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Merge pull request #12061 from alalek:dnn_test_skip_checks_only

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This commit is contained in:
Alexander Alekhin 2018-07-26 15:15:32 +00:00
commit 2f0fc920dd
1 changed files with 40 additions and 34 deletions
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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 cv::dnn;
using namespace testing; 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); DNNTestLayer::checkBackend(backendId, targetId);
randu(input, -1.0f, 1.0f); randu(input, -1.0f, 1.0f);
@ -29,16 +29,19 @@ static void test(Mat& input, Net& net, int backendId, int targetId)
net.setPreferableTarget(targetId); net.setPreferableTarget(targetId);
Mat outputHalide = net.forward().clone(); Mat outputHalide = net.forward().clone();
if (skipCheck)
return;
double l1, lInf; double l1, lInf;
DNNTestLayer::getDefaultThresholds(backendId, targetId, &l1, &lInf); DNNTestLayer::getDefaultThresholds(backendId, targetId, &l1, &lInf);
normAssert(outputDefault, outputHalide, "", 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 net;
net.addLayerToPrev(params.name, params.type, params); 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() static testing::internal::ParamGenerator<tuple<Backend, Target> > dnnBackendsAndTargetsWithHalide()
@ -101,16 +104,17 @@ TEST_P(Convolution, Accuracy)
Size pad = get<4>(GetParam()); Size pad = get<4>(GetParam());
Size dilation = get<5>(GetParam()); Size dilation = get<5>(GetParam());
bool hasBias = get<6>(GetParam()); bool hasBias = get<6>(GetParam());
int backendId = get<0>(get<7>(GetParam())); Backend backendId = get<0>(get<7>(GetParam()));
int targetId = get<1>(get<7>(GetParam())); Target targetId = get<1>(get<7>(GetParam()));
if (backendId == DNN_BACKEND_INFERENCE_ENGINE && targetId == DNN_TARGET_MYRIAD) if (backendId == DNN_BACKEND_INFERENCE_ENGINE && targetId == DNN_TARGET_MYRIAD)
throw SkipTestException(""); throw SkipTestException("");
bool skipCheck = false;
if (cvtest::skipUnstableTests && backendId == DNN_BACKEND_OPENCV && if (cvtest::skipUnstableTests && backendId == DNN_BACKEND_OPENCV &&
(targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16) && (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16) &&
kernel == Size(3, 1) && stride == Size(1, 1) && pad == Size(0, 1)) 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}; int sz[] = {outChannels, inChannels / group, kernel.height, kernel.width};
Mat weights(4, &sz[0], CV_32F); Mat weights(4, &sz[0], CV_32F);
@ -139,7 +143,9 @@ TEST_P(Convolution, Accuracy)
} }
int inpSz[] = {1, inChannels, inSize.height, inSize.width}; int inpSz[] = {1, inChannels, inSize.height, inSize.width};
Mat input(4, &inpSz[0], CV_32F); 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( 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 stride = Size(get<5>(GetParam())[0], get<5>(GetParam())[1]);
Size adjPad = Size(get<5>(GetParam())[2], get<5>(GetParam())[3]); Size adjPad = Size(get<5>(GetParam())[2], get<5>(GetParam())[3]);
bool hasBias = get<6>(GetParam()); bool hasBias = get<6>(GetParam());
int backendId = get<0>(get<7>(GetParam())); Backend backendId = get<0>(get<7>(GetParam()));
int targetId = get<1>(get<7>(GetParam())); Target targetId = get<1>(get<7>(GetParam()));
if (backendId == DNN_BACKEND_INFERENCE_ENGINE && targetId == DNN_TARGET_CPU && if (backendId == DNN_BACKEND_INFERENCE_ENGINE && targetId == DNN_TARGET_CPU &&
dilation.width == 2 && dilation.height == 2) dilation.width == 2 && dilation.height == 2)
throw SkipTestException(""); throw SkipTestException("");
@ -235,8 +241,8 @@ TEST_P(LRN, Accuracy)
float bias = get<2>(GetParam())[2]; float bias = get<2>(GetParam())[2];
bool normBySize = get<3>(GetParam()); bool normBySize = get<3>(GetParam());
std::string nrmType = get<4>(GetParam()); std::string nrmType = get<4>(GetParam());
int backendId = get<0>(get<5>(GetParam())); Backend backendId = get<0>(get<5>(GetParam()));
int targetId = get<1>(get<5>(GetParam())); Target targetId = get<1>(get<5>(GetParam()));
if (backendId == DNN_BACKEND_INFERENCE_ENGINE) if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
throw SkipTestException(""); throw SkipTestException("");
@ -276,8 +282,8 @@ TEST_P(AvePooling, Accuracy)
Size outSize = get<1>(GetParam());; // Input size will be computed from parameters. Size outSize = get<1>(GetParam());; // Input size will be computed from parameters.
Size kernel = get<2>(GetParam()); Size kernel = get<2>(GetParam());
Size stride = get<3>(GetParam()); Size stride = get<3>(GetParam());
int backendId = get<0>(get<4>(GetParam())); Backend backendId = get<0>(get<4>(GetParam()));
int targetId = get<1>(get<4>(GetParam())); Target targetId = get<1>(get<4>(GetParam()));
if (backendId == DNN_BACKEND_INFERENCE_ENGINE && targetId == DNN_TARGET_MYRIAD) if (backendId == DNN_BACKEND_INFERENCE_ENGINE && targetId == DNN_TARGET_MYRIAD)
throw SkipTestException(""); throw SkipTestException("");
@ -317,8 +323,8 @@ TEST_P(MaxPooling, Accuracy)
Size kernel = get<2>(GetParam()); Size kernel = get<2>(GetParam());
Size stride = get<3>(GetParam()); Size stride = get<3>(GetParam());
Size pad = get<4>(GetParam()); Size pad = get<4>(GetParam());
int backendId = get<0>(get<5>(GetParam())); Backend backendId = get<0>(get<5>(GetParam()));
int targetId = get<1>(get<5>(GetParam())); Target targetId = get<1>(get<5>(GetParam()));
LayerParams lp; LayerParams lp;
lp.set("pool", "max"); lp.set("pool", "max");
@ -355,8 +361,8 @@ TEST_P(FullyConnected, Accuracy)
Size inSize = get<1>(GetParam()); Size inSize = get<1>(GetParam());
int outChannels = get<2>(GetParam()); int outChannels = get<2>(GetParam());
bool hasBias = get<3>(GetParam()); bool hasBias = get<3>(GetParam());
int backendId = get<0>(get<4>(GetParam())); Backend backendId = get<0>(get<4>(GetParam()));
int targetId = get<1>(get<4>(GetParam())); Target targetId = get<1>(get<4>(GetParam()));
if (backendId == DNN_BACKEND_INFERENCE_ENGINE) if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
throw SkipTestException(""); throw SkipTestException("");
@ -394,8 +400,8 @@ typedef TestWithParam<tuple<int, tuple<Backend, Target> > > SoftMax;
TEST_P(SoftMax, Accuracy) TEST_P(SoftMax, Accuracy)
{ {
int inChannels = get<0>(GetParam()); int inChannels = get<0>(GetParam());
int backendId = get<0>(get<1>(GetParam())); Backend backendId = get<0>(get<1>(GetParam()));
int targetId = get<1>(get<1>(GetParam())); Target targetId = get<1>(get<1>(GetParam()));
LayerParams lp; LayerParams lp;
lp.type = "SoftMax"; lp.type = "SoftMax";
lp.name = "testLayer"; lp.name = "testLayer";
@ -457,7 +463,7 @@ TEST_P(Test_Halide_layers, MaxPoolUnpool)
//////////////////////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////////////////////////
static const int kNumChannels = 3; 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()); EXPECT_FALSE(lp.name.empty());
@ -485,8 +491,8 @@ TEST_P(BatchNorm, Accuracy)
bool hasWeights = get<0>(GetParam()); bool hasWeights = get<0>(GetParam());
bool hasBias = get<1>(GetParam()); bool hasBias = get<1>(GetParam());
float epsilon = get<2>(GetParam()); float epsilon = get<2>(GetParam());
int backendId = get<0>(get<3>(GetParam())); Backend backendId = get<0>(get<3>(GetParam()));
int targetId = get<1>(get<3>(GetParam())); Target targetId = get<1>(get<3>(GetParam()));
LayerParams lp; LayerParams lp;
lp.set("has_weight", hasWeights); lp.set("has_weight", hasWeights);
@ -518,8 +524,8 @@ typedef TestWithParam<tuple<float, tuple<Backend, Target> > > ReLU;
TEST_P(ReLU, Accuracy) TEST_P(ReLU, Accuracy)
{ {
float negativeSlope = get<0>(GetParam()); float negativeSlope = get<0>(GetParam());
int backendId = get<0>(get<1>(GetParam())); Backend backendId = get<0>(get<1>(GetParam()));
int targetId = get<1>(get<1>(GetParam())); Target targetId = get<1>(get<1>(GetParam()));
LayerParams lp; LayerParams lp;
lp.set("negative_slope", negativeSlope); 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; typedef TestWithParam<tuple<std::string, tuple<Backend, Target> > > NoParamActivation;
TEST_P(NoParamActivation, Accuracy) TEST_P(NoParamActivation, Accuracy)
{ {
int backendId = get<0>(get<1>(GetParam())); Backend backendId = get<0>(get<1>(GetParam()));
int targetId = get<1>(get<1>(GetParam())); Target targetId = get<1>(get<1>(GetParam()));
LayerParams lp; LayerParams lp;
lp.type = get<0>(GetParam()); lp.type = get<0>(GetParam());
@ -555,8 +561,8 @@ TEST_P(Power, Accuracy)
float power = get<0>(GetParam())[0]; float power = get<0>(GetParam())[0];
float scale = get<0>(GetParam())[1]; float scale = get<0>(GetParam())[1];
float shift = get<0>(GetParam())[2]; float shift = get<0>(GetParam())[2];
int backendId = get<0>(get<1>(GetParam())); Backend backendId = get<0>(get<1>(GetParam()));
int targetId = get<1>(get<1>(GetParam())); Target targetId = get<1>(get<1>(GetParam()));
LayerParams lp; LayerParams lp;
lp.set("power", power); lp.set("power", power);
@ -589,8 +595,8 @@ typedef TestWithParam<tuple<bool, tuple<Backend, Target> > > Scale;
TEST_P(Scale, Accuracy) TEST_P(Scale, Accuracy)
{ {
bool hasBias = get<0>(GetParam()); bool hasBias = get<0>(GetParam());
int backendId = get<0>(get<1>(GetParam())); Backend backendId = get<0>(get<1>(GetParam()));
int targetId = get<1>(get<1>(GetParam())); Target targetId = get<1>(get<1>(GetParam()));
LayerParams lp; LayerParams lp;
lp.set("bias_term", hasBias); lp.set("bias_term", hasBias);
@ -624,8 +630,8 @@ TEST_P(Concat, Accuracy)
{ {
Vec3i inSize = get<0>(GetParam()); Vec3i inSize = get<0>(GetParam());
Vec3i numChannels = get<1>(GetParam()); Vec3i numChannels = get<1>(GetParam());
int backendId = get<0>(get<2>(GetParam())); Backend backendId = get<0>(get<2>(GetParam()));
int targetId = get<1>(get<2>(GetParam())); Target targetId = get<1>(get<2>(GetParam()));
Net net; Net net;
@ -692,8 +698,8 @@ TEST_P(Eltwise, Accuracy)
std::string op = get<1>(GetParam()); std::string op = get<1>(GetParam());
int numConv = get<2>(GetParam()); int numConv = get<2>(GetParam());
bool weighted = get<3>(GetParam()); bool weighted = get<3>(GetParam());
int backendId = get<0>(get<4>(GetParam())); Backend backendId = get<0>(get<4>(GetParam()));
int targetId = get<1>(get<4>(GetParam())); Target targetId = get<1>(get<4>(GetParam()));
Net net; Net net;