mirror of
https://github.com/opencv/opencv.git
synced 2025-06-07 17:44:04 +08:00
Load networks from intermediate representation of Intel's Deep learning deployment toolkit.
This commit is contained in:
Dmitry Kurtaev
parent
e06d1e8083
commit
7972f47ed4
@ -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).
|
||||
|
||||
@ -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[] = {
|
||||
|
||||
@ -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)
|
||||
{
|
||||
Net cvNet;
|
||||
#ifndef HAVE_INF_ENGINE
|
||||
CV_Error(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);
|
||||
}
|
||||
|
||||
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;
|
||||
#endif // HAVE_INF_ENGINE
|
||||
return cvNet;
|
||||
}
|
||||
|
||||
Net::~Net()
|
||||
{
|
||||
}
|
||||
@ -2846,10 +2887,22 @@ Net readNet(const String& _model, const String& _config, const String& _framewor
|
||||
std::swap(model, config);
|
||||
return readNetFromDarknet(config, model);
|
||||
}
|
||||
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_Error(Error::StsError, "Cannot determine an origin framework of files: " +
|
||||
model + (config.empty() ? "" : ", " + config));
|
||||
return Net();
|
||||
}
|
||||
|
||||
Net readNetFromModelOptimizer(const String &xml, const String &bin)
|
||||
{
|
||||
return Net::readFromModelOptimizer(xml, bin);
|
||||
}
|
||||
|
||||
CV__DNN_EXPERIMENTAL_NS_END
|
||||
}} // namespace
|
||||
|
||||
@ -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();
|
||||
@ -213,11 +225,12 @@ size_t InfEngineBackendNet::getBatchSize() const noexcept
|
||||
return 0;
|
||||
}
|
||||
|
||||
void InfEngineBackendNet::initEngine()
|
||||
void InfEngineBackendNet::init()
|
||||
{
|
||||
if (inputs.empty())
|
||||
{
|
||||
CV_Assert(!isInitialized(), !layers.empty());
|
||||
|
||||
// Collect all external input blobs.
|
||||
inputs.clear();
|
||||
std::map<std::string, InferenceEngine::DataPtr> internalOutputs;
|
||||
for (const auto& l : layers)
|
||||
{
|
||||
@ -240,7 +253,10 @@ void InfEngineBackendNet::initEngine()
|
||||
}
|
||||
}
|
||||
CV_Assert(!inputs.empty());
|
||||
}
|
||||
|
||||
if (outputs.empty())
|
||||
{
|
||||
// Add all unconnected blobs to output blobs.
|
||||
InferenceEngine::OutputsDataMap unconnectedOuts;
|
||||
for (const auto& l : layers)
|
||||
@ -264,6 +280,7 @@ void InfEngineBackendNet::initEngine()
|
||||
{
|
||||
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()
|
||||
|
||||
@ -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();
|
||||
|
||||
@ -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[] = {
|
||||
|
||||
@ -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
|
||||
|
||||
Loading…
Reference in New Issue
Block a user