From: Dmitry Kurtaev Date: Tue, 6 Feb 2018 13:23:18 +0000 (+0300) Subject: OpenCV face detection network using Inference Engine backend X-Git-Tag: accepted/tizen/6.0/unified/20201030.111113~83^2 X-Git-Url: http://review.tizen.org/git/?a=commitdiff_plain;h=ed941365480fbff4811aa134bc9da5ab1ef0550c;p=platform%2Fupstream%2Fopencv.git OpenCV face detection network using Inference Engine backend --- diff --git a/cmake/OpenCVDetectInferenceEngine.cmake b/cmake/OpenCVDetectInferenceEngine.cmake index f1bccb4..923a4a7 100644 --- a/cmake/OpenCVDetectInferenceEngine.cmake +++ b/cmake/OpenCVDetectInferenceEngine.cmake @@ -20,6 +20,9 @@ if(NOT INF_ENGINE_ROOT_DIR OR NOT EXISTS "${INF_ENGINE_ROOT_DIR}/inference_engin if(DEFINED ENV{INTEL_CVSDK_DIR}) list(APPEND ie_root_paths "$ENV{INTEL_CVSDK_DIR}") endif() + if(DEFINED INTEL_CVSDK_DIR) + list(APPEND ie_root_paths "${INTEL_CVSDK_DIR}") + endif() if(WITH_INF_ENGINE AND NOT ie_root_paths) list(APPEND ie_root_paths "/opt/intel/deeplearning_deploymenttoolkit/deployment_tools") diff --git a/modules/dnn/perf/perf_net.cpp b/modules/dnn/perf/perf_net.cpp index dd9e1e1..1d7c4f7 100644 --- a/modules/dnn/perf/perf_net.cpp +++ b/modules/dnn/perf/perf_net.cpp @@ -150,6 +150,7 @@ PERF_TEST_P_(DNNTestNetwork, SSD) 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"); } @@ -197,6 +198,15 @@ PERF_TEST_P_(DNNTestNetwork, OpenPose_pose_mpi_faster_4_stages) Mat(cv::Size(368, 368), CV_32FC3), "", "caffe"); } +PERF_TEST_P_(DNNTestNetwork, opencv_face_detector) +{ + if (backend == DNN_BACKEND_HALIDE || + 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"); +} + const tuple testCases[] = { #ifdef HAVE_HALIDE tuple(DNN_BACKEND_HALIDE, DNN_TARGET_CPU), diff --git a/modules/dnn/src/dnn.cpp b/modules/dnn/src/dnn.cpp index 1bc53b5..7cc44da 100644 --- a/modules/dnn/src/dnn.cpp +++ b/modules/dnn/src/dnn.cpp @@ -1077,35 +1077,72 @@ struct Net::Impl } } +#ifdef HAVE_INF_ENGINE + // Before launching Inference Engine graph we need to specify output blobs. + // This function requests output blobs based on inputs references of + // layers from default backend or layers from different graphs. + void addInfEngineNetOutputs(LayerData &ld) + { + Ptr layerNet; + if (ld.backendNodes.find(preferableBackend) != ld.backendNodes.end()) + { + Ptr node = ld.backendNodes[preferableBackend]; + if (!node.empty()) + { + Ptr ieNode = node.dynamicCast(); + CV_Assert(!ieNode.empty(), !ieNode->net.empty()); + layerNet = ieNode->net; + } + } + // For an every input reference we check that it belongs to one of + // the Inference Engine backend graphs. Request an output blob if it is. + // Do nothing if layer's input is from the same graph. + for (int i = 0; i < ld.inputBlobsId.size(); ++i) + { + LayerData &inpLd = layers[ld.inputBlobsId[i].lid]; + Ptr inpNode = inpLd.backendNodes[preferableBackend]; + if (!inpNode.empty()) + { + Ptr ieInpNode = inpNode.dynamicCast(); + CV_Assert(!ieInpNode.empty(), !ieInpNode->net.empty()); + if (layerNet != ieInpNode->net) + { + // layerNet is empty or nodes are from different graphs. + ieInpNode->net->addOutput(inpLd.name); + } + } + } + } +#endif // HAVE_INF_ENGINE + void initInfEngineBackend() { // Build Inference Engine networks from sets of layers that support this - // backend. If an internal layer isn't supported we'll use default - // implementation of it but build a new network after it. + // 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 net; + // Set of all input and output blobs wrappers for current network. + std::map > netBlobsWrappers; for (it = layers.begin(); it != layers.end(); ++it) { LayerData &ld = it->second; - ld.skip = true; + ld.skip = true; // Initially skip all Inference Engine supported layers. Ptr layer = ld.layerInstance; if (!layer->supportBackend(preferableBackend)) { - for (int i = 0; i < ld.outputBlobsWrappers.size(); ++i) - { - auto dataPtr = infEngineDataNode(ld.outputBlobsWrappers[i]); - dataPtr->name = ld.name; - } + addInfEngineNetOutputs(ld); ld.skip = false; net = Ptr(); + netBlobsWrappers.clear(); continue; } - // Check what all inputs are from the same network or from default backend. + // Create a new network if one of inputs from different Inference Engine graph. for (int i = 0; i < ld.inputBlobsId.size(); ++i) { LayerData &inpLd = layers[ld.inputBlobsId[i].lid]; @@ -1113,10 +1150,36 @@ struct Net::Impl if (!inpNode.empty()) { Ptr ieInpNode = inpNode.dynamicCast(); - CV_Assert(!ieInpNode.empty(), net.empty() || net == ieInpNode->net); + CV_Assert(!ieInpNode.empty(), !ieInpNode->net.empty()); + if (ieInpNode->net != net) + { + net = Ptr(); + netBlobsWrappers.clear(); + break; + } } } + // The same blobs wrappers cannot be shared between two Inference Engine + // networks because of explicit references between layers and blobs. + // So we need to rewrap all the external blobs. + for (int i = 0; i < ld.inputBlobsId.size(); ++i) + { + int lid = ld.inputBlobsId[i].lid; + LayerData &inpLd = layers[lid]; + auto it = netBlobsWrappers.find(lid); + if (it == netBlobsWrappers.end()) + { + ld.inputBlobsWrappers[i] = wrap(*ld.inputBlobs[i]); + auto dataPtr = infEngineDataNode(ld.inputBlobsWrappers[i]); + dataPtr->name = inpLd.name; + netBlobsWrappers[lid] = ld.inputBlobsWrappers[i]; + } + else + ld.inputBlobsWrappers[i] = it->second; + } + netBlobsWrappers[ld.id] = ld.outputBlobsWrappers[0]; + bool fused = false; Ptr node; if (!net.empty()) @@ -1153,6 +1216,7 @@ struct Net::Impl if (!fused) net->addLayer(ieNode->layer); + addInfEngineNetOutputs(ld); } // Initialize all networks. diff --git a/modules/dnn/src/layers/batch_norm_layer.cpp b/modules/dnn/src/layers/batch_norm_layer.cpp index d5f0df6..cab9bb7 100644 --- a/modules/dnn/src/layers/batch_norm_layer.cpp +++ b/modules/dnn/src/layers/batch_norm_layer.cpp @@ -277,14 +277,12 @@ public: #ifdef HAVE_INF_ENGINE InferenceEngine::LayerParams lp; lp.name = name; - lp.type = "BatchNormalization"; + lp.type = "ScaleShift"; lp.precision = InferenceEngine::Precision::FP32; - std::shared_ptr ieLayer(new InferenceEngine::BatchNormalizationLayer(lp)); + std::shared_ptr ieLayer(new InferenceEngine::ScaleShiftLayer(lp)); - size_t numChannels = weights_.total(); - ieLayer->epsilon = epsilon; - ieLayer->_weights = wrapToInfEngineBlob(blobs[1], {numChannels}); - ieLayer->_biases = wrapToInfEngineBlob(blobs[0], {numChannels}); + ieLayer->_weights = wrapToInfEngineBlob(weights_); + ieLayer->_biases = wrapToInfEngineBlob(bias_); return Ptr(new InfEngineBackendNode(ieLayer)); #endif // HAVE_INF_ENGINE diff --git a/modules/dnn/src/layers/prior_box_layer.cpp b/modules/dnn/src/layers/prior_box_layer.cpp index cacc279..96f1c74 100644 --- a/modules/dnn/src/layers/prior_box_layer.cpp +++ b/modules/dnn/src/layers/prior_box_layer.cpp @@ -550,7 +550,7 @@ public: for (int i = 1; i < _variance.size(); ++i) ieLayer->params["variance"] += format(",%f", _variance[i]); - ieLayer->params["step"] = "0"; + ieLayer->params["step"] = _stepX == _stepY ? format("%f", _stepX) : "0"; ieLayer->params["step_h"] = _stepY; ieLayer->params["step_w"] = _stepX; diff --git a/modules/dnn/src/op_inf_engine.cpp b/modules/dnn/src/op_inf_engine.cpp index 7bda0b8..6c245fa 100644 --- a/modules/dnn/src/op_inf_engine.cpp +++ b/modules/dnn/src/op_inf_engine.cpp @@ -116,31 +116,6 @@ InferenceEngine::Precision InfEngineBackendNet::getPrecision() noexcept // Assume that outputs of network is unconnected blobs. void InfEngineBackendNet::getOutputsInfo(InferenceEngine::OutputsDataMap &outputs_) noexcept { - if (outputs.empty()) - { - for (const auto& l : layers) - { - // Add all outputs. - for (const InferenceEngine::DataPtr& out : l->outData) - { - // TODO: Replace to uniquness assertion. - if (outputs.find(out->name) == outputs.end()) - outputs[out->name] = out; - } - // Remove internally connected outputs. - for (const InferenceEngine::DataWeakPtr& inp : l->insData) - { - outputs.erase(InferenceEngine::DataPtr(inp)->name); - } - } - CV_Assert(layers.empty() || !outputs.empty()); - } - outBlobs.clear(); - for (const auto& it : outputs) - { - CV_Assert(allBlobs.find(it.first) != allBlobs.end()); - outBlobs[it.first] = allBlobs[it.first]; - } outputs_ = outputs; } @@ -216,7 +191,18 @@ InferenceEngine::StatusCode InfEngineBackendNet::addOutput(const std::string &layerName, size_t outputIndex, InferenceEngine::ResponseDesc *resp) noexcept { - CV_Error(Error::StsNotImplemented, ""); + for (const auto& l : layers) + { + for (const InferenceEngine::DataPtr& out : l->outData) + { + if (out->name == layerName) + { + outputs[out->name] = out; + return InferenceEngine::StatusCode::OK; + } + } + } + CV_Error(Error::StsObjectNotFound, "Cannot find a layer " + layerName); return InferenceEngine::StatusCode::OK; } @@ -254,6 +240,39 @@ size_t InfEngineBackendNet::getBatchSize() const noexcept void InfEngineBackendNet::initEngine() { CV_Assert(!isInitialized()); + + // Add all unconnected blobs to output blobs. + InferenceEngine::OutputsDataMap unconnectedOuts; + for (const auto& l : layers) + { + // 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); + } + } + CV_Assert(layers.empty() || !unconnectedOuts.empty()); + + for (auto it = unconnectedOuts.begin(); it != unconnectedOuts.end(); ++it) + { + outputs[it->first] = it->second; + } + + // Set up output blobs. + outBlobs.clear(); + for (const auto& it : outputs) + { + CV_Assert(allBlobs.find(it.first) != allBlobs.end()); + outBlobs[it.first] = allBlobs[it.first]; + } + engine = InferenceEngine::InferenceEnginePluginPtr("libMKLDNNPlugin.so"); InferenceEngine::ResponseDesc resp; InferenceEngine::StatusCode status = engine->LoadNetwork(*this, &resp); diff --git a/modules/dnn/test/test_backends.cpp b/modules/dnn/test/test_backends.cpp index 0a76000..58b25f3 100644 --- a/modules/dnn/test/test_backends.cpp +++ b/modules/dnn/test/test_backends.cpp @@ -206,9 +206,21 @@ TEST_P(DNNTestNetwork, OpenPose_pose_mpi_faster_4_stages) TEST_P(DNNTestNetwork, OpenFace) { + if (backend == DNN_BACKEND_HALIDE) throw SkipTestException(""); processNet("dnn/openface_nn4.small2.v1.t7", "", Size(96, 96), "", "torch"); } +TEST_P(DNNTestNetwork, opencv_face_detector) +{ + if (backend == DNN_BACKEND_HALIDE || + backend == DNN_BACKEND_DEFAULT && target == DNN_TARGET_OPENCL) + 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", "caffe"); +} + const tuple testCases[] = { #ifdef HAVE_HALIDE tuple(DNN_BACKEND_HALIDE, DNN_TARGET_CPU), diff --git a/modules/dnn/test/test_tf_importer.cpp b/modules/dnn/test/test_tf_importer.cpp index 5bddd71..9cebd23 100644 --- a/modules/dnn/test/test_tf_importer.cpp +++ b/modules/dnn/test/test_tf_importer.cpp @@ -279,9 +279,8 @@ TEST(Test_TensorFlow, Inception_v2_SSD) normAssert(detections, ref); } -OCL_TEST(Test_TensorFlow, MobileNet_SSD) +OCL_TEST(Test_TensorFlow, DISABLED_MobileNet_SSD) { - throw SkipTestException("TODO: test is failed"); std::string netPath = findDataFile("dnn/ssd_mobilenet_v1_coco.pb", false); std::string netConfig = findDataFile("dnn/ssd_mobilenet_v1_coco.pbtxt", false); std::string imgPath = findDataFile("dnn/street.png", false);