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dnn: reduce set of ignored warnings

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This commit is contained in:
Alexander Alekhin
2018-11-14 20:25:23 +00:00
committed by Alexander Alekhin
parent 02d2cc58d7
commit 96c71dd3d2
42 changed files with 195 additions and 178 deletions
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modules/dnn/test/test_backends.cpp
+3 -3
View File
@@ -194,7 +194,7 @@ TEST_P(DNNTestNetwork, SSD_VGG16)
TEST_P(DNNTestNetwork, OpenPose_pose_coco)
{
if (backend == DNN_BACKEND_HALIDE ||
backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
(backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD))
throw SkipTestException("");
processNet("dnn/openpose_pose_coco.caffemodel", "dnn/openpose_pose_coco.prototxt",
Size(368, 368));
@@ -203,7 +203,7 @@ TEST_P(DNNTestNetwork, OpenPose_pose_coco)
TEST_P(DNNTestNetwork, OpenPose_pose_mpi)
{
if (backend == DNN_BACKEND_HALIDE ||
backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
(backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD))
throw SkipTestException("");
processNet("dnn/openpose_pose_mpi.caffemodel", "dnn/openpose_pose_mpi.prototxt",
Size(368, 368));
@@ -212,7 +212,7 @@ TEST_P(DNNTestNetwork, OpenPose_pose_mpi)
TEST_P(DNNTestNetwork, OpenPose_pose_mpi_faster_4_stages)
{
if (backend == DNN_BACKEND_HALIDE ||
backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
(backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD))
throw SkipTestException("");
// The same .caffemodel but modified .prototxt
// See https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/src/openpose/pose/poseParameters.cpp
modules/dnn/test/test_common.hpp
+103 -3
View File
@@ -56,7 +56,7 @@ static inline void PrintTo(const cv::dnn::Backend& v, std::ostream* os)
case DNN_BACKEND_INFERENCE_ENGINE: *os << "DLIE"; return;
case DNN_BACKEND_OPENCV: *os << "OCV"; return;
} // don't use "default:" to emit compiler warnings
*os << "DNN_BACKEND_UNKNOWN(" << v << ")";
*os << "DNN_BACKEND_UNKNOWN(" << (int)v << ")";
}
static inline void PrintTo(const cv::dnn::Target& v, std::ostream* os)
@@ -67,7 +67,7 @@ static inline void PrintTo(const cv::dnn::Target& v, std::ostream* os)
case DNN_TARGET_OPENCL_FP16: *os << "OCL_FP16"; return;
case DNN_TARGET_MYRIAD: *os << "MYRIAD"; return;
} // don't use "default:" to emit compiler warnings
*os << "DNN_TARGET_UNKNOWN(" << v << ")";
*os << "DNN_TARGET_UNKNOWN(" << (int)v << ")";
}
using opencv_test::tuple;
@@ -235,7 +235,8 @@ namespace opencv_test {
using namespace cv::dnn;
static testing::internal::ParamGenerator<tuple<Backend, Target> > dnnBackendsAndTargets(
static inline
testing::internal::ParamGenerator<tuple<Backend, Target> > dnnBackendsAndTargets(
bool withInferenceEngine = true,
bool withHalide = false,
bool withCpuOCV = true
@@ -283,4 +284,103 @@ static testing::internal::ParamGenerator<tuple<Backend, Target> > dnnBackendsAnd
} // namespace
namespace opencv_test {
using namespace cv::dnn;
static inline
testing::internal::ParamGenerator<Target> availableDnnTargets()
{
static std::vector<Target> targets;
if (targets.empty())
{
targets.push_back(DNN_TARGET_CPU);
#ifdef HAVE_OPENCL
if (cv::ocl::useOpenCL())
targets.push_back(DNN_TARGET_OPENCL);
#endif
}
return testing::ValuesIn(targets);
}
class DNNTestLayer : public TestWithParam<tuple<Backend, Target> >
{
public:
dnn::Backend backend;
dnn::Target target;
double default_l1, default_lInf;
DNNTestLayer()
{
backend = (dnn::Backend)(int)get<0>(GetParam());
target = (dnn::Target)(int)get<1>(GetParam());
getDefaultThresholds(backend, target, &default_l1, &default_lInf);
}
static void getDefaultThresholds(int backend, int target, double* l1, double* lInf)
{
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)
{
*l1 = 4e-3;
*lInf = 2e-2;
}
else
{
*l1 = 1e-5;
*lInf = 1e-4;
}
}
static void checkBackend(int backend, int target, Mat* inp = 0, Mat* ref = 0)
{
if (backend == DNN_BACKEND_OPENCV && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
{
#ifdef HAVE_OPENCL
if (!cv::ocl::useOpenCL())
#endif
{
throw SkipTestException("OpenCL is not available/disabled in OpenCV");
}
}
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
{
if (!checkMyriadTarget())
{
throw SkipTestException("Myriad is not available/disabled in OpenCV");
}
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE < 2018030000
if (inp && ref && inp->size[0] != 1)
{
// Myriad plugin supports only batch size 1. Slice a single sample.
if (inp->size[0] == ref->size[0])
{
std::vector<cv::Range> range(inp->dims, Range::all());
range[0] = Range(0, 1);
*inp = inp->operator()(range);
range = std::vector<cv::Range>(ref->dims, Range::all());
range[0] = Range(0, 1);
*ref = ref->operator()(range);
}
else
throw SkipTestException("Myriad plugin supports only batch size 1");
}
#else
if (inp && ref && inp->dims == 4 && ref->dims == 4 &&
inp->size[0] != 1 && inp->size[0] != ref->size[0])
throw SkipTestException("Inconsistent batch size of input and output blobs for Myriad plugin");
#endif
}
}
protected:
void checkBackend(Mat* inp = 0, Mat* ref = 0)
{
checkBackend(backend, target, inp, ref);
}
};
} // namespace
#endif
modules/dnn/test/test_layers.cpp
+2
View File
@@ -558,7 +558,9 @@ TEST_P(Test_Caffe_layers, FasterRCNN_Proposal)
normAssert(outs[i].rowRange(0, numDets), ref);
if (numDets < outs[i].size[0])
{
EXPECT_EQ(countNonZero(outs[i].rowRange(numDets, outs[i].size[0])), 0);
}
}
}
modules/dnn/test/test_misc.cpp
+3 -3
View File
@@ -140,9 +140,9 @@ TEST(LayerFactory, custom_layers)
net.setPreferableBackend(DNN_BACKEND_OPENCV);
Mat output = net.forward();
if (i == 0) EXPECT_EQ(output.at<float>(0), 1);
else if (i == 1) EXPECT_EQ(output.at<float>(0), 2);
else if (i == 2) EXPECT_EQ(output.at<float>(0), 1);
if (i == 0) { EXPECT_EQ(output.at<float>(0), 1); }
else if (i == 1) { EXPECT_EQ(output.at<float>(0), 2); }
else if (i == 2) { EXPECT_EQ(output.at<float>(0), 1); }
}
LayerFactory::unregisterLayer("CustomType");
}
modules/dnn/test/test_onnx_importer.cpp
+3 -3
View File
@@ -118,8 +118,8 @@ TEST_P(Test_ONNX_layers, Transpose)
TEST_P(Test_ONNX_layers, Multiplication)
{
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16 ||
backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
if ((backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16) ||
(backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD))
throw SkipTestException("");
testONNXModels("mul");
}
@@ -296,7 +296,7 @@ TEST_P(Test_ONNX_nets, ResNet101_DUC_HDC)
TEST_P(Test_ONNX_nets, TinyYolov2)
{
if (cvtest::skipUnstableTests ||
backend == DNN_BACKEND_INFERENCE_ENGINE && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) {
(backend == DNN_BACKEND_INFERENCE_ENGINE && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))) {
throw SkipTestException("");
}
// output range: [-11; 8]
modules/dnn/test/test_precomp.hpp
-96
View File
@@ -49,100 +49,4 @@
#include "opencv2/dnn.hpp"
#include "test_common.hpp"
namespace opencv_test {
using namespace cv::dnn;
static testing::internal::ParamGenerator<Target> availableDnnTargets()
{
static std::vector<Target> targets;
if (targets.empty())
{
targets.push_back(DNN_TARGET_CPU);
#ifdef HAVE_OPENCL
if (cv::ocl::useOpenCL())
targets.push_back(DNN_TARGET_OPENCL);
#endif
}
return testing::ValuesIn(targets);
}
class DNNTestLayer : public TestWithParam<tuple<Backend, Target> >
{
public:
dnn::Backend backend;
dnn::Target target;
double default_l1, default_lInf;
DNNTestLayer()
{
backend = (dnn::Backend)(int)get<0>(GetParam());
target = (dnn::Target)(int)get<1>(GetParam());
getDefaultThresholds(backend, target, &default_l1, &default_lInf);
}
static void getDefaultThresholds(int backend, int target, double* l1, double* lInf)
{
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)
{
*l1 = 4e-3;
*lInf = 2e-2;
}
else
{
*l1 = 1e-5;
*lInf = 1e-4;
}
}
static void checkBackend(int backend, int target, Mat* inp = 0, Mat* ref = 0)
{
if (backend == DNN_BACKEND_OPENCV && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
{
#ifdef HAVE_OPENCL
if (!cv::ocl::useOpenCL())
#endif
{
throw SkipTestException("OpenCL is not available/disabled in OpenCV");
}
}
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
{
if (!checkMyriadTarget())
{
throw SkipTestException("Myriad is not available/disabled in OpenCV");
}
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE < 2018030000
if (inp && ref && inp->size[0] != 1)
{
// Myriad plugin supports only batch size 1. Slice a single sample.
if (inp->size[0] == ref->size[0])
{
std::vector<cv::Range> range(inp->dims, Range::all());
range[0] = Range(0, 1);
*inp = inp->operator()(range);
range = std::vector<cv::Range>(ref->dims, Range::all());
range[0] = Range(0, 1);
*ref = ref->operator()(range);
}
else
throw SkipTestException("Myriad plugin supports only batch size 1");
}
#else
if (inp && ref && inp->dims == 4 && ref->dims == 4 &&
inp->size[0] != 1 && inp->size[0] != ref->size[0])
throw SkipTestException("Inconsistent batch size of input and output blobs for Myriad plugin");
#endif
}
}
protected:
void checkBackend(Mat* inp = 0, Mat* ref = 0)
{
checkBackend(backend, target, inp, ref);
}
};
} // namespace
#endif
modules/dnn/test/test_tf_importer.cpp
+2
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@@ -101,7 +101,9 @@ public:
string dataConfig;
if (hasText)
{
ASSERT_TRUE(readFileInMemory(netConfig, dataConfig));
}
net = readNetFromTensorflow(dataModel.c_str(), dataModel.size(),
dataConfig.c_str(), dataConfig.size());