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Merge remote-tracking branch 'upstream/3.4' into merge-3.4

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pull/21572/head
Alexander Alekhin 3 years ago
parent 46e1560678 a00a0dbfcd
commit 57d3002ee1
8 changed files with 109 additions and 59 deletions
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  1. 2
      CMakeLists.txt
  2. 13
      cmake/OpenCVDetectInferenceEngine.cmake
  3. 1
      cmake/OpenCVUtils.cmake
  4. 10
      modules/dnn/include/opencv2/dnn/shape_utils.hpp
  5. 66
      modules/dnn/src/dnn.cpp
  6. 47
      modules/dnn/src/ie_ngraph.cpp
  7. 8
      modules/dnn/src/ie_ngraph.hpp
  8. 21
      modules/dnn/test/test_onnx_conformance_layer_filter__openvino.inl.hpp

2
CMakeLists.txt
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@ -243,7 +243,7 @@ OCV_OPTION(WITH_AVFOUNDATION "Use AVFoundation for Video I/O (iOS/Mac)" ON
OCV_OPTION(WITH_CAP_IOS "Enable iOS video capture" ON OCV_OPTION(WITH_CAP_IOS "Enable iOS video capture" ON
VISIBLE_IF IOS VISIBLE_IF IOS
VERIFY HAVE_CAP_IOS) VERIFY HAVE_CAP_IOS)
OCV_OPTION(WITH_CAROTENE "Use NVidia carotene acceleration library for ARM platform" ON OCV_OPTION(WITH_CAROTENE "Use NVidia carotene acceleration library for ARM platform" (NOT CV_DISABLE_OPTIMIZATION)
VISIBLE_IF (ARM OR AARCH64) AND NOT IOS) VISIBLE_IF (ARM OR AARCH64) AND NOT IOS)
OCV_OPTION(WITH_CPUFEATURES "Use cpufeatures Android library" ON OCV_OPTION(WITH_CPUFEATURES "Use cpufeatures Android library" ON
VISIBLE_IF ANDROID VISIBLE_IF ANDROID

13
cmake/OpenCVDetectInferenceEngine.cmake
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@ -16,6 +16,19 @@ endif()
# ====================== # ======================
if(WITH_OPENVINO)
find_package(OpenVINO QUIET)
if(OpenVINO_FOUND)
message(STATUS "OpenVINO FOUND: ${OpenVINO_VERSION}")
math(EXPR ver "${OpenVINO_VERSION_MAJOR} * 1000000 + ${OpenVINO_VERSION_MINOR} * 10000 + ${OpenVINO_VERSION_PATCH} * 100")
ocv_add_external_target(openvino "" "openvino::runtime" "INF_ENGINE_RELEASE=${ver};HAVE_NGRAPH;HAVE_DNN_NGRAPH;HAVE_INF_ENGINE")
set(HAVE_OPENVINO 1)
return()
endif()
endif()
# ======================
macro(ocv_ie_find_extra_libraries find_prefix find_suffix) macro(ocv_ie_find_extra_libraries find_prefix find_suffix)
file(GLOB libraries "${INF_ENGINE_LIB_DIRS}/${find_prefix}inference_engine*${find_suffix}") file(GLOB libraries "${INF_ENGINE_LIB_DIRS}/${find_prefix}inference_engine*${find_suffix}")
foreach(full_path IN LISTS libraries) foreach(full_path IN LISTS libraries)

1
cmake/OpenCVUtils.cmake
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@ -1619,6 +1619,7 @@ function(ocv_add_external_target name inc link def)
endif() endif()
endfunction() endfunction()
# Returns the first non-interface target # Returns the first non-interface target
function(ocv_get_imported_target imported interface) function(ocv_get_imported_target imported interface)
set(__result "${interface}") set(__result "${interface}")

10
modules/dnn/include/opencv2/dnn/shape_utils.hpp
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@ -184,7 +184,8 @@ static inline MatShape concat(const MatShape& a, const MatShape& b)
return c; return c;
} }
static inline std::string toString(const MatShape& shape, const String& name = "") template<typename _Tp>
static inline std::string toString(const std::vector<_Tp>& shape, const String& name = "")
{ {
std::ostringstream ss; std::ostringstream ss;
if (!name.empty()) if (!name.empty())
@ -195,11 +196,14 @@ static inline std::string toString(const MatShape& shape, const String& name = "
ss << " ]"; ss << " ]";
return ss.str(); return ss.str();
} }
static inline void print(const MatShape& shape, const String& name = "")
template<typename _Tp>
static inline void print(const std::vector<_Tp>& shape, const String& name = "")
{ {
std::cout << toString(shape, name) << std::endl; std::cout << toString(shape, name) << std::endl;
} }
static inline std::ostream& operator<<(std::ostream &out, const MatShape& shape) template<typename _Tp>
static inline std::ostream& operator<<(std::ostream &out, const std::vector<_Tp>& shape)
{ {
out << toString(shape); out << toString(shape);
return out; return out;

66
modules/dnn/src/dnn.cpp
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@ -2120,10 +2120,15 @@ struct Net::Impl : public detail::NetImplBase
#ifdef HAVE_DNN_NGRAPH #ifdef HAVE_DNN_NGRAPH
/** mark input pins as outputs from other subnetworks
* FIXIT must be done by DNN engine not ngraph.
*/
void addNgraphOutputs(LayerData &ld) void addNgraphOutputs(LayerData &ld)
{ {
CV_TRACE_FUNCTION(); CV_TRACE_FUNCTION();
CV_LOG_DEBUG(NULL, "DNN/IE: layer of new subnet: " << ld.name << "@" << ld.type);
Ptr<InfEngineNgraphNet> layerNet; Ptr<InfEngineNgraphNet> layerNet;
auto it = ld.backendNodes.find(preferableBackend); auto it = ld.backendNodes.find(preferableBackend);
if (it != ld.backendNodes.end()) if (it != ld.backendNodes.end())
@ -2147,8 +2152,8 @@ struct Net::Impl : public detail::NetImplBase
CV_Assert(!ieInpNode.empty()); CV_Assert(!ieInpNode->net.empty()); CV_Assert(!ieInpNode.empty()); CV_Assert(!ieInpNode->net.empty());
if (layerNet != ieInpNode->net) if (layerNet != ieInpNode->net)
{ {
ieInpNode->net->addOutput(ieInpNode->node->get_friendly_name()); CV_LOG_DEBUG(NULL, "DNN/IE: pin output between subnets: " << ieInpNode->node->get_friendly_name());
ieInpNode->net->setUnconnectedNodes(ieInpNode); ieInpNode->net->addOutput(ieInpNode);
} }
} }
} }
@ -2247,13 +2252,19 @@ struct Net::Impl : public detail::NetImplBase
{ {
LayerData& ld = it->second; LayerData& ld = it->second;
CV_LOG_DEBUG(NULL, "DNN/IE: processing layer " << ld.name << "@" << ld.type << " (" << ld.id << ") ...");
if (ld.id == 0 && ld.skip) if (ld.id == 0 && ld.skip)
{
CV_LOG_DEBUG(NULL, "DNN/IE: SKIP!");
continue; continue;
}
bool fused = ld.skip; bool fused = ld.skip;
Ptr<Layer> layer = ld.layerInstance; Ptr<Layer> layer = ld.layerInstance;
if (!fused && !layer->supportBackend(preferableBackend)) if (!fused && !layer->supportBackend(preferableBackend))
{ {
CV_LOG_DEBUG(NULL, "DNN/IE: NOT supported!");
bool customizable = ld.id != 0 && supportsCPUFallback; bool customizable = ld.id != 0 && supportsCPUFallback;
// TODO: there is a bug in Myriad plugin with custom layers shape infer. // TODO: there is a bug in Myriad plugin with custom layers shape infer.
@ -2281,6 +2292,7 @@ struct Net::Impl : public detail::NetImplBase
if (!customizable) if (!customizable)
{ {
CV_LOG_DEBUG(NULL, "DNN/IE: NOT customizable!");
addNgraphOutputs(ld); addNgraphOutputs(ld);
net = Ptr<InfEngineNgraphNet>(); net = Ptr<InfEngineNgraphNet>();
layer->preferableTarget = DNN_TARGET_CPU; layer->preferableTarget = DNN_TARGET_CPU;
@ -2292,7 +2304,7 @@ struct Net::Impl : public detail::NetImplBase
if (!inpNode.empty()) { if (!inpNode.empty()) {
Ptr<InfEngineNgraphNode> ieNode = inpNode.dynamicCast<InfEngineNgraphNode>(); Ptr<InfEngineNgraphNode> ieNode = inpNode.dynamicCast<InfEngineNgraphNode>();
CV_Assert(!ieNode.empty()); CV_Assert(!ieNode.empty());
ieNode->net->setUnconnectedNodes(ieNode); ieNode->net->addOutput(ieNode);
} }
} }
continue; continue;
@ -2380,9 +2392,22 @@ struct Net::Impl : public detail::NetImplBase
continue; continue;
auto ieInpNode = inputNodes[i].dynamicCast<InfEngineNgraphNode>(); auto ieInpNode = inputNodes[i].dynamicCast<InfEngineNgraphNode>();
CV_Assert(oid < ieInpNode->node->get_output_size()); const auto& ngraph_input_node = ieInpNode->node;
CV_LOG_DEBUG(NULL, "DNN/IE: bind output port " << lid << ":" << oid << " (" << ngraph_input_node->get_friendly_name() << ":" << ngraph_input_node->get_type_info().name << ")");
// Handle parameters from other subnets. Output port is not used in this case
if ((ngraph::op::is_parameter(ngraph_input_node) || ngraph::op::is_constant(ngraph_input_node)) &&
ngraph_input_node->get_output_size() == 1)
{
inputNodes[i] = Ptr<BackendNode>(new InfEngineNgraphNode(ngraph_input_node));
continue;
}
CV_CheckLT((size_t)oid, ngraph_input_node->get_output_size(), "");
#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2020_4) #if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2020_4)
inputNodes[i] = Ptr<BackendNode>(new InfEngineNgraphNode(ieInpNode->node)); // FIXIT refactor ".initNgraph()" API to use Output<Node>
// WA: use Concat to emulate Identity operation with requested output port
auto oid_node = std::make_shared<ngraph::op::Concat>(ngraph::OutputVector {ngraph_input_node->output(oid)}, 0);
inputNodes[i] = Ptr<BackendNode>(new InfEngineNgraphNode(oid_node));
#elif INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2020_3) #elif INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2020_3)
inputNodes[i] = Ptr<BackendNode>(new InfEngineNgraphNode(ieInpNode->node->get_output_as_single_output_node(oid))); inputNodes[i] = Ptr<BackendNode>(new InfEngineNgraphNode(ieInpNode->node->get_output_as_single_output_node(oid)));
#else #else
@ -2392,21 +2417,30 @@ struct Net::Impl : public detail::NetImplBase
if (layer->supportBackend(preferableBackend)) if (layer->supportBackend(preferableBackend))
{ {
CV_LOG_DEBUG(NULL, "DNN/IE: wrap layer " << ld.name << "@" << ld.type << " - outputs: " << ld.outputBlobsWrappers.size());
node = layer->initNgraph(ld.inputBlobsWrappers, inputNodes); node = layer->initNgraph(ld.inputBlobsWrappers, inputNodes);
#if 0 // FIXIT doesn't work with multiple outputs (set name is applied to the same node)
for (int i = 0; i < ld.outputBlobsWrappers.size(); ++i) for (int i = 0; i < ld.outputBlobsWrappers.size(); ++i)
{ {
InferenceEngine::DataPtr dataPtr = ngraphDataNode(ld.outputBlobsWrappers[i]); InferenceEngine::DataPtr dataPtr = ngraphDataNode(ld.outputBlobsWrappers[i]);
node.dynamicCast<InfEngineNgraphNode>()->setName(dataPtr->getName()); node.dynamicCast<InfEngineNgraphNode>()->setName(dataPtr->getName());
} }
#else
node.dynamicCast<InfEngineNgraphNode>()->setName(layer->name);
#endif
} }
else else
{ {
CV_LOG_DEBUG(NULL, "DNN/IE: layer is not supported: " << ld.name << "@" << ld.type);
node = Ptr<BackendNode>(new InfEngineNgraphNode(inputNodes, node = Ptr<BackendNode>(new InfEngineNgraphNode(inputNodes,
ld.layerInstance, ld.inputBlobs, ld.outputBlobs, ld.internals)); ld.layerInstance, ld.inputBlobs, ld.outputBlobs, ld.internals));
} }
} }
else if (node.empty()) else if (node.empty())
{
CV_LOG_DEBUG(NULL, "DNN/IE: node.empty() bypass...");
continue; continue;
}
ld.backendNodes[preferableBackend] = node; ld.backendNodes[preferableBackend] = node;
@ -2414,15 +2448,11 @@ struct Net::Impl : public detail::NetImplBase
CV_Assert(!ieNode.empty()); CV_Assert(!ieNode.empty());
ieNode->net = net; ieNode->net = net;
if (ld.consumers.empty()) {
// TF EAST_text_detection
ieNode->net->setUnconnectedNodes(ieNode);
}
for (const auto& pin : blobsToKeep_) for (const auto& pin : blobsToKeep_)
{ {
if (pin.lid == ld.id) if (pin.lid == ld.id)
{ {
ieNode->net->addOutput(ieNode->node->get_friendly_name()); ieNode->net->addOutput(ieNode);
break; break;
} }
} }
@ -2453,7 +2483,7 @@ struct Net::Impl : public detail::NetImplBase
if (!ieNode->net->isInitialized()) if (!ieNode->net->isInitialized())
{ {
ieNode->net->setUnconnectedNodes(ieNode); ieNode->net->addOutput(ieNode);
ieNode->net->createNet((Target)preferableTarget); ieNode->net->createNet((Target)preferableTarget);
ld.skip = false; ld.skip = false;
} }
@ -2967,8 +2997,15 @@ struct Net::Impl : public detail::NetImplBase
preferableBackend != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)) preferableBackend != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH))
return; return;
#if 0 // FIXIT mode without fusion is broken due to unsupported layers and handling of "custom" nodes
if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
return;
#endif
// scan through all the layers. If there is convolution layer followed by the activation layer, // scan through all the layers. If there is convolution layer followed by the activation layer,
// we try to embed this activation into the convolution and disable separate execution of the activation // we try to embed this activation into the convolution and disable separate execution of the activation
// FIXIT replace by layersToKeep to avoid hacks like "LayerPin(lid, 0)"
std::set<LayerPin> pinsToKeep(blobsToKeep_.begin(), std::set<LayerPin> pinsToKeep(blobsToKeep_.begin(),
blobsToKeep_.end()); blobsToKeep_.end());
for (MapIdToLayerData::const_iterator it = layers.begin(); it != layers.end(); it++) for (MapIdToLayerData::const_iterator it = layers.begin(); it != layers.end(); it++)
@ -2993,6 +3030,13 @@ struct Net::Impl : public detail::NetImplBase
LayerPin lpNext(ld.consumers[0].lid, 0); LayerPin lpNext(ld.consumers[0].lid, 0);
while (nextData) while (nextData)
{ {
#ifdef HAVE_INF_ENGINE
if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && pinsToKeep.count(lpNext) != 0)
{
CV_LOG_DEBUG(NULL, "DNN/IE: skip fusing with 'output' node: " << nextData->name << "@" << nextData->type);
break;
}
#endif
/* we use `tryFuse` member of convolution layer to fuse eltwise later /* we use `tryFuse` member of convolution layer to fuse eltwise later
* it's not intended to be fused here; hence, we stop when we encounter eltwise * it's not intended to be fused here; hence, we stop when we encounter eltwise
*/ */

47
modules/dnn/src/ie_ngraph.cpp
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@ -330,7 +330,7 @@ public:
InfEngineNgraphNode::InfEngineNgraphNode(std::shared_ptr<ngraph::Node>&& _node) InfEngineNgraphNode::InfEngineNgraphNode(std::shared_ptr<ngraph::Node>&& _node)
: BackendNode(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH), node(std::move(_node)) {} : BackendNode(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH), node(std::move(_node)) {}
InfEngineNgraphNode::InfEngineNgraphNode(std::shared_ptr<ngraph::Node>& _node) InfEngineNgraphNode::InfEngineNgraphNode(const std::shared_ptr<ngraph::Node>& _node)
: BackendNode(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH), node(_node) {} : BackendNode(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH), node(_node) {}
InfEngineNgraphNode::InfEngineNgraphNode(const std::vector<Ptr<BackendNode> >& nodes, InfEngineNgraphNode::InfEngineNgraphNode(const std::vector<Ptr<BackendNode> >& nodes,
@ -379,16 +379,21 @@ InfEngineNgraphNet::InfEngineNgraphNet(detail::NetImplBase& netImpl, InferenceEn
device_name = "CPU"; device_name = "CPU";
} }
void InfEngineNgraphNet::addOutput(const std::string& name) void InfEngineNgraphNet::addOutput(const Ptr<InfEngineNgraphNode>& node)
{ {
requestedOutputs.push_back(name); CV_Assert(node);
CV_Assert(node->node);
const std::string& name = node->node->get_friendly_name();
requestedOutputs.insert({name, node});
} }
void InfEngineNgraphNet::setNodePtr(std::shared_ptr<ngraph::Node>* ptr) { void InfEngineNgraphNet::setNodePtr(std::shared_ptr<ngraph::Node>* ptr) {
all_nodes.emplace((*ptr)->get_friendly_name(), ptr); all_nodes.emplace((*ptr)->get_friendly_name(), ptr);
} }
void InfEngineNgraphNet::release() { void InfEngineNgraphNet::release()
{
// FIXIT release should not be conditional, release ALL
for (auto& node : components.back()) { for (auto& node : components.back()) {
#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2020_4) #if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2020_4)
if (!(ngraph::op::is_parameter(node) || ngraph::op::is_output(node) || ngraph::op::is_constant(node)) ) { if (!(ngraph::op::is_parameter(node) || ngraph::op::is_output(node) || ngraph::op::is_constant(node)) ) {
@ -397,7 +402,6 @@ void InfEngineNgraphNet::setNodePtr(std::shared_ptr<ngraph::Node>* ptr) {
#endif #endif
auto it = all_nodes.find(node->get_friendly_name()); auto it = all_nodes.find(node->get_friendly_name());
if (it != all_nodes.end()) { if (it != all_nodes.end()) {
unconnectedNodes.erase(*(it->second));
it->second->reset(); it->second->reset();
all_nodes.erase(it); all_nodes.erase(it);
} }
@ -422,7 +426,8 @@ void InfEngineNgraphNet::dfs(std::shared_ptr<ngraph::Node>& node,
} }
} }
int InfEngineNgraphNet::getNumComponents() { int InfEngineNgraphNet::getNumComponents()
{
if (!components.empty()) { if (!components.empty()) {
return components.size(); return components.size();
} }
@ -445,17 +450,21 @@ int InfEngineNgraphNet::getNumComponents() {
void InfEngineNgraphNet::createNet(Target targetId) { void InfEngineNgraphNet::createNet(Target targetId) {
if (!hasNetOwner) if (!hasNetOwner)
{ {
CV_Assert(!unconnectedNodes.empty()); CV_Assert(!requestedOutputs.empty());
ngraph::ResultVector outs; ngraph::ResultVector outs;
for (auto& node : unconnectedNodes)
for (auto output_node_it = requestedOutputs.begin(); output_node_it != requestedOutputs.end(); ++output_node_it)
{ {
auto out = std::make_shared<ngraph::op::Result>(node); CV_LOG_DEBUG(NULL, "DNN/NGRAPH: Add 'Result' output: " << output_node_it->first);
CV_Assert(output_node_it->second);
auto out = std::make_shared<ngraph::op::Result>(output_node_it->second->node);
outs.push_back(out); outs.push_back(out);
} }
CV_Assert_N(!inputs_vec.empty(), !outs.empty()); CV_Assert_N(!inputs_vec.empty(), !outs.empty());
ngraph_function = std::make_shared<ngraph::Function>(outs, inputs_vec); ngraph_function = std::make_shared<ngraph::Function>(outs, inputs_vec);
int num_comp = getNumComponents(); int num_comp = getNumComponents();
CV_LOG_DEBUG(NULL, "DNN/IE: number of subgraphs: " << num_comp);
if (num_comp > 1) { if (num_comp > 1) {
for (int i = num_comp - 1; i >= 0; --i) { for (int i = num_comp - 1; i >= 0; --i) {
ngraph::ResultVector outputs; ngraph::ResultVector outputs;
@ -466,6 +475,7 @@ void InfEngineNgraphNet::createNet(Target targetId) {
#else #else
if (node->is_parameter()) { if (node->is_parameter()) {
#endif #endif
CV_LOG_DEBUG(NULL, "DNN/IE: subgraph[" << i << "]: +input[" << inps.size() << "] = '" << node->get_friendly_name() << "'");
auto parameter = std::dynamic_pointer_cast<ngraph::op::Parameter>(node); auto parameter = std::dynamic_pointer_cast<ngraph::op::Parameter>(node);
inps.push_back(parameter); inps.push_back(parameter);
} }
@ -474,10 +484,12 @@ void InfEngineNgraphNet::createNet(Target targetId) {
#else #else
else if (node->is_output()) { else if (node->is_output()) {
#endif #endif
CV_LOG_DEBUG(NULL, "DNN/IE: subgraph[" << i << "]: +output[" << outputs.size() << "] = '" << node->get_friendly_name() << "'");
auto result = std::dynamic_pointer_cast<ngraph::op::Result>(node); auto result = std::dynamic_pointer_cast<ngraph::op::Result>(node);
outputs.push_back(result); outputs.push_back(result);
} }
} }
CV_LOG_DEBUG(NULL, "DNN/IE: subgraph[" << i << ": nodes=" << components.back().size() << " inputs=" << inps.size() << " outputs=" << outputs.size());
isInit = false; isInit = false;
CV_Assert_N(!inps.empty(), !outputs.empty()); CV_Assert_N(!inps.empty(), !outputs.empty());
ngraph_function = std::make_shared<ngraph::Function>(outputs, inps); ngraph_function = std::make_shared<ngraph::Function>(outputs, inps);
@ -574,7 +586,7 @@ void InfEngineNgraphNet::init(Target targetId)
auto node = ngraph_function->output(i).get_node(); auto node = ngraph_function->output(i).get_node();
for (size_t j = 0; j < node->get_input_size(); ++j) { for (size_t j = 0; j < node->get_input_size(); ++j) {
std::string name = node->input_value(j).get_node()->get_friendly_name(); std::string name = node->input_value(j).get_node()->get_friendly_name();
auto iter = std::find(requestedOutputs.begin(), requestedOutputs.end(), name); auto iter = requestedOutputs.find(name);
if (iter != requestedOutputs.end()) { if (iter != requestedOutputs.end()) {
requestedOutputs.erase(iter); requestedOutputs.erase(iter);
cnn.addOutput(name); cnn.addOutput(name);
@ -582,10 +594,6 @@ void InfEngineNgraphNet::init(Target targetId)
} }
} }
} }
for (const auto& name : requestedOutputs)
{
cnn.addOutput(name);
}
for (const auto& it : cnn.getInputsInfo()) for (const auto& it : cnn.getInputsInfo())
{ {
@ -630,9 +638,6 @@ ngraph::ParameterVector InfEngineNgraphNet::setInputs(const std::vector<cv::Mat>
return current_inp; return current_inp;
} }
void InfEngineNgraphNet::setUnconnectedNodes(Ptr<InfEngineNgraphNode>& node) {
unconnectedNodes.insert(node->node);
}
void InfEngineNgraphNet::initPlugin(InferenceEngine::CNNNetwork& net) void InfEngineNgraphNet::initPlugin(InferenceEngine::CNNNetwork& net)
{ {
@ -732,10 +737,10 @@ void InfEngineNgraphNet::initPlugin(InferenceEngine::CNNNetwork& net)
} }
} }
} }
if (isHetero)
netExec = ie.LoadNetwork(net, "HETERO:" + device_name + ",CPU", config); std::string ieDevice = isHetero ? ("HETERO:" + device_name + ",CPU") : device_name;
else CV_LOG_INFO(NULL, "DNN/IE: Calling LoadNetwork(device=" << ieDevice << ")...");
netExec = ie.LoadNetwork(net, device_name, config); netExec = ie.LoadNetwork(net, ieDevice, config);
} }
catch (const std::exception& ex) catch (const std::exception& ex)
{ {

8
modules/dnn/src/ie_ngraph.hpp
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@ -37,7 +37,7 @@ public:
InfEngineNgraphNet(detail::NetImplBase& netImpl); InfEngineNgraphNet(detail::NetImplBase& netImpl);
InfEngineNgraphNet(detail::NetImplBase& netImpl, InferenceEngine::CNNNetwork& net); InfEngineNgraphNet(detail::NetImplBase& netImpl, InferenceEngine::CNNNetwork& net);
void addOutput(const std::string& name); void addOutput(const Ptr<InfEngineNgraphNode>& node);
bool isInitialized(); bool isInitialized();
void init(Target targetId); void init(Target targetId);
@ -47,7 +47,6 @@ public:
void initPlugin(InferenceEngine::CNNNetwork& net); void initPlugin(InferenceEngine::CNNNetwork& net);
ngraph::ParameterVector setInputs(const std::vector<cv::Mat>& inputs, const std::vector<std::string>& names); ngraph::ParameterVector setInputs(const std::vector<cv::Mat>& inputs, const std::vector<std::string>& names);
void setUnconnectedNodes(Ptr<InfEngineNgraphNode>& node);
void addBlobs(const std::vector<cv::Ptr<BackendWrapper> >& ptrs); void addBlobs(const std::vector<cv::Ptr<BackendWrapper> >& ptrs);
void createNet(Target targetId); void createNet(Target targetId);
@ -88,8 +87,7 @@ public:
InferenceEngine::CNNNetwork cnn; InferenceEngine::CNNNetwork cnn;
bool hasNetOwner; bool hasNetOwner;
std::vector<std::string> requestedOutputs; std::unordered_map<std::string, Ptr<InfEngineNgraphNode> > requestedOutputs;
std::unordered_set<std::shared_ptr<ngraph::Node>> unconnectedNodes;
std::map<std::string, InferenceEngine::TensorDesc> outputsDesc; std::map<std::string, InferenceEngine::TensorDesc> outputsDesc;
}; };
@ -102,7 +100,7 @@ public:
std::vector<Mat>& internals); std::vector<Mat>& internals);
InfEngineNgraphNode(std::shared_ptr<ngraph::Node>&& _node); InfEngineNgraphNode(std::shared_ptr<ngraph::Node>&& _node);
InfEngineNgraphNode(std::shared_ptr<ngraph::Node>& _node); InfEngineNgraphNode(const std::shared_ptr<ngraph::Node>& _node);
void setName(const std::string& name); void setName(const std::string& name);

21
modules/dnn/test/test_onnx_conformance_layer_filter__openvino.inl.hpp
Unescape View File

@ -1771,26 +1771,11 @@ CASE(test_spacetodepth)
CASE(test_spacetodepth_example) CASE(test_spacetodepth_example)
// no filter // no filter
CASE(test_split_equal_parts_1d) CASE(test_split_equal_parts_1d)
#if INF_ENGINE_VER_MAJOR_EQ(2021040000) // no filter
SKIP_CPU;
// MYRIAD is ok
SKIP_OPENCL;
SKIP_OPENCL_FP16;
#endif
CASE(test_split_equal_parts_2d) CASE(test_split_equal_parts_2d)
#if INF_ENGINE_VER_MAJOR_EQ(2021040000) // no filter
SKIP_CPU;
// MYRIAD is ok
SKIP_OPENCL;
SKIP_OPENCL_FP16;
#endif
CASE(test_split_equal_parts_default_axis) CASE(test_split_equal_parts_default_axis)
#if INF_ENGINE_VER_MAJOR_EQ(2021040000) // no filter
SKIP_CPU;
// MYRIAD is ok
SKIP_OPENCL;
SKIP_OPENCL_FP16;
#endif
CASE(test_split_variable_parts_1d) CASE(test_split_variable_parts_1d)
// no filter // no filter
CASE(test_split_variable_parts_2d) CASE(test_split_variable_parts_2d)

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