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Code Packages Releases Wiki Activity

Disable some tests for Myriad target of nGraph

Browse Source 
Add lightweight IE hardware targets checks

nGraph: Concat with paddings

Enable more nGraph tests

Restore FP32->FP16 for GPU plugin of IE

try to fix buildbot

Use lightweight IE targets check only starts from R4
This commit is contained in:
Dmitry Kurtaev
2019-12-24 13:34:33 +03:00
committed by Alexander Alekhin
parent 43cb28bdb5
commit 2f23737e66
14 changed files with 174 additions and 59 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/dnn/src/dnn.cpp
+17
View File
@@ -109,6 +109,22 @@ public:
#ifdef HAVE_INF_ENGINE
static inline bool checkIETarget(Target target)
{
#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2019R3)
// Lightweight detection
const std::vector<std::string> devices = getCore().GetAvailableDevices();
for (std::vector<std::string>::const_iterator i = devices.begin(); i != devices.end(); ++i)
{
if (std::string::npos != i->find("MYRIAD") && target == DNN_TARGET_MYRIAD)
return true;
else if (std::string::npos != i->find("FPGA") && target == DNN_TARGET_FPGA)
return true;
else if (std::string::npos != i->find("CPU") && target == DNN_TARGET_CPU)
return true;
else if (std::string::npos != i->find("GPU") && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
return true;
}
return false;
#else
cv::dnn::Net net;
cv::dnn::LayerParams lp;
lp.set("kernel_size", 1);
@@ -132,6 +148,7 @@ public:
return false;
}
return true;
#endif
}
#endif
modules/dnn/src/ie_ngraph.cpp
+15 -10
View File
@@ -168,21 +168,26 @@ void InfEngineNgraphNet::init(Target targetId)
{
if (!hasNetOwner)
{
if (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) {
if (targetId == DNN_TARGET_OPENCL_FP16)
{
auto nodes = ngraph_function->get_ordered_ops();
for (auto& node : nodes) {
for (auto& node : nodes)
{
auto parameter = std::dynamic_pointer_cast<ngraph::op::Parameter>(node);
if (parameter && parameter->get_element_type() == ngraph::element::f32) {
if (parameter && parameter->get_element_type() == ngraph::element::f32)
{
parameter->set_element_type(ngraph::element::f16);
}
auto constant = std::dynamic_pointer_cast<ngraph::op::Constant>(node);
if (constant && constant->get_element_type() == ngraph::element::f32) {
auto data = constant->get_vector<float>();
std::vector<ngraph::float16> new_data(data.size());
for (size_t i = 0; i < data.size(); ++i) {
new_data[i] = ngraph::float16(data[i]);
}
auto new_const = std::make_shared<ngraph::op::Constant>(ngraph::element::f16, constant->get_shape(), new_data);
if (constant && constant->get_element_type() == ngraph::element::f32)
{
const float* floatsData = constant->get_data_ptr<float>();
size_t total = ngraph::shape_size(constant->get_shape());
Mat floats(1, total, CV_32F, (void*)floatsData);
Mat halfs;
cv::convertFp16(floats, halfs);
auto new_const = std::make_shared<ngraph::op::Constant>(ngraph::element::f16, constant->get_shape(), halfs.data);
new_const->set_friendly_name(constant->get_friendly_name());
ngraph::replace_node(constant, new_const);
}
modules/dnn/src/layers/concat_layer.cpp
+38 -6
View File
@@ -114,7 +114,8 @@ public:
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_CUDA ||
(backendId == DNN_BACKEND_HALIDE && haveHalide() && axis == 1 && !padding) || // By channels
((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && haveInfEngine() && !padding) ||
(backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && haveInfEngine() && !padding) ||
backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH ||
(backendId == DNN_BACKEND_VKCOM && haveVulkan() && !padding);
}
@@ -351,14 +352,45 @@ public:
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
InferenceEngine::DataPtr data = ngraphDataNode(inputs[0]);
const int numDims = data->getDims().size();
const int cAxis = clamp(axis, numDims);
std::vector<size_t> maxDims(numDims, 0);
CV_Assert(inputs.size() == nodes.size());
ngraph::NodeVector inp_nodes;
for (auto& node : nodes) {
inp_nodes.push_back(node.dynamicCast<InfEngineNgraphNode>()->node);
}
for (int i = 0; i < nodes.size(); ++i)
{
inp_nodes.push_back(nodes[i].dynamicCast<InfEngineNgraphNode>()->node);
InferenceEngine::DataPtr data = ngraphDataNode(inputs[0]);
auto concat = std::make_shared<ngraph::op::Concat>(inp_nodes, clamp(axis, data->getDims().size()));
std::vector<size_t> inpShape = ngraphDataNode(inputs[i])->getDims();
for (int i = 0; i < numDims; ++i)
maxDims[i] = std::max(maxDims[i], inpShape[i]);
}
for (int i = 0; i < inp_nodes.size(); ++i)
{
bool needPadding = false;
std::vector<size_t> inpShape = ngraphDataNode(inputs[i])->getDims();
std::vector<int64_t> begins(inpShape.size(), 0), ends(inpShape.size(), 0);
for (int j = 0; j < inpShape.size(); ++j)
{
if (j != cAxis && inpShape[j] != maxDims[j])
{
needPadding = true;
begins[j] = static_cast<int64_t>((maxDims[j] - inpShape[j]) / 2);
ends[j] = static_cast<int64_t>(maxDims[j] - inpShape[j] - begins[j]);
}
}
if (needPadding)
{
inp_nodes[i] = std::make_shared<ngraph::op::v1::Pad>(
inp_nodes[i],
std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{begins.size()}, begins.data()),
std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{ends.size()}, ends.data()),
ngraph::op::PadMode::CONSTANT);
}
}
auto concat = std::make_shared<ngraph::op::Concat>(inp_nodes, cAxis);
return Ptr<BackendNode>(new InfEngineNgraphNode(concat));
}
#endif // HAVE_DNN_NGRAPH
modules/dnn/src/layers/pooling_layer.cpp
+1 -1
View File
@@ -203,7 +203,7 @@ public:
#endif
}
else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) {
return type != STOCHASTIC;
return !computeMaxIdx && type != STOCHASTIC;
}
else
{
modules/dnn/src/op_inf_engine.cpp
+16
View File
@@ -574,6 +574,21 @@ InferenceEngine::Core& getCore()
#if !defined(OPENCV_DNN_IE_VPU_TYPE_DEFAULT)
static bool detectMyriadX_()
{
#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2019R3)
// Lightweight detection
InferenceEngine::Core& ie = getCore();
const std::vector<std::string> devices = ie.GetAvailableDevices();
for (std::vector<std::string>::const_iterator i = devices.begin(); i != devices.end(); ++i)
{
if (i->find("MYRIAD") != std::string::npos)
{
const std::string name = ie.GetMetric(*i, METRIC_KEY(FULL_DEVICE_NAME)).as<std::string>();
CV_LOG_INFO(NULL, "Myriad device: " << name);
return name.find("MyriadX") != std::string::npos || name.find("Myriad X") != std::string::npos;
}
}
return false;
#else
InferenceEngine::Builder::Network builder("");
InferenceEngine::idx_t inpId = builder.addLayer(
InferenceEngine::Builder::InputLayer().setPort(InferenceEngine::Port({1})));
@@ -634,6 +649,7 @@ static bool detectMyriadX_()
return false;
}
return true;
#endif
}
#endif // !defined(OPENCV_DNN_IE_VPU_TYPE_DEFAULT)
modules/dnn/test/test_backends.cpp
+4 -4
View File
@@ -197,8 +197,8 @@ TEST_P(DNNTestNetwork, MobileNet_SSD_Caffe_Different_Width_Height)
if (backend == DNN_BACKEND_HALIDE)
applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) &&
target == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
#endif
Mat sample = imread(findDataFile("dnn/street.png"));
@@ -249,8 +249,8 @@ TEST_P(DNNTestNetwork, MobileNet_SSD_v1_TensorFlow_Different_Width_Height)
if (backend == DNN_BACKEND_HALIDE)
applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) &&
target == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
#endif
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019020000)
modules/dnn/test/test_caffe_importer.cpp
+8 -4
View File
@@ -691,9 +691,11 @@ TEST_P(Test_Caffe_nets, FasterRCNN_zf)
(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB),
CV_TEST_TAG_DEBUG_LONG
);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_OPENCL_FP16)
if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||
backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||
backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
if (target == DNN_TARGET_CUDA_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA_FP16);
@@ -710,9 +712,11 @@ TEST_P(Test_Caffe_nets, RFCN)
CV_TEST_TAG_LONG,
CV_TEST_TAG_DEBUG_VERYLONG
);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_OPENCL_FP16)
if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||
backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||
backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
float scoreDiff = default_l1, iouDiff = default_lInf;
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
modules/dnn/test/test_darknet_importer.cpp
+6 -5
View File
@@ -307,8 +307,8 @@ TEST_P(Test_Darknet_nets, YoloVoc)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
#endif
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) &&
target == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X); // need to update check function
#endif
@@ -352,8 +352,8 @@ TEST_P(Test_Darknet_nets, TinyYoloVoc)
applyTestTag(CV_TEST_TAG_MEMORY_512MB);
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) &&
target == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X); // need to update check function
#endif
// batchId, classId, confidence, left, top, right, bottom
@@ -486,7 +486,8 @@ TEST_P(Test_Darknet_nets, YOLOv3)
std::string weights_file = "yolov3.weights";
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD &&
if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||
backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && target == DNN_TARGET_MYRIAD &&
getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
{
scoreDiff = 0.04;
modules/dnn/test/test_halide_layers.cpp
+2 -6
View File
@@ -357,11 +357,6 @@ TEST_P(MaxPooling, Accuracy)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
#if defined(INF_ENGINE_RELEASE)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && stride != Size(1, 1) && pad != Size(0, 0))
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
#endif
LayerParams lp;
lp.set("pool", "max");
lp.set("kernel_w", kernel.width);
@@ -399,7 +394,8 @@ TEST_P(FullyConnected, Accuracy)
bool hasBias = get<3>(GetParam());
Backend backendId = get<0>(get<4>(GetParam()));
Target targetId = get<1>(get<4>(GetParam()));
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && (targetId == DNN_TARGET_OPENCL_FP16 ||
if ((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||
backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && (targetId == DNN_TARGET_OPENCL_FP16 ||
(targetId == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X))) {
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
modules/dnn/test/test_ie_models.cpp
+5 -4
View File
@@ -134,12 +134,13 @@ static const std::vector<std::string> getOpenVINOTestModelsList()
return result;
}
static inline void genData(const std::vector<size_t>& dims, Mat& m, Blob::Ptr& dataPtr)
static inline void genData(const InferenceEngine::TensorDesc& desc, Mat& m, Blob::Ptr& dataPtr)
{
const std::vector<size_t>& dims = desc.getDims();
m.create(std::vector<int>(dims.begin(), dims.end()), CV_32F);
randu(m, -1, 1);
dataPtr = make_shared_blob<float>({Precision::FP32, dims, Layout::ANY}, (float*)m.data);
dataPtr = make_shared_blob<float>(desc, (float*)m.data);
}
void runIE(Target target, const std::string& xmlPath, const std::string& binPath,
@@ -238,7 +239,7 @@ void runIE(Target target, const std::string& xmlPath, const std::string& binPath
BlobMap inputBlobs;
for (auto& it : net.getInputsInfo())
{
genData(it.second->getTensorDesc().getDims(), inputsMap[it.first], inputBlobs[it.first]);
genData(it.second->getTensorDesc(), inputsMap[it.first], inputBlobs[it.first]);
}
infRequest.SetInput(inputBlobs);
@@ -247,7 +248,7 @@ void runIE(Target target, const std::string& xmlPath, const std::string& binPath
BlobMap outputBlobs;
for (auto& it : net.getOutputsInfo())
{
genData(it.second->getTensorDesc().getDims(), outputsMap[it.first], outputBlobs[it.first]);
genData(it.second->getTensorDesc(), outputsMap[it.first], outputBlobs[it.first]);
}
infRequest.SetOutput(outputBlobs);
modules/dnn/test/test_layers.cpp
+4 -2
View File
@@ -864,6 +864,8 @@ TEST_P(Test_Caffe_layers, PriorBox_squares)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
LayerParams lp;
lp.name = "testPriorBox";
lp.type = "PriorBox";
@@ -1301,7 +1303,7 @@ static void test_dldt_fused_output(Backend backend, Target target)
}
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
net.setInput(Mat({1, 1, 1, 1}, CV_32FC1, Scalar(1)));
net.setInput(Mat({1, 1, 2, 3}, CV_32FC1, Scalar(1)));
net.forward();
}
@@ -1340,7 +1342,7 @@ TEST_P(Test_DLDT_layers, multiple_networks)
nets[i].addLayerToPrev(lp.name, lp.type, lp);
nets[i].setPreferableBackend(backend);
nets[i].setPreferableTarget(target);
nets[i].setInput(Mat({1, 1, 1, 1}, CV_32FC1, Scalar(1)));
nets[i].setInput(Mat({1, 1, 2, 3}, CV_32FC1, Scalar(1)));
}
Mat out_1 = nets[0].forward();
Mat out_2 = nets[1].forward();
modules/dnn/test/test_onnx_importer.cpp
+27 -11
View File
@@ -369,9 +369,12 @@ TEST_P(Test_ONNX_layers, Div)
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
Mat inp1 = blobFromNPY(_tf("data/input_div_0.npy"));
Mat inp2 = blobFromNPY(_tf("data/input_div_1.npy"));
// Reference output values range is -68.80928, 2.991873. So to avoid computational
// difference for FP16 we'll perform reversed division (just swap inputs).
Mat inp1 = blobFromNPY(_tf("data/input_div_1.npy"));
Mat inp2 = blobFromNPY(_tf("data/input_div_0.npy"));
Mat ref = blobFromNPY(_tf("data/output_div.npy"));
cv::divide(1.0, ref, ref);
checkBackend(&inp1, &ref);
net.setInput(inp1, "0");
@@ -473,6 +476,9 @@ TEST_P(Test_ONNX_nets, Googlenet)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
const String model = _tf("models/googlenet.onnx", false);
Net net = readNetFromONNX(model);
@@ -516,7 +522,7 @@ TEST_P(Test_ONNX_nets, RCNN_ILSVRC13)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
// Reference output values are in range [-4.992, -1.161]
testONNXModels("rcnn_ilsvrc13", pb, 0.0045);
testONNXModels("rcnn_ilsvrc13", pb, 0.0046);
}
TEST_P(Test_ONNX_nets, VGG16_bn)
@@ -583,10 +589,12 @@ TEST_P(Test_ONNX_nets, TinyYolov2)
)
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X
if (target == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X
)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X,
backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ?
CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER :
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
#endif
// output range: [-11; 8]
@@ -628,6 +636,12 @@ TEST_P(Test_ONNX_nets, LResNet100E_IR)
if (target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
}
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
if (target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
}
double l1 = default_l1, lInf = default_lInf;
// output range: [-3; 3]
@@ -652,10 +666,11 @@ TEST_P(Test_ONNX_nets, LResNet100E_IR)
TEST_P(Test_ONNX_nets, Emotion_ferplus)
{
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X
)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (target == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X,
backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ?
CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER :
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
#endif
double l1 = default_l1;
@@ -692,7 +707,8 @@ TEST_P(Test_ONNX_nets, DenseNet121)
TEST_P(Test_ONNX_nets, Inception_v1)
{
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||
backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
#endif
testONNXModels("inception_v1", pb);
modules/dnn/test/test_tf_importer.cpp
+19 -6
View File
@@ -261,10 +261,13 @@ TEST_P(Test_TensorFlow_layers, ave_pool_same)
{
// Reference output values are in range [-0.519531, 0.112976]
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X
)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
if (target == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
}
#endif
runTensorFlowNet("ave_pool_same");
}
@@ -399,6 +402,8 @@ TEST_P(Test_TensorFlow_layers, l2_normalize_3d)
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
#endif
runTensorFlowNet("l2_normalize_3d");
@@ -409,11 +414,15 @@ class Test_TensorFlow_nets : public DNNTestLayer {};
TEST_P(Test_TensorFlow_nets, MobileNet_SSD)
{
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
if (target == DNN_TARGET_MYRIAD)
{
#if INF_ENGINE_VER_MAJOR_GE(2019020000)
if (getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X,
backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ?
CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER :
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH,
CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
}
#endif
@@ -554,6 +563,10 @@ TEST_P(Test_TensorFlow_nets, Faster_RCNN)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 &&
(INF_ENGINE_VER_MAJOR_LT(2019020000) || target != DNN_TARGET_CPU))
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
if (INF_ENGINE_VER_MAJOR_GT(2019030000) &&
backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
#endif
// segfault: inference-engine/thirdparty/clDNN/src/gpu/detection_output_cpu.cpp:111:
// Assertion `prior_height > 0' failed.
modules/dnn/test/test_torch_importer.cpp
+12
View File
@@ -239,6 +239,8 @@ TEST_P(Test_Torch_layers, net_conv_gemm_lrn)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
double l1 = 0.0, lInf = 0.0;
if (target == DNN_TARGET_OPENCL_FP16)
{
@@ -398,6 +400,13 @@ TEST_P(Test_Torch_nets, ENet_accuracy)
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
throw SkipTestException("");
}
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU)
{
if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
if (target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
throw SkipTestException("");
}
Net net;
{
@@ -450,6 +459,9 @@ TEST_P(Test_Torch_nets, FastNeuralStyle_accuracy)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
#endif
checkBackend();