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Merge pull request #11098 from dkurt:dnn_native_inf_engine

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pull/11187/head
Alexander Alekhin 7 years ago
parent 1097d0e810 2f3a9ba1d4
commit 167034fb04
10 changed files with 319 additions and 159 deletions
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  1. 17
      cmake/OpenCVDetectInferenceEngine.cmake
  2. 2
      modules/core/CMakeLists.txt
  3. 10
      modules/dnn/CMakeLists.txt
  4. 20
      modules/dnn/include/opencv2/dnn/dnn.hpp
  5. 51
      modules/dnn/perf/perf_net.cpp
  6. 124
      modules/dnn/src/dnn.cpp
  7. 143
      modules/dnn/src/op_inf_engine.cpp
  8. 36
      modules/dnn/src/op_inf_engine.hpp
  9. 54
      modules/dnn/test/test_backends.cpp
  10. 21
      modules/dnn/test/test_layers.cpp

17
cmake/OpenCVDetectInferenceEngine.cmake
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@ -46,9 +46,13 @@ endif()
set(INF_ENGINE_LIBRARIES "")
set(ie_lib_list inference_engine)
if(UNIX)
list(APPEND ie_lib_list mklml_intel iomp5)
endif()
link_directories(
${INTEL_CVSDK_DIR}/external/mklml_lnx/lib
${INTEL_CVSDK_DIR}/inference_engine/external/mklml_lnx/lib
${INTEL_CVSDK_DIR}/external/cldnn/lib
${INTEL_CVSDK_DIR}/inference_engine/external/cldnn/lib
)
foreach(lib ${ie_lib_list})
find_library(${lib}
@ -56,9 +60,6 @@ foreach(lib ${ie_lib_list})
# For inference_engine
HINTS ${IE_PLUGINS_PATH}
HINTS "$ENV{IE_PLUGINS_PATH}"
# For mklml_intel, iomp5
HINTS ${INTEL_CVSDK_DIR}/external/mklml_lnx/lib
HINTS ${INTEL_CVSDK_DIR}/inference_engine/external/mklml_lnx/lib
)
if(NOT ${lib})
ie_fail()
@ -67,7 +68,3 @@ foreach(lib ${ie_lib_list})
endforeach()
set(HAVE_INF_ENGINE TRUE)
include_directories(${INF_ENGINE_INCLUDE_DIRS})
list(APPEND OPENCV_LINKER_LIBS ${INF_ENGINE_LIBRARIES})
add_definitions(-DHAVE_INF_ENGINE)

2
modules/core/CMakeLists.txt
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@ -59,7 +59,7 @@ ocv_create_module(${extra_libs})
ocv_target_link_libraries(${the_module} LINK_PRIVATE
"${ZLIB_LIBRARIES}" "${OPENCL_LIBRARIES}" "${VA_LIBRARIES}"
"${LAPACK_LIBRARIES}" "${CPUFEATURES_LIBRARIES}" "${HALIDE_LIBRARIES}" "${INF_ENGINE_LIBRARIES}"
"${LAPACK_LIBRARIES}" "${CPUFEATURES_LIBRARIES}" "${HALIDE_LIBRARIES}"
"${ITT_LIBRARIES}"
"${OPENCV_HAL_LINKER_LIBS}"
)

10
modules/dnn/CMakeLists.txt
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@ -73,14 +73,22 @@ endif()
set(include_dirs ${fw_inc})
set(sources_options "")
set(libs libprotobuf ${LAPACK_LIBRARIES})
if(OPENCV_DNN_OPENCL AND HAVE_OPENCL)
list(APPEND include_dirs ${OPENCL_INCLUDE_DIRS})
else()
set(sources_options EXCLUDE_OPENCL)
endif()
if(WITH_INF_ENGINE AND HAVE_INF_ENGINE)
add_definitions(-DHAVE_INF_ENGINE=1)
list(APPEND include_dirs ${INF_ENGINE_INCLUDE_DIRS})
list(APPEND libs ${INF_ENGINE_LIBRARIES})
endif()
ocv_module_include_directories(${include_dirs})
ocv_glob_module_sources(${sources_options} SOURCES ${fw_srcs})
ocv_create_module(libprotobuf ${LAPACK_LIBRARIES})
ocv_create_module(${libs})
ocv_add_samples()
ocv_add_accuracy_tests()
ocv_add_perf_tests()

20
modules/dnn/include/opencv2/dnn/dnn.hpp
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@ -341,6 +341,14 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
CV_WRAP Net(); //!< Default constructor.
CV_WRAP ~Net(); //!< Destructor frees the net only if there aren't references to the net anymore.
/** @brief Create a network from Intel's Model Optimizer intermediate representation.
* @param[in] xml XML configuration file with network's topology.
* @param[in] bin Binary file with trained weights.
* Networks imported from Intel's Model Optimizer are lauched in Intel's Inference Engine
* backend.
*/
CV_WRAP static Net readFromModelOptimizer(const String& xml, const String& bin);
/** Returns true if there are no layers in the network. */
CV_WRAP bool empty() const;
@ -691,11 +699,13 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
* * `*.pb` (TensorFlow, https://www.tensorflow.org/)
* * `*.t7` | `*.net` (Torch, http://torch.ch/)
* * `*.weights` (Darknet, https://pjreddie.com/darknet/)
* * `*.bin` (DLDT, https://software.seek.intel.com/deep-learning-deployment)
* @param[in] config Text file contains network configuration. It could be a
* file with the following extensions:
* * `*.prototxt` (Caffe, http://caffe.berkeleyvision.org/)
* * `*.pbtxt` (TensorFlow, https://www.tensorflow.org/)
* * `*.cfg` (Darknet, https://pjreddie.com/darknet/)
* * `*.xml` (DLDT, https://software.seek.intel.com/deep-learning-deployment)
* @param[in] framework Explicit framework name tag to determine a format.
* @returns Net object.
*
@ -710,6 +720,16 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
* @warning This function has the same limitations as readNetFromTorch().
*/
CV_EXPORTS_W Mat readTorchBlob(const String &filename, bool isBinary = true);
/** @brief Load a network from Intel's Model Optimizer intermediate representation.
* @param[in] xml XML configuration file with network's topology.
* @param[in] bin Binary file with trained weights.
* @returns Net object.
* Networks imported from Intel's Model Optimizer are lauched in Intel's Inference Engine
* backend.
*/
CV_EXPORTS_W Net readNetFromModelOptimizer(const String &xml, const String &bin);
/** @brief Creates 4-dimensional blob from image. Optionally resizes and crops @p image from center,
* subtract @p mean values, scales values by @p scalefactor, swap Blue and Red channels.
* @param image input image (with 1-, 3- or 4-channels).

51
modules/dnn/perf/perf_net.cpp
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@ -30,8 +30,7 @@ public:
}
void processNet(std::string weights, std::string proto, std::string halide_scheduler,
const Mat& input, const std::string& outputLayer,
const std::string& framework)
const Mat& input, const std::string& outputLayer = "")
{
if (backend == DNN_BACKEND_DEFAULT && target == DNN_TARGET_OPENCL)
{
@ -57,21 +56,7 @@ public:
if (!halide_scheduler.empty())
halide_scheduler = findDataFile(std::string("dnn/halide_scheduler_") + (target == DNN_TARGET_OPENCL ? "opencl_" : "") + halide_scheduler, true);
}
if (framework == "caffe")
{
net = cv::dnn::readNetFromCaffe(proto, weights);
}
else if (framework == "torch")
{
net = cv::dnn::readNetFromTorch(weights);
}
else if (framework == "tensorflow")
{
net = cv::dnn::readNetFromTensorflow(weights, proto);
}
else
CV_Error(Error::StsNotImplemented, "Unknown framework " + framework);
net = readNet(proto, weights);
net.setInput(blobFromImage(input, 1.0, Size(), Scalar(), false));
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
@ -105,25 +90,25 @@ public:
PERF_TEST_P_(DNNTestNetwork, AlexNet)
{
processNet("dnn/bvlc_alexnet.caffemodel", "dnn/bvlc_alexnet.prototxt",
"alexnet.yml", Mat(cv::Size(227, 227), CV_32FC3), "prob", "caffe");
"alexnet.yml", Mat(cv::Size(227, 227), CV_32FC3));
}
PERF_TEST_P_(DNNTestNetwork, GoogLeNet)
{
processNet("dnn/bvlc_googlenet.caffemodel", "dnn/bvlc_googlenet.prototxt",
"", Mat(cv::Size(224, 224), CV_32FC3), "prob", "caffe");
"", Mat(cv::Size(224, 224), CV_32FC3));
}
PERF_TEST_P_(DNNTestNetwork, ResNet_50)
{
processNet("dnn/ResNet-50-model.caffemodel", "dnn/ResNet-50-deploy.prototxt",
"resnet_50.yml", Mat(cv::Size(224, 224), CV_32FC3), "prob", "caffe");
"resnet_50.yml", Mat(cv::Size(224, 224), CV_32FC3));
}
PERF_TEST_P_(DNNTestNetwork, SqueezeNet_v1_1)
{
processNet("dnn/squeezenet_v1.1.caffemodel", "dnn/squeezenet_v1.1.prototxt",
"squeezenet_v1_1.yml", Mat(cv::Size(227, 227), CV_32FC3), "prob", "caffe");
"squeezenet_v1_1.yml", Mat(cv::Size(227, 227), CV_32FC3));
}
PERF_TEST_P_(DNNTestNetwork, Inception_5h)
@ -131,35 +116,35 @@ PERF_TEST_P_(DNNTestNetwork, Inception_5h)
if (backend == DNN_BACKEND_INFERENCE_ENGINE) throw SkipTestException("");
processNet("dnn/tensorflow_inception_graph.pb", "",
"inception_5h.yml",
Mat(cv::Size(224, 224), CV_32FC3), "softmax2", "tensorflow");
Mat(cv::Size(224, 224), CV_32FC3), "softmax2");
}
PERF_TEST_P_(DNNTestNetwork, ENet)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE) throw SkipTestException("");
processNet("dnn/Enet-model-best.net", "", "enet.yml",
Mat(cv::Size(512, 256), CV_32FC3), "l367_Deconvolution", "torch");
Mat(cv::Size(512, 256), CV_32FC3));
}
PERF_TEST_P_(DNNTestNetwork, SSD)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE) throw SkipTestException("");
processNet("dnn/VGG_ILSVRC2016_SSD_300x300_iter_440000.caffemodel", "dnn/ssd_vgg16.prototxt", "disabled",
Mat(cv::Size(300, 300), CV_32FC3), "detection_out", "caffe");
Mat(cv::Size(300, 300), CV_32FC3));
}
PERF_TEST_P_(DNNTestNetwork, OpenFace)
{
if (backend == DNN_BACKEND_HALIDE) throw SkipTestException("");
processNet("dnn/openface_nn4.small2.v1.t7", "", "",
Mat(cv::Size(96, 96), CV_32FC3), "", "torch");
Mat(cv::Size(96, 96), CV_32FC3));
}
PERF_TEST_P_(DNNTestNetwork, MobileNet_SSD_Caffe)
{
if (backend == DNN_BACKEND_HALIDE) throw SkipTestException("");
processNet("dnn/MobileNetSSD_deploy.caffemodel", "dnn/MobileNetSSD_deploy.prototxt", "",
Mat(cv::Size(300, 300), CV_32FC3), "detection_out", "caffe");
Mat(cv::Size(300, 300), CV_32FC3));
}
PERF_TEST_P_(DNNTestNetwork, MobileNet_SSD_TensorFlow)
@ -168,28 +153,28 @@ PERF_TEST_P_(DNNTestNetwork, MobileNet_SSD_TensorFlow)
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");
Mat(cv::Size(300, 300), CV_32FC3));
}
PERF_TEST_P_(DNNTestNetwork, DenseNet_121)
{
if (backend == DNN_BACKEND_HALIDE) throw SkipTestException("");
processNet("dnn/DenseNet_121.caffemodel", "dnn/DenseNet_121.prototxt", "",
Mat(cv::Size(224, 224), CV_32FC3), "", "caffe");
Mat(cv::Size(224, 224), CV_32FC3));
}
PERF_TEST_P_(DNNTestNetwork, OpenPose_pose_coco)
{
if (backend == DNN_BACKEND_HALIDE) throw SkipTestException("");
processNet("dnn/openpose_pose_coco.caffemodel", "dnn/openpose_pose_coco.prototxt", "",
Mat(cv::Size(368, 368), CV_32FC3), "", "caffe");
Mat(cv::Size(368, 368), CV_32FC3));
}
PERF_TEST_P_(DNNTestNetwork, OpenPose_pose_mpi)
{
if (backend == DNN_BACKEND_HALIDE) throw SkipTestException("");
processNet("dnn/openpose_pose_mpi.caffemodel", "dnn/openpose_pose_mpi.prototxt", "",
Mat(cv::Size(368, 368), CV_32FC3), "", "caffe");
Mat(cv::Size(368, 368), CV_32FC3));
}
PERF_TEST_P_(DNNTestNetwork, OpenPose_pose_mpi_faster_4_stages)
@ -198,7 +183,7 @@ PERF_TEST_P_(DNNTestNetwork, OpenPose_pose_mpi_faster_4_stages)
// 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");
Mat(cv::Size(368, 368), CV_32FC3));
}
PERF_TEST_P_(DNNTestNetwork, opencv_face_detector)
@ -207,14 +192,14 @@ PERF_TEST_P_(DNNTestNetwork, opencv_face_detector)
backend == DNN_BACKEND_DEFAULT && target == DNN_TARGET_OPENCL)
throw SkipTestException("");
processNet("dnn/opencv_face_detector.caffemodel", "dnn/opencv_face_detector.prototxt", "",
Mat(cv::Size(300, 300), CV_32FC3), "", "caffe");
Mat(cv::Size(300, 300), CV_32FC3));
}
PERF_TEST_P_(DNNTestNetwork, Inception_v2_SSD_TensorFlow)
{
if (backend == DNN_BACKEND_HALIDE) throw SkipTestException("");
processNet("dnn/ssd_inception_v2_coco_2017_11_17.pb", "ssd_inception_v2_coco_2017_11_17.pbtxt", "",
Mat(cv::Size(300, 300), CV_32FC3), "", "tensorflow");
Mat(cv::Size(300, 300), CV_32FC3));
}
const tuple<DNNBackend, DNNTarget> testCases[] = {

124
modules/dnn/src/dnn.cpp
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@ -420,7 +420,6 @@ struct DataLayer : public Layer
return false;
}
private:
std::vector<String> outNames;
};
@ -700,10 +699,10 @@ struct Net::Impl
fusion = true;
preferableBackend = DNN_BACKEND_DEFAULT;
preferableTarget = DNN_TARGET_CPU;
skipInfEngineInit = false;
}
Ptr<DataLayer> netInputLayer;
std::vector<int> netOutputs;
std::vector<LayerPin> blobsToKeep;
MapIdToLayerData layers;
std::map<String, int> layerNameToId;
@ -711,6 +710,7 @@ struct Net::Impl
int preferableBackend;
int preferableTarget;
String halideConfigFile;
bool skipInfEngineInit;
// Map host data to backend specific wrapper.
std::map<void*, Ptr<BackendWrapper> > backendWrappers;
@ -857,7 +857,6 @@ struct Net::Impl
clear();
allocateLayers(blobsToKeep_);
computeNetOutputLayers();
initBackend();
if (!netWasAllocated )
@ -1019,29 +1018,6 @@ struct Net::Impl
ldOut.consumers.push_back(LayerPin(inLayerId, outNum));
}
void computeNetOutputLayers()
{
CV_TRACE_FUNCTION();
netOutputs.clear();
MapIdToLayerData::iterator it;
for (it = layers.begin(); it != layers.end(); it++)
{
int lid = it->first;
LayerData &ld = it->second;
if (ld.requiredOutputs.size() == 0)
netOutputs.push_back(lid);
}
#ifndef NDEBUG
std::cout << "\nNet Outputs(" << netOutputs.size() << "):\n";
for (size_t i = 0; i < netOutputs.size(); i++)
std::cout << layers[netOutputs[i]].name << "\n";
#endif
}
void initBackend()
{
CV_TRACE_FUNCTION();
@ -1150,14 +1126,42 @@ struct Net::Impl
void initInfEngineBackend()
{
// Build Inference Engine networks from sets of layers that support this
// backend. Split a whole model on several Inference Engine networks if
// some of layers is not implemented.
CV_TRACE_FUNCTION();
CV_Assert(preferableBackend == DNN_BACKEND_INFERENCE_ENGINE, haveInfEngine());
#ifdef HAVE_INF_ENGINE
MapIdToLayerData::iterator it;
Ptr<InfEngineBackendNet> net;
if (skipInfEngineInit)
{
Ptr<BackendNode> node = layers[lastLayerId].backendNodes[preferableBackend];
CV_Assert(!node.empty());
Ptr<InfEngineBackendNode> ieNode = node.dynamicCast<InfEngineBackendNode>();
CV_Assert(!ieNode.empty());
for (it = layers.begin(); it != layers.end(); ++it)
{
LayerData &ld = it->second;
for (int i = 0; i < ld.outputBlobsWrappers.size(); ++i)
{
InferenceEngine::DataPtr dataPtr = infEngineDataNode(ld.outputBlobsWrappers[i]);
dataPtr->name = ld.id == 0 ? netInputLayer->outNames[i] : ld.name;
}
ieNode->net->addBlobs(ld.inputBlobsWrappers);
ieNode->net->addBlobs(ld.outputBlobsWrappers);
ld.skip = true;
}
layers[lastLayerId].skip = false;
ieNode->net->init();
return;
}
// Build Inference Engine networks from sets of layers that support this
// backend. Split a whole model on several Inference Engine networks if
// some of layers is not implemented.
// Set of all input and output blobs wrappers for current network.
std::map<int, Ptr<BackendWrapper> > netBlobsWrappers;
for (it = layers.begin(); it != layers.end(); ++it)
@ -1272,7 +1276,7 @@ struct Net::Impl
if (!ieNode->net->isInitialized())
{
ieNode->net->initEngine();
ieNode->net->init();
ld.skip = false;
}
}
@ -1383,7 +1387,6 @@ struct Net::Impl
// 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
std::vector<String> outnames;
std::set<LayerPin> pinsToKeep(blobsToKeep_.begin(),
blobsToKeep_.end());
MapIdToLayerData::iterator it;
@ -1397,8 +1400,6 @@ struct Net::Impl
continue;
}
printf_(("analyzing %s: %s\n", ld.layerInstance->name.c_str(), ld.layerInstance->type.c_str()));
if( ld.consumers.size() == 0 )
outnames.push_back(ld.layerInstance->name);
// the optimization #1. try to fuse batch norm, scaling and/or activation layers
// with the current layer if they follow it. Normally, the are fused with the convolution layer,
@ -1912,6 +1913,46 @@ Net::Net() : impl(new Net::Impl)
{
}
Net Net::readFromModelOptimizer(const String& xml, const String& bin)
{
#ifndef HAVE_INF_ENGINE
CV_ErrorNoReturn(Error::StsError, "Build OpenCV with Inference Engine to enable loading models from Model Optimizer.");
#else
InferenceEngine::CNNNetReader reader;
reader.ReadNetwork(xml);
reader.ReadWeights(bin);
InferenceEngine::CNNNetwork ieNet = reader.getNetwork();
std::vector<String> inputsNames;
for (auto& it : ieNet.getInputsInfo())
{
inputsNames.push_back(it.first);
}
Net cvNet;
cvNet.setInputsNames(inputsNames);
Ptr<InfEngineBackendNode> backendNode(new InfEngineBackendNode(0));
backendNode->net = Ptr<InfEngineBackendNet>(new InfEngineBackendNet(ieNet));
for (auto& it : ieNet.getOutputsInfo())
{
LayerParams lp;
int lid = cvNet.addLayer(it.first, "", lp);
LayerData& ld = cvNet.impl->layers[lid];
ld.layerInstance = Ptr<Layer>(new InfEngineBackendLayer(it.second));
ld.backendNodes[DNN_BACKEND_INFERENCE_ENGINE] = backendNode;
cvNet.connect(0, 0, lid, 0);
}
cvNet.setPreferableBackend(DNN_BACKEND_INFERENCE_ENGINE);
cvNet.impl->skipInfEngineInit = true;
return cvNet;
#endif // HAVE_INF_ENGINE
}
Net::~Net()
{
}
@ -2846,9 +2887,20 @@ Net readNet(const String& _model, const String& _config, const String& _framewor
std::swap(model, config);
return readNetFromDarknet(config, model);
}
CV_Error(Error::StsError, "Cannot determine an origin framework of files: " +
model + (config.empty() ? "" : ", " + config));
return Net();
if (framework == "dldt" || modelExt == "bin" || configExt == "bin" ||
modelExt == "xml" || configExt == "xml")
{
if (modelExt == "xml" || configExt == "bin")
std::swap(model, config);
return readNetFromModelOptimizer(config, model);
}
CV_ErrorNoReturn(Error::StsError, "Cannot determine an origin framework of files: " +
model + (config.empty() ? "" : ", " + config));
}
Net readNetFromModelOptimizer(const String &xml, const String &bin)
{
return Net::readFromModelOptimizer(xml, bin);
}
CV__DNN_EXPERIMENTAL_NS_END

143
modules/dnn/src/op_inf_engine.cpp
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@ -102,6 +102,18 @@ void InfEngineBackendWrapper::setHostDirty()
}
InfEngineBackendNet::InfEngineBackendNet()
{
}
InfEngineBackendNet::InfEngineBackendNet(InferenceEngine::CNNNetwork& net)
{
inputs = net.getInputsInfo();
outputs = net.getOutputsInfo();
layers.resize(net.layerCount()); // A hack to execute InfEngineBackendNet::layerCount correctly.
initPlugin(net);
}
void InfEngineBackendNet::Release() noexcept
{
layers.clear();
@ -213,57 +225,62 @@ size_t InfEngineBackendNet::getBatchSize() const noexcept
return 0;
}
void InfEngineBackendNet::initEngine()
void InfEngineBackendNet::init()
{
CV_Assert(!isInitialized(), !layers.empty());
// Collect all external input blobs.
std::map<std::string, InferenceEngine::DataPtr> internalOutputs;
for (const auto& l : layers)
if (inputs.empty())
{
for (const InferenceEngine::DataWeakPtr& ptr : l->insData)
// Collect all external input blobs.
inputs.clear();
std::map<std::string, InferenceEngine::DataPtr> internalOutputs;
for (const auto& l : layers)
{
InferenceEngine::DataPtr inp(ptr);
if (internalOutputs.find(inp->name) == internalOutputs.end())
for (const InferenceEngine::DataWeakPtr& ptr : l->insData)
{
InferenceEngine::InputInfo::Ptr inpInfo(new InferenceEngine::InputInfo());
inpInfo->setInputData(inp);
if (inputs.find(inp->name) == inputs.end())
inputs[inp->name] = inpInfo;
InferenceEngine::DataPtr inp(ptr);
if (internalOutputs.find(inp->name) == internalOutputs.end())
{
InferenceEngine::InputInfo::Ptr inpInfo(new InferenceEngine::InputInfo());
inpInfo->setInputData(inp);
if (inputs.find(inp->name) == inputs.end())
inputs[inp->name] = inpInfo;
}
}
for (const InferenceEngine::DataPtr& out : l->outData)
{
// TODO: Replace to uniquness assertion.
if (internalOutputs.find(out->name) == internalOutputs.end())
internalOutputs[out->name] = out;
}
}
for (const InferenceEngine::DataPtr& out : l->outData)
{
// TODO: Replace to uniquness assertion.
if (internalOutputs.find(out->name) == internalOutputs.end())
internalOutputs[out->name] = out;
}
CV_Assert(!inputs.empty());
}
CV_Assert(!inputs.empty());
// Add all unconnected blobs to output blobs.
InferenceEngine::OutputsDataMap unconnectedOuts;
for (const auto& l : layers)
if (outputs.empty())
{
// Add all outputs.
for (const InferenceEngine::DataPtr& out : l->outData)
// Add all unconnected blobs to output blobs.
InferenceEngine::OutputsDataMap unconnectedOuts;
for (const auto& l : layers)
{
// TODO: Replace to uniquness assertion.
if (unconnectedOuts.find(out->name) == unconnectedOuts.end())
unconnectedOuts[out->name] = out;
// Add all outputs.
for (const InferenceEngine::DataPtr& out : l->outData)
{
// TODO: Replace to uniquness assertion.
if (unconnectedOuts.find(out->name) == unconnectedOuts.end())
unconnectedOuts[out->name] = out;
}
// Remove internally connected outputs.
for (const InferenceEngine::DataWeakPtr& inp : l->insData)
{
unconnectedOuts.erase(InferenceEngine::DataPtr(inp)->name);
}
}
// Remove internally connected outputs.
for (const InferenceEngine::DataWeakPtr& inp : l->insData)
CV_Assert(!unconnectedOuts.empty());
for (auto it = unconnectedOuts.begin(); it != unconnectedOuts.end(); ++it)
{
unconnectedOuts.erase(InferenceEngine::DataPtr(inp)->name);
outputs[it->first] = it->second;
}
}
CV_Assert(!unconnectedOuts.empty());
for (auto it = unconnectedOuts.begin(); it != unconnectedOuts.end(); ++it)
{
outputs[it->first] = it->second;
}
// Set up input blobs.
inpBlobs.clear();
@ -281,20 +298,27 @@ void InfEngineBackendNet::initEngine()
outBlobs[it.first] = allBlobs[it.first];
}
if (!isInitialized())
initPlugin(*this);
}
void InfEngineBackendNet::initPlugin(InferenceEngine::ICNNNetwork& net)
{
CV_Assert(!isInitialized());
#ifdef _WIN32
engine = InferenceEngine::InferenceEnginePluginPtr("MKLDNNPlugin.dll");
plugin = InferenceEngine::InferenceEnginePluginPtr("MKLDNNPlugin.dll");
#else
engine = InferenceEngine::InferenceEnginePluginPtr("libMKLDNNPlugin.so");
plugin = InferenceEngine::InferenceEnginePluginPtr("libMKLDNNPlugin.so");
#endif // _WIN32
InferenceEngine::ResponseDesc resp;
InferenceEngine::StatusCode status = engine->LoadNetwork(*this, &resp);
InferenceEngine::StatusCode status = plugin->LoadNetwork(net, &resp);
if (status != InferenceEngine::StatusCode::OK)
CV_Error(Error::StsAssert, resp.msg);
}
bool InfEngineBackendNet::isInitialized()
{
return (bool)engine;
return (bool)plugin;
}
void InfEngineBackendNet::addBlobs(const std::vector<Ptr<BackendWrapper> >& ptrs)
@ -309,7 +333,7 @@ void InfEngineBackendNet::addBlobs(const std::vector<Ptr<BackendWrapper> >& ptrs
void InfEngineBackendNet::forward()
{
InferenceEngine::ResponseDesc resp;
InferenceEngine::StatusCode status = engine->Infer(inpBlobs, outBlobs, &resp);
InferenceEngine::StatusCode status = plugin->Infer(inpBlobs, outBlobs, &resp);
if (status != InferenceEngine::StatusCode::OK)
CV_Error(Error::StsAssert, resp.msg);
}
@ -373,6 +397,41 @@ void fuseConvWeights(const std::shared_ptr<InferenceEngine::ConvolutionLayer>& c
conv->_biases = wrapToInfEngineBlob(b);
}
InfEngineBackendLayer::InfEngineBackendLayer(const InferenceEngine::DataPtr& output_)
{
output = output_;
}
bool InfEngineBackendLayer::getMemoryShapes(const std::vector<MatShape> &inputs,
const int requiredOutputs,
std::vector<MatShape> &outputs,
std::vector<MatShape> &internals) const
{
std::vector<size_t> dims = output->dims;
std::vector<int> shape(dims.begin(), dims.end());
std::reverse(shape.begin(), shape.end());
outputs.assign(1, shape);
return false;
}
bool InfEngineBackendLayer::supportBackend(int backendId)
{
return backendId == DNN_BACKEND_DEFAULT ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine();
}
void InfEngineBackendLayer::forward(std::vector<Mat*> &input, std::vector<Mat> &output,
std::vector<Mat> &internals)
{
CV_Error(Error::StsError, "Choose Inference Engine as a preferable backend.");
}
void InfEngineBackendLayer::forward(InputArrayOfArrays inputs, OutputArrayOfArrays outputs,
OutputArrayOfArrays internals)
{
CV_Error(Error::StsInternal, "Choose Inference Engine as a preferable backend.");
}
#endif // HAVE_INF_ENGINE
bool haveInfEngine()

36
modules/dnn/src/op_inf_engine.hpp
Unescape View File

@ -19,6 +19,10 @@ namespace cv { namespace dnn {
class InfEngineBackendNet : public InferenceEngine::ICNNNetwork
{
public:
InfEngineBackendNet();
InfEngineBackendNet(InferenceEngine::CNNNetwork& net);
virtual void Release() noexcept;
virtual InferenceEngine::Precision getPrecision() noexcept;
@ -55,7 +59,7 @@ public:
virtual size_t getBatchSize() const noexcept;
void initEngine();
void init();
void addBlobs(const std::vector<Ptr<BackendWrapper> >& wrappers);
@ -70,7 +74,9 @@ private:
InferenceEngine::BlobMap inpBlobs;
InferenceEngine::BlobMap outBlobs;
InferenceEngine::BlobMap allBlobs;
InferenceEngine::InferenceEnginePluginPtr engine;
InferenceEngine::InferenceEnginePluginPtr plugin;
void initPlugin(InferenceEngine::ICNNNetwork& net);
};
class InfEngineBackendNode : public BackendNode
@ -111,6 +117,32 @@ InferenceEngine::DataPtr infEngineDataNode(const Ptr<BackendWrapper>& ptr);
void fuseConvWeights(const std::shared_ptr<InferenceEngine::ConvolutionLayer>& conv,
const Mat& w, const Mat& b = Mat());
// This is a fake class to run networks from Model Optimizer. Objects of that
// class simulate responses of layers are imported by OpenCV and supported by
// Inference Engine. The main difference is that they do not perform forward pass.
class InfEngineBackendLayer : public Layer
{
public:
InfEngineBackendLayer(const InferenceEngine::DataPtr& output);
virtual bool getMemoryShapes(const std::vector<MatShape> &inputs,
const int requiredOutputs,
std::vector<MatShape> &outputs,
std::vector<MatShape> &internals) const;
virtual void forward(std::vector<Mat*> &input, std::vector<Mat> &output,
std::vector<Mat> &internals);
virtual void forward(InputArrayOfArrays inputs, OutputArrayOfArrays outputs,
OutputArrayOfArrays internals);
virtual bool supportBackend(int backendId);
private:
InferenceEngine::DataPtr output;
};
#endif // HAVE_INF_ENGINE
bool haveInfEngine();

54
modules/dnn/test/test_backends.cpp
Unescape View File

@ -10,19 +10,6 @@
namespace opencv_test { namespace {
static void loadNet(const std::string& weights, const std::string& proto,
const std::string& framework, Net* net)
{
if (framework == "caffe")
*net = cv::dnn::readNetFromCaffe(proto, weights);
else if (framework == "torch")
*net = cv::dnn::readNetFromTorch(weights);
else if (framework == "tensorflow")
*net = cv::dnn::readNetFromTensorflow(weights, proto);
else
CV_Error(Error::StsNotImplemented, "Unknown framework " + framework);
}
class DNNTestNetwork : public TestWithParam <tuple<DNNBackend, DNNTarget> >
{
public:
@ -37,7 +24,7 @@ public:
void processNet(const std::string& weights, const std::string& proto,
Size inpSize, const std::string& outputLayer,
const std::string& framework, const std::string& halideScheduler = "",
const std::string& halideScheduler = "",
double l1 = 1e-5, double lInf = 1e-4)
{
// Create a common input blob.
@ -45,12 +32,12 @@ public:
Mat inp(4, blobSize, CV_32FC1);
randu(inp, 0.0f, 1.0f);
processNet(weights, proto, inp, outputLayer, framework, halideScheduler, l1, lInf);
processNet(weights, proto, inp, outputLayer, halideScheduler, l1, lInf);
}
void processNet(std::string weights, std::string proto,
Mat inp, const std::string& outputLayer,
const std::string& framework, std::string halideScheduler = "",
std::string halideScheduler = "",
double l1 = 1e-5, double lInf = 1e-4)
{
if (backend == DNN_BACKEND_DEFAULT && target == DNN_TARGET_OPENCL)
@ -67,9 +54,8 @@ public:
proto = findDataFile(proto, false);
// Create two networks - with default backend and target and a tested one.
Net netDefault, net;
loadNet(weights, proto, framework, &netDefault);
loadNet(weights, proto, framework, &net);
Net netDefault = readNet(weights, proto);
Net net = readNet(weights, proto);
netDefault.setInput(inp);
Mat outDefault = netDefault.forward(outputLayer).clone();
@ -115,7 +101,7 @@ public:
TEST_P(DNNTestNetwork, AlexNet)
{
processNet("dnn/bvlc_alexnet.caffemodel", "dnn/bvlc_alexnet.prototxt",
Size(227, 227), "prob", "caffe",
Size(227, 227), "prob",
target == DNN_TARGET_OPENCL ? "dnn/halide_scheduler_opencl_alexnet.yml" :
"dnn/halide_scheduler_alexnet.yml");
}
@ -123,7 +109,7 @@ TEST_P(DNNTestNetwork, AlexNet)
TEST_P(DNNTestNetwork, ResNet_50)
{
processNet("dnn/ResNet-50-model.caffemodel", "dnn/ResNet-50-deploy.prototxt",
Size(224, 224), "prob", "caffe",
Size(224, 224), "prob",
target == DNN_TARGET_OPENCL ? "dnn/halide_scheduler_opencl_resnet_50.yml" :
"dnn/halide_scheduler_resnet_50.yml");
}
@ -131,7 +117,7 @@ TEST_P(DNNTestNetwork, ResNet_50)
TEST_P(DNNTestNetwork, SqueezeNet_v1_1)
{
processNet("dnn/squeezenet_v1.1.caffemodel", "dnn/squeezenet_v1.1.prototxt",
Size(227, 227), "prob", "caffe",
Size(227, 227), "prob",
target == DNN_TARGET_OPENCL ? "dnn/halide_scheduler_opencl_squeezenet_v1_1.yml" :
"dnn/halide_scheduler_squeezenet_v1_1.yml");
}
@ -139,13 +125,13 @@ TEST_P(DNNTestNetwork, SqueezeNet_v1_1)
TEST_P(DNNTestNetwork, GoogLeNet)
{
processNet("dnn/bvlc_googlenet.caffemodel", "dnn/bvlc_googlenet.prototxt",
Size(224, 224), "prob", "caffe");
Size(224, 224), "prob");
}
TEST_P(DNNTestNetwork, Inception_5h)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE) throw SkipTestException("");
processNet("dnn/tensorflow_inception_graph.pb", "", Size(224, 224), "softmax2", "tensorflow",
processNet("dnn/tensorflow_inception_graph.pb", "", Size(224, 224), "softmax2",
target == DNN_TARGET_OPENCL ? "dnn/halide_scheduler_opencl_inception_5h.yml" :
"dnn/halide_scheduler_inception_5h.yml");
}
@ -153,7 +139,7 @@ TEST_P(DNNTestNetwork, Inception_5h)
TEST_P(DNNTestNetwork, ENet)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE) throw SkipTestException("");
processNet("dnn/Enet-model-best.net", "", Size(512, 512), "l367_Deconvolution", "torch",
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);
@ -166,7 +152,7 @@ TEST_P(DNNTestNetwork, MobileNet_SSD_Caffe)
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", "caffe");
inp, "detection_out");
}
TEST_P(DNNTestNetwork, MobileNet_SSD_TensorFlow)
@ -175,7 +161,7 @@ TEST_P(DNNTestNetwork, MobileNet_SSD_TensorFlow)
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_mobilenet_v1_coco.pb", "dnn/ssd_mobilenet_v1_coco.pbtxt",
inp, "detection_out", "tensorflow");
inp, "detection_out");
}
TEST_P(DNNTestNetwork, SSD_VGG16)
@ -185,21 +171,21 @@ TEST_P(DNNTestNetwork, SSD_VGG16)
backend == DNN_BACKEND_INFERENCE_ENGINE)
throw SkipTestException("");
processNet("dnn/VGG_ILSVRC2016_SSD_300x300_iter_440000.caffemodel",
"dnn/ssd_vgg16.prototxt", Size(300, 300), "detection_out", "caffe");
"dnn/ssd_vgg16.prototxt", Size(300, 300), "detection_out");
}
TEST_P(DNNTestNetwork, OpenPose_pose_coco)
{
if (backend == DNN_BACKEND_HALIDE) throw SkipTestException("");
processNet("dnn/openpose_pose_coco.caffemodel", "dnn/openpose_pose_coco.prototxt",
Size(368, 368), "", "caffe");
Size(368, 368), "");
}
TEST_P(DNNTestNetwork, OpenPose_pose_mpi)
{
if (backend == DNN_BACKEND_HALIDE) throw SkipTestException("");
processNet("dnn/openpose_pose_mpi.caffemodel", "dnn/openpose_pose_mpi.prototxt",
Size(368, 368), "", "caffe");
Size(368, 368), "");
}
TEST_P(DNNTestNetwork, OpenPose_pose_mpi_faster_4_stages)
@ -208,13 +194,13 @@ TEST_P(DNNTestNetwork, OpenPose_pose_mpi_faster_4_stages)
// 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",
Size(368, 368), "", "caffe");
Size(368, 368), "");
}
TEST_P(DNNTestNetwork, OpenFace)
{
if (backend == DNN_BACKEND_HALIDE) throw SkipTestException("");
processNet("dnn/openface_nn4.small2.v1.t7", "", Size(96, 96), "", "torch");
processNet("dnn/openface_nn4.small2.v1.t7", "", Size(96, 96), "");
}
TEST_P(DNNTestNetwork, opencv_face_detector)
@ -223,7 +209,7 @@ TEST_P(DNNTestNetwork, opencv_face_detector)
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", "caffe");
inp, "detection_out");
}
TEST_P(DNNTestNetwork, Inception_v2_SSD_TensorFlow)
@ -232,7 +218,7 @@ TEST_P(DNNTestNetwork, Inception_v2_SSD_TensorFlow)
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", "tensorflow");
inp, "detection_out");
}
const tuple<DNNBackend, DNNTarget> testCases[] = {

21
modules/dnn/test/test_layers.cpp
Unescape View File

@ -865,4 +865,25 @@ TEST(Layer_PriorBox, squares)
normAssert(out.reshape(1, 4), target);
}
#ifdef HAVE_INF_ENGINE
// Using Intel's Model Optimizer generate .xml and .bin files:
// ./ModelOptimizer -w /path/to/caffemodel -d /path/to/prototxt \
// -p FP32 -i -b ${batch_size} -o /path/to/output/folder
TEST(Layer_Test_Convolution_DLDT, Accuracy)
{
Net netDefault = readNet(_tf("layer_convolution.caffemodel"), _tf("layer_convolution.prototxt"));
Net net = readNet(_tf("layer_convolution.xml"), _tf("layer_convolution.bin"));
Mat inp = blobFromNPY(_tf("blob.npy"));
netDefault.setInput(inp);
Mat outDefault = netDefault.forward();
net.setInput(inp);
Mat out = net.forward();
normAssert(outDefault, out);
}
#endif // HAVE_INF_ENGINE
}} // namespace

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