TEST_P(DNNTestNetwork, MobileNet_SSD_Caffe_Different_Width_Height)
{
if (backend == DNN_BACKEND_HALIDE)
throw SkipTestException("");
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
throw SkipTestException("Test is disabled for MyriadX");
#endif
Mat sample = imread(findDataFile("dnn/street.png", false));
Mat inp = blobFromImage(sample, 1.0f / 127.5, Size(300, 560), Scalar(127.5, 127.5, 127.5), false);
float diffScores = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.029 : 0.0;
float diffSquares = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.09 : 0.0;
processNet("dnn/MobileNetSSD_deploy.caffemodel", "dnn/MobileNetSSD_deploy.prototxt",
inp, "detection_out", "", diffScores, diffSquares);
expectNoFallbacksFromIE(net);
}
TEST_P(DNNTestNetwork, MobileNet_SSD_v1_TensorFlow_Different_Width_Height)
{
if (backend == DNN_BACKEND_HALIDE)
throw SkipTestException("");
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
throw SkipTestException("Test is disabled for MyriadX");
#endif
Mat sample = imread(findDataFile("dnn/street.png", false));
Mat inp = blobFromImage(sample, 1.0f, Size(300, 560), Scalar(), false);
float l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.012 : 0.0;
float lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.06 : 0.0;
processNet("dnn/ssd_mobilenet_v1_coco_2017_11_17.pb", "dnn/ssd_mobilenet_v1_coco_2017_11_17.pbtxt",
inp, "detection_out", "", l1, lInf);
expectNoFallbacksFromIE(net);
}
TEST_P(Test_Darknet_layers, shortcut)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE < 2018040000
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_CPU)
throw SkipTestException("Test is enabled starts from OpenVINO 2018R4");
#endif
testDarknetLayer("shortcut");
}
TEST_P(DNNTestNetwork, opencv_face_detector)
{
if (backend == DNN_BACKEND_HALIDE) throw SkipTestException("");
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);
processNet("dnn/opencv_face_detector.caffemodel", "dnn/opencv_face_detector.prototxt",
inp, "detection_out");
}
PERF_TEST_P_(DNNTestNetwork, OpenPose_pose_mpi_faster_4_stages)
{
if (backend == DNN_BACKEND_HALIDE) throw SkipTestException("");
// The same .caffemodel but modified .prototxt
// See https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/src/openpose/pose/poseParameters.cpp
processNet("dnn/openpose_pose_mpi.caffemodel", "dnn/openpose_pose_mpi_faster_4_stages.prototxt", "",
Mat(cv::Size(368, 368), CV_32FC3), "", "caffe");
}
void TestExcelAttachedDocument::TestSave()
{
if (isPASubstDrive()) // CH 12/10/07
{
throw SkipTestException(_T("Excel 'Save' does not work on a 'subst'ed P: drive - Test skipped"));
}
throw SkipTestException(_T("TODO: Find out why Save sometimes fails (output document open by someone else)"));
Excel::_WorkbookPtr spWorkbook(m_spAttachedDocument);
assertMessage(VARIANT_TRUE == spWorkbook->Saved, _T("Expected the workbook to be saved"));
spWorkbook->Worksheets->Add();
assertMessage(VARIANT_FALSE == spWorkbook->Saved, _T("Expected the workbook to be unsaved after modification"));
m_pDocument->Save();
assertMessage(VARIANT_TRUE == spWorkbook->Saved, _T("Expected the workbook to be saved after a call to save"));
}
TEST_P(Test_TensorFlow_layers, unfused_flatten)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE &&
(target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
throw SkipTestException("");
runTensorFlowNet("unfused_flatten");
runTensorFlowNet("unfused_flatten_unknown_batch");
}
TEST_P(DNNTestNetwork, ENet)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE) throw SkipTestException("");
processNet("dnn/Enet-model-best.net", "", Size(512, 512), "l367_Deconvolution",
target == DNN_TARGET_OPENCL ? "dnn/halide_scheduler_opencl_enet.yml" :
"dnn/halide_scheduler_enet.yml",
2e-5, 0.15);
}
TEST_P(DNNTestNetwork, SSD_VGG16)
{
if (backend == DNN_BACKEND_DEFAULT && target == DNN_TARGET_OPENCL ||
backend == DNN_BACKEND_HALIDE && target == DNN_TARGET_CPU)
throw SkipTestException("");
processNet("dnn/VGG_ILSVRC2016_SSD_300x300_iter_440000.caffemodel",
"dnn/ssd_vgg16.prototxt", Size(300, 300), "detection_out", "caffe");
}
TEST_P(Test_Torch_layers, net_conv_gemm_lrn)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException("");
runTorchNet("net_conv_gemm_lrn", "", false, true,
target == DNN_TARGET_OPENCL_FP16 ? 0.046 : 0.0,
target == DNN_TARGET_OPENCL_FP16 ? 0.023 : 0.0);
}
TEST_P(Test_ONNX_nets, Inception_v1)
{
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException("Test is disabled for Myriad targets");
#endif
testONNXModels("inception_v1", pb);
}
TEST_P(Test_Torch_layers, net_inception_block)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE == 2018030000
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException("");
#endif
runTorchNet("net_inception_block", "", false, true);
}
TEST_P(DNNTestNetwork, OpenFace)
{
#if defined(INF_ENGINE_RELEASE)
#if INF_ENGINE_VER_MAJOR_EQ(2018050000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException("Test is disabled for Myriad targets");
#elif INF_ENGINE_VER_MAJOR_EQ(2018030000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16)
throw SkipTestException("Test has been fixed in OpenVINO 2018R4");
#endif
#endif
if (backend == DNN_BACKEND_HALIDE)
throw SkipTestException("");
const float l1 = (target == DNN_TARGET_MYRIAD) ? 0.0024 : 0.0;
const float lInf = (target == DNN_TARGET_MYRIAD) ? 0.0071 : 0.0;
processNet("dnn/openface_nn4.small2.v1.t7", "", Size(96, 96), "", "", l1, lInf);
}
TEST_P(Test_Torch_layers, run_deconv)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE == 2018040000
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException("Test is disabled for OpenVINO 2018R4");
#endif
runTorchNet("net_deconv");
}
TEST_P(Test_TensorFlow_layers, lstm)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE ||
(backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16))
throw SkipTestException("");
runTensorFlowNet("lstm", true);
runTensorFlowNet("lstm", true, 0.0, 0.0, true);
}
TEST_P(DNNTestNetwork, Inception_v2_SSD_TensorFlow)
{
if (backend == DNN_BACKEND_HALIDE) throw SkipTestException("");
Mat sample = imread(findDataFile("dnn/street.png", false));
Mat inp = blobFromImage(sample, 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), false);
processNet("dnn/ssd_inception_v2_coco_2017_11_17.pb", "dnn/ssd_inception_v2_coco_2017_11_17.pbtxt",
inp, "detection_out");
}
TEST_P(Test_TensorFlow_layers, matmul)
{
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
throw SkipTestException("");
runTensorFlowNet("matmul");
runTensorFlowNet("nhwc_reshape_matmul");
runTensorFlowNet("nhwc_transpose_reshape_matmul");
}
// TODO: fix tests and replace to pooling
TEST_P(Test_TensorFlow_layers, ave_pool_same)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE < 2018030000
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException("Test is enabled starts from OpenVINO 2018R3");
#endif
runTensorFlowNet("ave_pool_same");
}
PERF_TEST_P_(DNNTestNetwork, MobileNet_SSD_TensorFlow)
{
if (backend == DNN_BACKEND_DEFAULT && target == DNN_TARGET_OPENCL ||
backend == DNN_BACKEND_HALIDE)
throw SkipTestException("");
processNet("dnn/ssd_mobilenet_v1_coco.pb", "ssd_mobilenet_v1_coco.pbtxt", "",
Mat(cv::Size(300, 300), CV_32FC3), "", "tensorflow");
}
TEST_P(Test_ONNX_layers, Constant)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2018050000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
throw SkipTestException("Test is disabled for OpenVINO <= 2018R5 + MyriadX target");
#endif
testONNXModels("constant");
}
TEST_P(Test_Torch_layers, run_reshape)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE == 2018040000
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException("Test is disabled for OpenVINO 2018R4");
#endif
runTorchNet("net_reshape_batch");
runTorchNet("net_reshape_channels", "", false, true);
}
TEST_P(DNNTestNetwork, MobileNet_SSD_Caffe)
{
if (backend == DNN_BACKEND_HALIDE) throw SkipTestException("");
Mat sample = imread(findDataFile("dnn/street.png", false));
Mat inp = blobFromImage(sample, 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), false);
processNet("dnn/MobileNetSSD_deploy.caffemodel", "dnn/MobileNetSSD_deploy.prototxt",
inp, "detection_out");
}
void TestFacadeDocumentProvider::TestAreTheseDifferentVersionsOfTheSameDocument()
{
throw SkipTestException(_T("Oh No!! Queryinterface doesn't work!!! Still under investigation!!!"));
//IWSDocumentProvider7Ptr spProvider;
//HRESULT hr = spProvider.CreateInstance("Workshare.FacadeDocumentProvider");
//assertMessage(FAILED(hr), "Failed to instantiate the \"Workshare.FacadeDocumentProvider\"");
//VARIANT_BOOL result;
//assertMessage(E_NOTIMPL == spProvider->raw_AreTheseDifferentVersionsOfTheSameDocument(L"", L"", &result), "Expect E_NOT_IMPL to be returned");
}
void TestFacadeDocumentProvider::TestGetCapabilities()
{
throw SkipTestException(_T("Cannot Queryinterface for IWSDocumentProvider6 on Workshare.FacadeDocumentProvider from within the TestHarness (can from elsewhere). Why, you ask. We don't know. But please tell us if you do."));
//IWSDocumentProvider6Ptr spDocProvider;
//assertMessage(SUCCEEDED(spDocProvider.CreateInstance("Workshare.FacadeDocumentProvider")), "Failed to create an instance of Workshare.FacadeDocumentProvider");
//long capabilities = spDocProvider->GetCapabilities(L"", 0);
//assertMessage(0 <= capabilities, "The capability flags cannot be a negative value");
}
TEST_P(Test_TensorFlow_layers, reshape)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
throw SkipTestException("");
runTensorFlowNet("shift_reshape_no_reorder");
runTensorFlowNet("reshape_no_reorder");
runTensorFlowNet("reshape_reduce");
runTensorFlowNet("reshape_as_shape");
}
TEST_P(DNNTestNetwork, OpenPose_pose_mpi)
{
applyTestTag(CV_TEST_TAG_LONG, (target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_1GB : CV_TEST_TAG_MEMORY_2GB),
CV_TEST_TAG_DEBUG_VERYLONG);
if (backend == DNN_BACKEND_HALIDE)
throw SkipTestException("");
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2018050000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
throw SkipTestException("Test is disabled for OpenVINO <= 2018R5 + MyriadX target");
#endif
// output range: [-0.001, 0.97]
const float l1 = (target == DNN_TARGET_MYRIAD) ? 0.012 : 0.0;
const float lInf = (target == DNN_TARGET_MYRIAD || target == DNN_TARGET_OPENCL_FP16) ? 0.16 : 0.0;
processNet("dnn/openpose_pose_mpi.caffemodel", "dnn/openpose_pose_mpi.prototxt",
Size(46, 46), "", "", l1, lInf);
expectNoFallbacksFromIE(net);
}
TEST_P(Test_Torch_layers, net_residual)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE == 2018050000
if (backend == DNN_BACKEND_INFERENCE_ENGINE && (target == DNN_TARGET_OPENCL ||
target == DNN_TARGET_OPENCL_FP16))
throw SkipTestException("Test is disabled for OpenVINO 2018R5");
#endif
runTorchNet("net_residual", "", false, true);
}
TEST_P(InterpolatorTest, MultiThreadReproducibility)
{
if (cv::getNumberOfCPUs() == 1)
return;
double MAX_DIF = 1.0;
double MAX_MEAN_DIF = 1.0 / 256.0;
int loopsCount = 2;
RNG rng(0);
InterpolatorParams params = GetParam();
Size size = get<0>(params);
int guideType = get<1>(params);
Mat from(size, guideType);
randu(from, 0, 255);
int num_matches = rng.uniform(5,SHRT_MAX-1);
vector<Point2f> from_points;
vector<Point2f> to_points;
for(int i=0;i<num_matches;i++)
{
from_points.push_back(Point2f(rng.uniform(0.01f,(float)size.width-1.01f),rng.uniform(0.01f,(float)size.height-1.01f)));
to_points.push_back(Point2f(rng.uniform(0.01f,(float)size.width-1.01f),rng.uniform(0.01f,(float)size.height-1.01f)));
}
int nThreads = cv::getNumThreads();
if (nThreads == 1)
throw SkipTestException("Single thread environment");
for (int iter = 0; iter <= loopsCount; iter++)
{
int K = rng.uniform(4,512);
float sigma = rng.uniform(0.01f,0.5f);
float FGSlambda = rng.uniform(100.0f, 10000.0f);
float FGSsigma = rng.uniform(0.5f, 100.0f);
Ptr<EdgeAwareInterpolator> interpolator = createEdgeAwareInterpolator();
interpolator->setK(K);
interpolator->setSigma(sigma);
interpolator->setUsePostProcessing(true);
interpolator->setFGSLambda(FGSlambda);
interpolator->setFGSSigma(FGSsigma);
cv::setNumThreads(nThreads);
Mat resMultiThread;
interpolator->interpolate(from,from_points,Mat(),to_points,resMultiThread);
cv::setNumThreads(1);
Mat resSingleThread;
interpolator->interpolate(from,from_points,Mat(),to_points,resSingleThread);
EXPECT_LE(cv::norm(resSingleThread, resMultiThread, NORM_INF), MAX_DIF);
EXPECT_LE(cv::norm(resSingleThread, resMultiThread, NORM_L1) , MAX_MEAN_DIF*resMultiThread.total());
}
}
// TODO: fix pad_and_concat and add this test case to fp16_weights
TEST_P(Test_TensorFlow_layers, fp16_pad_and_concat)
{
const float l1 = 0.00071;
const float lInf = 0.012;
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE < 2018030000
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException("Test is enabled starts from OpenVINO 2018R3");
#endif
runTensorFlowNet("fp16_pad_and_concat", false, l1, lInf);
}
void TestFacadeDocumentProvider::TestIsDmsInOfflineMode()
{
throw SkipTestException(_T("Oh No!! Queryinterface doesn't work!!! Still under investigation!!!"));
//IWSDocumentProvider7Ptr spProvider;
//HRESULT hr = spProvider.CreateInstance("Workshare.FacadeDocumentProvider");
//assertMessage(FAILED(hr), "Failed to instantiate the \"Workshare.FacadeDocumentProvider\"");
//VARIANT_BOOL result;
//assertMessage(S_OK == spProvider->raw_IsDmsInOfflineMode(L"", &result), "Expect S_OK to be returned");
//assertMessage(VARIANT_FALSE == result, "Expect VARIANT_FALSE to be returned");
}
