INFERENCE_ENGINE && targetId == DNN_TARGET_CPU &&
dilation.width == 2 && dilation.height == 2)
throw SkipTestException("");
@ -235,8 +241,8 @@ TEST_P(LRN, Accuracy)
float bias = get<2>(GetParam())[2];
bool normBySize = get<3>(GetParam());
std::string nrmType = get<4>(GetParam());
int backendId = get<0>(get<5>(GetParam()));
int targetId = get<1>(get<5>(GetParam()));
Backend backendId = get<0>(get<5>(GetParam()));
Target targetId = get<1>(get<5>(GetParam()));
if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
throw SkipTestException("");
@ -276,8 +282,8 @@ TEST_P(AvePooling, Accuracy)
Size outSize = get<1>(GetParam());; // Input size will be computed from parameters.
Size kernel = get<2>(GetParam());
Size stride = get<3>(GetParam());
int backendId = get<0>(get<4>(GetParam()));
int targetId = get<1>(get<4>(GetParam()));
Backend backendId = get<0>(get<4>(GetParam()));
Target targetId = get<1>(get<4>(GetParam()));
if (backendId == DNN_BACKEND_INFERENCE_ENGINE && targetId == DNN_TARGET_MYRIAD)
throw SkipTestException("");
@ -317,8 +323,8 @@ TEST_P(MaxPooling, Accuracy)
Size kernel = get<2>(GetParam());
Size stride = get<3>(GetParam());
Size pad = get<4>(GetParam());
int backendId = get<0>(get<5>(GetParam()));
int targetId = get<1>(get<5>(GetParam()));
Backend backendId = get<0>(get<5>(GetParam()));
Target targetId = get<1>(get<5>(GetParam()));
LayerParams lp;
lp.set("pool", "max");
@ -355,8 +361,8 @@ TEST_P(FullyConnected, Accuracy)
Size inSize = get<1>(GetParam());
int outChannels = get<2>(GetParam());
bool hasBias = get<3>(GetParam());
int backendId = get<0>(get<4>(GetParam()));
int targetId = get<1>(get<4>(GetParam()));
Backend backendId = get<0>(get<4>(GetParam()));
Target targetId = get<1>(get<4>(GetParam()));
if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
throw SkipTestException("");
@ -394,8 +400,8 @@ typedef TestWithParam<tuple<int, tuple<Backend, Target> > > SoftMax;
TEST_P(SoftMax, Accuracy)
{
int inChannels = get<0>(GetParam());
int backendId = get<0>(get<1>(GetParam()));
int targetId = get<1>(get<1>(GetParam()));
Backend backendId = get<0>(get<1>(GetParam()));
Target targetId = get<1>(get<1>(GetParam()));
LayerParams lp;
lp.type = "SoftMax";
lp.name = "testLayer";
@ -457,7 +463,7 @@ TEST_P(Test_Halide_layers, MaxPoolUnpool)
////////////////////////////////////////////////////////////////////////////////
static const int kNumChannels = 3;
void testInPlaceActivation(LayerParams& lp, int backendId, int targetId)
void testInPlaceActivation(LayerParams& lp, Backend backendId, Target targetId)
{
EXPECT_FALSE(lp.name.empty());
@ -485,8 +491,8 @@ TEST_P(BatchNorm, Accuracy)
bool hasWeights = get<0>(GetParam());
bool hasBias = get<1>(GetParam());
float epsilon = get<2>(GetParam());
int backendId = get<0>(get<3>(GetParam()));
int targetId = get<1>(get<3>(GetParam()));
Backend backendId = get<0>(get<3>(GetParam()));
Target targetId = get<1>(get<3>(GetParam()));
LayerParams lp;
lp.set("has_weight", hasWeights);
@ -518,8 +524,8 @@ typedef TestWithParam<tuple<float, tuple<Backend, Target> > > ReLU;
TEST_P(ReLU, Accuracy)
{
float negativeSlope = get<0>(GetParam());
int backendId = get<0>(get<1>(GetParam()));
int targetId = get<1>(get<1>(GetParam()));
Backend backendId = get<0>(get<1>(GetParam()));
Target targetId = get<1>(get<1>(GetParam()));
LayerParams lp;
lp.set("negative_slope", negativeSlope);
@ -536,8 +542,8 @@ INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, ReLU, Combine(
typedef TestWithParam<tuple<std::string, tuple<Backend, Target> > > NoParamActivation;
TEST_P(NoParamActivation, Accuracy)
{
int backendId = get<0>(get<1>(GetParam()));
int targetId = get<1>(get<1>(GetParam()));
Backend backendId = get<0>(get<1>(GetParam()));
Target targetId = get<1>(get<1>(GetParam()));
LayerParams lp;
lp.type = get<0>(GetParam());
@ -555,8 +561,8 @@ TEST_P(Power, Accuracy)
float power = get<0>(GetParam())[0];
float scale = get<0>(GetParam())[1];
float shift = get<0>(GetParam())[2];
int backendId = get<0>(get<1>(GetParam()));
int targetId = get<1>(get<1>(GetParam()));
Backend backendId = get<0>(get<1>(GetParam()));
Target targetId = get<1>(get<1>(GetParam()));
LayerParams lp;
lp.set("power", power);
@ -589,8 +595,8 @@ typedef TestWithParam<tuple<bool, tuple<Backend, Target> > > Scale;
TEST_P(Scale, Accuracy)
{
bool hasBias = get<0>(GetParam());
int backendId = get<0>(get<1>(GetParam()));
int targetId = get<1>(get<1>(GetParam()));
Backend backendId = get<0>(get<1>(GetParam()));
Target targetId = get<1>(get<1>(GetParam()));
LayerParams lp;
lp.set("bias_term", hasBias);
@ -624,8 +630,8 @@ TEST_P(Concat, Accuracy)
{
Vec3i inSize = get<0>(GetParam());
Vec3i numChannels = get<1>(GetParam());
int backendId = get<0>(get<2>(GetParam()));
int targetId = get<1>(get<2>(GetParam()));
Backend backendId = get<0>(get<2>(GetParam()));
Target targetId = get<1>(get<2>(GetParam()));
Net net;
@ -692,8 +698,8 @@ TEST_P(Eltwise, Accuracy)
std::string op = get<1>(GetParam());
int numConv = get<2>(GetParam());
bool weighted = get<3>(GetParam());
int backendId = get<0>(get<4>(GetParam()));
int targetId = get<1>(get<4>(GetParam()));
Backend backendId = get<0>(get<4>(GetParam()));
Target targetId = get<1>(get<4>(GetParam()));
Net net;

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