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Merge pull request #11062 from dkurt:dnn_inf_engine_cldnn

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pull/11302/head
Vadim Pisarevsky 7 years ago
parent 47134fa1b4 709cf5d038
commit 30175594e9
12 changed files with 196 additions and 169 deletions
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  1. 3
      modules/dnn/include/opencv2/dnn/dnn.hpp
  2. 35
      modules/dnn/perf/perf_net.cpp
  3. 49
      modules/dnn/src/dnn.cpp
  4. 18
      modules/dnn/src/layers/batch_norm_layer.cpp
  5. 38
      modules/dnn/src/layers/convolution_layer.cpp
  6. 4
      modules/dnn/src/layers/fully_connected_layer.cpp
  7. 19
      modules/dnn/src/layers/scale_layer.cpp
  8. 21
      modules/dnn/src/layers/shift_layer.cpp
  9. 107
      modules/dnn/src/op_inf_engine.cpp
  10. 19
      modules/dnn/src/op_inf_engine.hpp
  11. 50
      modules/dnn/test/test_backends.cpp
  12. 2
      modules/dnn/test/test_precomp.hpp

3
modules/dnn/include/opencv2/dnn/dnn.hpp
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@ -80,7 +80,8 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
enum Target
{
DNN_TARGET_CPU,
DNN_TARGET_OPENCL
DNN_TARGET_OPENCL,
DNN_TARGET_OPENCL_FP16
};
/** @brief This class provides all data needed to initialize layer.

35
modules/dnn/perf/perf_net.cpp
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@ -13,7 +13,7 @@
namespace opencv_test {
CV_ENUM(DNNBackend, DNN_BACKEND_DEFAULT, DNN_BACKEND_HALIDE, DNN_BACKEND_INFERENCE_ENGINE)
CV_ENUM(DNNTarget, DNN_TARGET_CPU, DNN_TARGET_OPENCL)
CV_ENUM(DNNTarget, DNN_TARGET_CPU, DNN_TARGET_OPENCL, DNN_TARGET_OPENCL_FP16)
class DNNTestNetwork : public ::perf::TestBaseWithParam< tuple<DNNBackend, DNNTarget> >
{
@ -41,8 +41,6 @@ public:
throw cvtest::SkipTestException("OpenCL is not available/disabled in OpenCV");
}
}
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL)
throw SkipTestException("Skip OpenCL target of Inference Engine backend");
randu(input, 0.0f, 1.0f);
@ -89,24 +87,32 @@ public:
PERF_TEST_P_(DNNTestNetwork, AlexNet)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_CPU)
throw SkipTestException("");
processNet("dnn/bvlc_alexnet.caffemodel", "dnn/bvlc_alexnet.prototxt",
"alexnet.yml", Mat(cv::Size(227, 227), CV_32FC3));
}
PERF_TEST_P_(DNNTestNetwork, GoogLeNet)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16)
throw SkipTestException("");
processNet("dnn/bvlc_googlenet.caffemodel", "dnn/bvlc_googlenet.prototxt",
"", Mat(cv::Size(224, 224), CV_32FC3));
}
PERF_TEST_P_(DNNTestNetwork, ResNet_50)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16)
throw SkipTestException("");
processNet("dnn/ResNet-50-model.caffemodel", "dnn/ResNet-50-deploy.prototxt",
"resnet_50.yml", Mat(cv::Size(224, 224), CV_32FC3));
}
PERF_TEST_P_(DNNTestNetwork, SqueezeNet_v1_1)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16)
throw SkipTestException("");
processNet("dnn/squeezenet_v1.1.caffemodel", "dnn/squeezenet_v1.1.prototxt",
"squeezenet_v1_1.yml", Mat(cv::Size(227, 227), CV_32FC3));
}
@ -135,14 +141,18 @@ PERF_TEST_P_(DNNTestNetwork, SSD)
PERF_TEST_P_(DNNTestNetwork, OpenFace)
{
if (backend == DNN_BACKEND_HALIDE) throw SkipTestException("");
if (backend == DNN_BACKEND_HALIDE ||
backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_CPU)
throw SkipTestException("");
processNet("dnn/openface_nn4.small2.v1.t7", "", "",
Mat(cv::Size(96, 96), CV_32FC3));
}
PERF_TEST_P_(DNNTestNetwork, MobileNet_SSD_Caffe)
{
if (backend == DNN_BACKEND_HALIDE) throw SkipTestException("");
if (backend == DNN_BACKEND_HALIDE ||
backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_CPU)
throw SkipTestException("");
processNet("dnn/MobileNetSSD_deploy.caffemodel", "dnn/MobileNetSSD_deploy.prototxt", "",
Mat(cv::Size(300, 300), CV_32FC3));
}
@ -150,7 +160,8 @@ PERF_TEST_P_(DNNTestNetwork, MobileNet_SSD_Caffe)
PERF_TEST_P_(DNNTestNetwork, MobileNet_SSD_TensorFlow)
{
if (backend == DNN_BACKEND_DEFAULT && target == DNN_TARGET_OPENCL ||
backend == DNN_BACKEND_HALIDE)
backend == DNN_BACKEND_HALIDE ||
backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_CPU)
throw SkipTestException("");
processNet("dnn/ssd_mobilenet_v1_coco.pb", "ssd_mobilenet_v1_coco.pbtxt", "",
Mat(cv::Size(300, 300), CV_32FC3));
@ -158,7 +169,9 @@ PERF_TEST_P_(DNNTestNetwork, MobileNet_SSD_TensorFlow)
PERF_TEST_P_(DNNTestNetwork, DenseNet_121)
{
if (backend == DNN_BACKEND_HALIDE) throw SkipTestException("");
if (backend == DNN_BACKEND_HALIDE ||
backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16)
throw SkipTestException("");
processNet("dnn/DenseNet_121.caffemodel", "dnn/DenseNet_121.prototxt", "",
Mat(cv::Size(224, 224), CV_32FC3));
}
@ -189,7 +202,7 @@ PERF_TEST_P_(DNNTestNetwork, OpenPose_pose_mpi_faster_4_stages)
PERF_TEST_P_(DNNTestNetwork, opencv_face_detector)
{
if (backend == DNN_BACKEND_HALIDE ||
backend == DNN_BACKEND_DEFAULT && target == DNN_TARGET_OPENCL)
backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_CPU)
throw SkipTestException("");
processNet("dnn/opencv_face_detector.caffemodel", "dnn/opencv_face_detector.prototxt", "",
Mat(cv::Size(300, 300), CV_32FC3));
@ -197,7 +210,9 @@ PERF_TEST_P_(DNNTestNetwork, opencv_face_detector)
PERF_TEST_P_(DNNTestNetwork, Inception_v2_SSD_TensorFlow)
{
if (backend == DNN_BACKEND_HALIDE) throw SkipTestException("");
if (backend == DNN_BACKEND_HALIDE ||
backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_CPU)
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));
}
@ -209,6 +224,8 @@ const tuple<DNNBackend, DNNTarget> testCases[] = {
#endif
#ifdef HAVE_INF_ENGINE
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_CPU),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL_FP16),
#endif
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_DEFAULT, DNN_TARGET_CPU),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_DEFAULT, DNN_TARGET_OPENCL)

49
modules/dnn/src/dnn.cpp
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@ -1154,7 +1154,7 @@ struct Net::Impl
ld.skip = true;
}
layers[lastLayerId].skip = false;
ieNode->net->init();
ieNode->net->init(preferableTarget);
return;
}
@ -1167,17 +1167,17 @@ struct Net::Impl
for (it = layers.begin(); it != layers.end(); ++it)
{
LayerData &ld = it->second;
ld.skip = true; // Initially skip all Inference Engine supported layers.
Ptr<Layer> layer = ld.layerInstance;
bool fused = ld.skip && ld.id != 0;
Ptr<Layer> layer = ld.layerInstance;
if (!layer->supportBackend(preferableBackend))
{
addInfEngineNetOutputs(ld);
ld.skip = false;
net = Ptr<InfEngineBackendNet>();
netBlobsWrappers.clear();
continue;
}
ld.skip = true; // Initially skip all Inference Engine supported layers.
// Create a new network if one of inputs from different Inference Engine graph.
for (int i = 0; i < ld.inputBlobsId.size(); ++i)
@ -1217,19 +1217,16 @@ struct Net::Impl
}
netBlobsWrappers[ld.id] = ld.outputBlobsWrappers[0];
bool fused = false;
Ptr<BackendNode> node;
if (!net.empty())
{
// Try to fuse.
bool inPlace = ld.inputBlobsId.size() == 1 && ld.outputBlobs.size() == 1 &&
ld.inputBlobs[0]->data == ld.outputBlobs[0].data;
if (inPlace)
if (fused)
{
node = layer->tryAttach(layers[ld.inputBlobsId[0].lid].backendNodes[preferableBackend]);
fused = !node.empty();
if (fused)
ld.inputBlobsWrappers = layers[ld.inputBlobsId[0].lid].inputBlobsWrappers;
bool inPlace = ld.inputBlobsId.size() == 1 && ld.outputBlobs.size() == 1 &&
ld.inputBlobs[0]->data == ld.outputBlobs[0].data;
CV_Assert(inPlace);
node = layers[ld.inputBlobsId[0].lid].backendNodes[preferableBackend];
ld.inputBlobsWrappers = layers[ld.inputBlobsId[0].lid].inputBlobsWrappers;
}
}
else
@ -1247,6 +1244,19 @@ struct Net::Impl
CV_Assert(!ieNode.empty());
ieNode->net = net;
if (preferableTarget == DNN_TARGET_OPENCL_FP16 && !fused)
{
ieNode->layer->precision = InferenceEngine::Precision::FP16;
auto weightableLayer = std::dynamic_pointer_cast<InferenceEngine::WeightableLayer>(ieNode->layer);
if (weightableLayer)
{
if (weightableLayer->_weights)
weightableLayer->_weights = convertFp16(weightableLayer->_weights);
if (weightableLayer->_biases)
weightableLayer->_biases = convertFp16(weightableLayer->_biases);
}
}
ieNode->connect(ld.inputBlobsWrappers, ld.outputBlobsWrappers);
net->addBlobs(ld.inputBlobsWrappers);
net->addBlobs(ld.outputBlobsWrappers);
@ -1276,7 +1286,7 @@ struct Net::Impl
if (!ieNode->net->isInitialized())
{
ieNode->net->init();
ieNode->net->init(preferableTarget);
ld.skip = false;
}
}
@ -1380,7 +1390,8 @@ struct Net::Impl
void fuseLayers(const std::vector<LayerPin>& blobsToKeep_)
{
if( !fusion || preferableBackend != DNN_BACKEND_DEFAULT)
if( !fusion || preferableBackend != DNN_BACKEND_DEFAULT &&
preferableBackend != DNN_BACKEND_INFERENCE_ENGINE)
return;
CV_TRACE_FUNCTION();
@ -1407,7 +1418,7 @@ struct Net::Impl
// some other layers.
// TODO: OpenCL target support more fusion styles.
if ( preferableTarget == DNN_TARGET_OPENCL &&
if ( preferableBackend == DNN_BACKEND_DEFAULT && preferableTarget == DNN_TARGET_OPENCL &&
(!cv::ocl::useOpenCL() || (ld.layerInstance->type != "Convolution" &&
ld.layerInstance->type != "MVN")) )
continue;
@ -1442,6 +1453,9 @@ struct Net::Impl
break;
}
if (preferableBackend != DNN_BACKEND_DEFAULT)
continue; // Go to the next layer.
// For now, OpenCL target support fusion with activation of ReLU/ChannelsPReLU/Power/Tanh
if ( preferableTarget != DNN_TARGET_OPENCL ||
(preferableTarget == DNN_TARGET_OPENCL &&
@ -1583,6 +1597,9 @@ struct Net::Impl
}
}
if (preferableBackend != DNN_BACKEND_DEFAULT)
continue; // Go to the next layer.
// the optimization #2. if there is no layer that takes max pooling layer's computed
// max indices (and only some semantical segmentation networks might need this;
// many others only take the maximum values), then we switch the max pooling

18
modules/dnn/src/layers/batch_norm_layer.cpp
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@ -234,19 +234,6 @@ public:
#endif // HAVE_HALIDE
break;
}
case DNN_BACKEND_INFERENCE_ENGINE:
{
#ifdef HAVE_INF_ENGINE
auto base = node.dynamicCast<InfEngineBackendNode>();
auto conv = std::dynamic_pointer_cast<InferenceEngine::ConvolutionLayer>(base->layer);
if (conv)
{
fuseConvWeights(conv, weights_, bias_);
return base;
}
#endif // HAVE_INF_ENGINE
break;
}
}
return Ptr<BackendNode>();
}
@ -287,8 +274,9 @@ public:
lp.precision = InferenceEngine::Precision::FP32;
std::shared_ptr<InferenceEngine::ScaleShiftLayer> ieLayer(new InferenceEngine::ScaleShiftLayer(lp));
ieLayer->_weights = wrapToInfEngineBlob(weights_);
ieLayer->_biases = wrapToInfEngineBlob(bias_);
const int numChannels = weights_.total();
ieLayer->_weights = wrapToInfEngineBlob(weights_, {numChannels}, InferenceEngine::Layout::C);
ieLayer->_biases = wrapToInfEngineBlob(bias_, {numChannels}, InferenceEngine::Layout::C);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif // HAVE_INF_ENGINE

38
modules/dnn/src/layers/convolution_layer.cpp
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@ -173,21 +173,21 @@ public:
std::vector<float> biasvec;
std::vector<float> reluslope;
Ptr<ActivationLayer> activ;
bool newWeightAndBias;
bool fusedBias;
#ifdef HAVE_OPENCL
Ptr<OCL4DNNConvSpatial<float> > convolutionOp;
std::vector<UMat> umat_blobs;
bool fusedBias;
bool newWeightAndBias;
bool newActiv;
ocl4dnnFusedActiv_t activType;
float power;
#endif
ConvolutionLayerImpl(const LayerParams &params) : BaseConvolutionLayerImpl(params)
{
#ifdef HAVE_OPENCL
fusedBias = false;
newWeightAndBias = false;
fusedBias = false;
#ifdef HAVE_OPENCL
newActiv = false;
activType = OCL4DNN_CONV_FUSED_ACTIV_NONE;
power = 0.f;
@ -350,10 +350,8 @@ public:
biasvec[i] += b.at<float>(i);
}
#ifdef HAVE_OPENCL
newWeightAndBias = !w.empty() || !b.empty();
fusedBias = hasBias() || !b.empty();
#endif
biasvec[outCn] = biasvec[outCn+1] = biasvec[outCn-1];
}
@ -433,9 +431,31 @@ public:
ieLayer->_dilation_y = dilation.height;
ieLayer->_group = group;
ieLayer->_weights = wrapToInfEngineBlob(blobs[0]);
if (hasBias())
ieLayer->_biases = wrapToInfEngineBlob(blobs[1]);
ieLayer->_weights = wrapToInfEngineBlob(blobs[0], InferenceEngine::Layout::OIHW);
if (newWeightAndBias)
{
if (weightsMat.isContinuous())
{
Mat fusedWeights = weightsMat.reshape(1, blobs[0].dims, blobs[0].size);
ieLayer->_weights = wrapToInfEngineBlob(fusedWeights, InferenceEngine::Layout::OIHW);
}
else
{
ieLayer->_weights = InferenceEngine::make_shared_blob<float>(
InferenceEngine::Precision::FP32, InferenceEngine::Layout::OIHW,
ieLayer->_weights->dims());
ieLayer->_weights->allocate();
Mat newWeights = infEngineBlobToMat(ieLayer->_weights).reshape(1, outCn);
Mat fusedWeights = weightsMat.colRange(0, newWeights.cols);
fusedWeights.copyTo(newWeights);
}
}
if (hasBias() || fusedBias)
{
Mat biasesMat({outCn}, CV_32F, &biasvec[0]);
ieLayer->_biases = wrapToInfEngineBlob(biasesMat, {outCn}, InferenceEngine::Layout::C);
}
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif // HAVE_INF_ENGINE
return Ptr<BackendNode>();

4
modules/dnn/src/layers/fully_connected_layer.cpp
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@ -412,9 +412,9 @@ public:
std::shared_ptr<InferenceEngine::FullyConnectedLayer> ieLayer(new InferenceEngine::FullyConnectedLayer(lp));
ieLayer->_out_num = blobs[0].size[0];
ieLayer->_weights = wrapToInfEngineBlob(blobs[0]);
ieLayer->_weights = wrapToInfEngineBlob(blobs[0], {blobs[0].size[0], blobs[0].size[1], 1, 1}, InferenceEngine::Layout::OIHW);
if (blobs.size() > 1)
ieLayer->_biases = wrapToInfEngineBlob(blobs[1]);
ieLayer->_biases = wrapToInfEngineBlob(blobs[1], {ieLayer->_out_num}, InferenceEngine::Layout::C);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif // HAVE_INF_ENGINE
return Ptr<BackendNode>();

19
modules/dnn/src/layers/scale_layer.cpp
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@ -132,20 +132,6 @@ public:
#endif // HAVE_HALIDE
break;
}
case DNN_BACKEND_INFERENCE_ENGINE:
{
#ifdef HAVE_INF_ENGINE
auto base = node.dynamicCast<InfEngineBackendNode>();
auto conv = std::dynamic_pointer_cast<InferenceEngine::ConvolutionLayer>(base->layer);
if (conv)
{
Mat bias = hasBias ? blobs[1] : Mat();
fuseConvWeights(conv, blobs[0], bias);
return base;
}
#endif // HAVE_INF_ENGINE
break;
}
}
return Ptr<BackendNode>();
}
@ -192,9 +178,10 @@ public:
lp.precision = InferenceEngine::Precision::FP32;
std::shared_ptr<InferenceEngine::ScaleShiftLayer> ieLayer(new InferenceEngine::ScaleShiftLayer(lp));
ieLayer->_weights = wrapToInfEngineBlob(blobs[0]);
const int numChannels = blobs[0].total();
ieLayer->_weights = wrapToInfEngineBlob(blobs[0], {numChannels}, InferenceEngine::Layout::C);
if (hasBias)
ieLayer->_biases = wrapToInfEngineBlob(blobs[1]);
ieLayer->_biases = wrapToInfEngineBlob(blobs[1], {numChannels}, InferenceEngine::Layout::C);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif // HAVE_INF_ENGINE

21
modules/dnn/src/layers/shift_layer.cpp
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@ -90,27 +90,6 @@ public:
}
}
virtual Ptr<BackendNode> tryAttach(const Ptr<BackendNode>& node) CV_OVERRIDE
{
switch (node->backendId)
{
case DNN_BACKEND_INFERENCE_ENGINE:
{
#ifdef HAVE_INF_ENGINE
auto base = node.dynamicCast<InfEngineBackendNode>();
auto conv = std::dynamic_pointer_cast<InferenceEngine::ConvolutionLayer>(base->layer);
if (conv)
{
fuseConvWeights(conv, Mat(), blobs[0]);
return base;
}
#endif // HAVE_INF_ENGINE
break;
}
}
return Ptr<BackendNode>();
}
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{
#ifdef HAVE_INF_ENGINE

107
modules/dnn/src/op_inf_engine.cpp
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@ -59,22 +59,22 @@ static InferenceEngine::DataPtr wrapToInfEngineDataNode(const Mat& m, const std:
std::vector<size_t> reversedShape(&m.size[0], &m.size[0] + m.dims);
std::reverse(reversedShape.begin(), reversedShape.end());
return InferenceEngine::DataPtr(
new InferenceEngine::Data(name, reversedShape, InferenceEngine::Precision::FP32,
InferenceEngine::Layout::ANY)
new InferenceEngine::Data(name, reversedShape, InferenceEngine::Precision::FP32)
);
}
InferenceEngine::TBlob<float>::Ptr wrapToInfEngineBlob(const Mat& m, const std::vector<size_t>& shape)
InferenceEngine::TBlob<float>::Ptr wrapToInfEngineBlob(const Mat& m, const std::vector<size_t>& shape,
InferenceEngine::Layout layout)
{
return InferenceEngine::make_shared_blob<float>(InferenceEngine::Precision::FP32,
shape, (float*)m.data);
layout, shape, (float*)m.data);
}
InferenceEngine::TBlob<float>::Ptr wrapToInfEngineBlob(const Mat& m)
InferenceEngine::TBlob<float>::Ptr wrapToInfEngineBlob(const Mat& m, InferenceEngine::Layout layout)
{
std::vector<size_t> reversedShape(&m.size[0], &m.size[0] + m.dims);
std::reverse(reversedShape.begin(), reversedShape.end());
return wrapToInfEngineBlob(m, reversedShape);
return wrapToInfEngineBlob(m, reversedShape, layout);
}
InferenceEngine::DataPtr infEngineDataNode(const Ptr<BackendWrapper>& ptr)
@ -109,10 +109,14 @@ void InfEngineBackendWrapper::setHostDirty()
InfEngineBackendNet::InfEngineBackendNet()
{
targetDevice = InferenceEngine::TargetDevice::eCPU;
precision = InferenceEngine::Precision::FP32;
}
InfEngineBackendNet::InfEngineBackendNet(InferenceEngine::CNNNetwork& net)
{
targetDevice = InferenceEngine::TargetDevice::eCPU;
precision = InferenceEngine::Precision::FP32;
inputs = net.getInputsInfo();
outputs = net.getOutputsInfo();
layers.resize(net.layerCount()); // A hack to execute InfEngineBackendNet::layerCount correctly.
@ -126,9 +130,14 @@ void InfEngineBackendNet::Release() noexcept
outputs.clear();
}
void InfEngineBackendNet::setPrecision(InferenceEngine::Precision p) noexcept
{
precision = p;
}
InferenceEngine::Precision InfEngineBackendNet::getPrecision() noexcept
{
return InferenceEngine::Precision::FP32;
return precision;
}
// Assume that outputs of network is unconnected blobs.
@ -161,9 +170,8 @@ InferenceEngine::InputInfo::Ptr InfEngineBackendNet::getInput(const std::string
return it->second;
}
void InfEngineBackendNet::getName(char *pName, size_t len) noexcept
void InfEngineBackendNet::getName(char*, size_t) noexcept
{
CV_Error(Error::StsNotImplemented, "");
}
size_t InfEngineBackendNet::layerCount() noexcept
@ -213,13 +221,15 @@ InfEngineBackendNet::getLayerByName(const char *layerName, InferenceEngine::CNNL
void InfEngineBackendNet::setTargetDevice(InferenceEngine::TargetDevice device) noexcept
{
if (device != InferenceEngine::TargetDevice::eCPU)
if (device != InferenceEngine::TargetDevice::eCPU &&
device != InferenceEngine::TargetDevice::eGPU)
CV_Error(Error::StsNotImplemented, "");
targetDevice = device;
}
InferenceEngine::TargetDevice InfEngineBackendNet::getTargetDevice() noexcept
{
return InferenceEngine::TargetDevice::eCPU;
return targetDevice;
}
InferenceEngine::StatusCode InfEngineBackendNet::setBatchSize(const size_t size) noexcept
@ -234,7 +244,7 @@ size_t InfEngineBackendNet::getBatchSize() const noexcept
return 0;
}
void InfEngineBackendNet::init()
void InfEngineBackendNet::init(int targetId)
{
if (inputs.empty())
{
@ -307,6 +317,15 @@ void InfEngineBackendNet::init()
outBlobs[it.first] = allBlobs[it.first];
}
switch (targetId)
{
case DNN_TARGET_CPU: setTargetDevice(InferenceEngine::TargetDevice::eCPU); break;
case DNN_TARGET_OPENCL_FP16: setPrecision(InferenceEngine::Precision::FP16); // Fallback to the next.
case DNN_TARGET_OPENCL: setTargetDevice(InferenceEngine::TargetDevice::eGPU); break;
default:
CV_Error(Error::StsError, format("Unknown target identifier: %d", targetId));
}
if (!isInitialized())
initPlugin(*this);
}
@ -319,7 +338,7 @@ void InfEngineBackendNet::initPlugin(InferenceEngine::ICNNNetwork& net)
InferenceEngine::ResponseDesc resp;
const InferenceEngine::Version* v = InferenceEngine::GetInferenceEngineVersion();
plugin = InferenceEngine::PluginDispatcher({""}).getSuitablePlugin(InferenceEngine::TargetDevice::eCPU);
plugin = InferenceEngine::PluginDispatcher({""}).getSuitablePlugin(targetDevice);
if (std::atoi(v->buildNumber) > 5855)
{
#ifdef _WIN32
@ -360,7 +379,7 @@ void InfEngineBackendNet::forward()
CV_Error(Error::StsAssert, resp.msg);
}
static inline Mat infEngineBlobToMat(const InferenceEngine::Blob::Ptr& blob)
Mat infEngineBlobToMat(const InferenceEngine::Blob::Ptr& blob)
{
// NOTE: Inference Engine sizes are reversed.
std::vector<size_t> dims = blob->dims();
@ -369,56 +388,6 @@ static inline Mat infEngineBlobToMat(const InferenceEngine::Blob::Ptr& blob)
return Mat(size, CV_32F, (void*)blob->buffer());
}
void fuseConvWeights(const std::shared_ptr<InferenceEngine::ConvolutionLayer>& conv,
const Mat& w, const Mat& b)
{
CV_Assert(!w.empty() || !b.empty());
if (!w.empty())
{
// Get convolution's weights. Clone the data because Inference Engine can host it
// and conv->_weights->allocate() below will deallocate it.
Mat originWeights = infEngineBlobToMat(conv->_weights).clone();
// Create new weights blob.
conv->_weights = InferenceEngine::make_shared_blob<float>(
InferenceEngine::Precision::FP32, conv->_weights->dims());
conv->_weights->allocate();
// Convolution weights have OIHW data layout.
// (conv(I) + b1 ) * w + b2
// w*conv(I) + b1 * w + b2
Mat fusedWeights = infEngineBlobToMat(conv->_weights);
const int numChannels = fusedWeights.size[0];
// Mat weights = blobs[0].reshape(1, 1);
// Mat bias = hasBias ? blobs[1].reshape(1, 1) : Mat();
CV_Assert(numChannels == w.total());
CV_Assert(b.empty() || numChannels == b.total());
for (int i = 0; i < numChannels; ++i)
{
cv::multiply(slice(originWeights, i), w.at<float>(i), slice(fusedWeights, i));
}
}
if (conv->_biases)
{
// The same for biases.
Mat originBiases = infEngineBlobToMat(conv->_biases).clone();
conv->_biases = InferenceEngine::make_shared_blob<float>(
InferenceEngine::Precision::FP32, conv->_biases->dims());
conv->_biases->allocate();
Mat fusedBiases = infEngineBlobToMat(conv->_biases);
originBiases.copyTo(fusedBiases);
if (!w.empty())
cv::multiply(w.reshape(1, fusedBiases.dims, &fusedBiases.size[0]), fusedBiases, fusedBiases);
if (!b.empty())
cv::add(fusedBiases, b.reshape(1, fusedBiases.dims, &fusedBiases.size[0]), fusedBiases);
}
else
conv->_biases = wrapToInfEngineBlob(b);
}
InfEngineBackendLayer::InfEngineBackendLayer(const InferenceEngine::DataPtr& output_)
{
output = output_;
@ -454,6 +423,16 @@ void InfEngineBackendLayer::forward(InputArrayOfArrays inputs, OutputArrayOfArra
CV_Error(Error::StsInternal, "Choose Inference Engine as a preferable backend.");
}
InferenceEngine::TBlob<int16_t>::Ptr convertFp16(const InferenceEngine::Blob::Ptr& blob)
{
auto halfs = InferenceEngine::make_shared_blob<int16_t>(InferenceEngine::Precision::FP16, blob->layout(), blob->dims());
halfs->allocate();
Mat floatsData(1, blob->size(), CV_32F, blob->buffer());
Mat halfsData(1, blob->size(), CV_16SC1, halfs->buffer());
convertFp16(floatsData, halfsData);
return halfs;
}
#endif // HAVE_INF_ENGINE
bool haveInfEngine()

19
modules/dnn/src/op_inf_engine.hpp
Unescape View File

@ -32,6 +32,8 @@ public:
virtual void Release() noexcept CV_OVERRIDE;
void setPrecision(InferenceEngine::Precision p) noexcept;
virtual InferenceEngine::Precision getPrecision() noexcept CV_OVERRIDE;
virtual void getOutputsInfo(InferenceEngine::OutputsDataMap &out) noexcept /*CV_OVERRIDE*/;
@ -68,7 +70,7 @@ public:
virtual size_t getBatchSize() const noexcept CV_OVERRIDE;
void init();
void init(int targetId);
void addBlobs(const std::vector<Ptr<BackendWrapper> >& wrappers);
@ -83,6 +85,8 @@ private:
InferenceEngine::BlobMap inpBlobs;
InferenceEngine::BlobMap outBlobs;
InferenceEngine::BlobMap allBlobs;
InferenceEngine::TargetDevice targetDevice;
InferenceEngine::Precision precision;
InferenceEngine::InferenceEnginePluginPtr plugin;
void initPlugin(InferenceEngine::ICNNNetwork& net);
@ -116,15 +120,17 @@ public:
InferenceEngine::TBlob<float>::Ptr blob;
};
InferenceEngine::TBlob<float>::Ptr wrapToInfEngineBlob(const Mat& m);
InferenceEngine::TBlob<float>::Ptr wrapToInfEngineBlob(const Mat& m, InferenceEngine::Layout layout = InferenceEngine::Layout::ANY);
InferenceEngine::TBlob<float>::Ptr wrapToInfEngineBlob(const Mat& m, const std::vector<size_t>& shape);
InferenceEngine::TBlob<float>::Ptr wrapToInfEngineBlob(const Mat& m, const std::vector<size_t>& shape, InferenceEngine::Layout layout);
InferenceEngine::DataPtr infEngineDataNode(const Ptr<BackendWrapper>& ptr);
// Fuses convolution weights and biases with channel-wise scales and shifts.
void fuseConvWeights(const std::shared_ptr<InferenceEngine::ConvolutionLayer>& conv,
const Mat& w, const Mat& b = Mat());
Mat infEngineBlobToMat(const InferenceEngine::Blob::Ptr& blob);
// Convert Inference Engine blob with FP32 precision to FP16 precision.
// Allocates memory for a new blob.
InferenceEngine::TBlob<int16_t>::Ptr convertFp16(const InferenceEngine::Blob::Ptr& blob);
// 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
@ -151,7 +157,6 @@ private:
InferenceEngine::DataPtr output;
};
#endif // HAVE_INF_ENGINE
bool haveInfEngine();

50
modules/dnn/test/test_backends.cpp
Unescape View File

@ -100,6 +100,8 @@ public:
TEST_P(DNNTestNetwork, AlexNet)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_CPU)
throw SkipTestException("");
processNet("dnn/bvlc_alexnet.caffemodel", "dnn/bvlc_alexnet.prototxt",
Size(227, 227), "prob",
target == DNN_TARGET_OPENCL ? "dnn/halide_scheduler_opencl_alexnet.yml" :
@ -108,6 +110,8 @@ TEST_P(DNNTestNetwork, AlexNet)
TEST_P(DNNTestNetwork, ResNet_50)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16)
throw SkipTestException("");
processNet("dnn/ResNet-50-model.caffemodel", "dnn/ResNet-50-deploy.prototxt",
Size(224, 224), "prob",
target == DNN_TARGET_OPENCL ? "dnn/halide_scheduler_opencl_resnet_50.yml" :
@ -116,6 +120,8 @@ TEST_P(DNNTestNetwork, ResNet_50)
TEST_P(DNNTestNetwork, SqueezeNet_v1_1)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16)
throw SkipTestException("");
processNet("dnn/squeezenet_v1.1.caffemodel", "dnn/squeezenet_v1.1.prototxt",
Size(227, 227), "prob",
target == DNN_TARGET_OPENCL ? "dnn/halide_scheduler_opencl_squeezenet_v1_1.yml" :
@ -124,6 +130,8 @@ TEST_P(DNNTestNetwork, SqueezeNet_v1_1)
TEST_P(DNNTestNetwork, GoogLeNet)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16)
throw SkipTestException("");
processNet("dnn/bvlc_googlenet.caffemodel", "dnn/bvlc_googlenet.prototxt",
Size(224, 224), "prob");
}
@ -147,7 +155,9 @@ TEST_P(DNNTestNetwork, ENet)
TEST_P(DNNTestNetwork, MobileNet_SSD_Caffe)
{
if (backend == DNN_BACKEND_HALIDE) throw SkipTestException("");
if (backend == DNN_BACKEND_HALIDE ||
backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_CPU)
throw SkipTestException("");
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);
@ -157,7 +167,9 @@ TEST_P(DNNTestNetwork, MobileNet_SSD_Caffe)
TEST_P(DNNTestNetwork, MobileNet_SSD_TensorFlow)
{
if (backend == DNN_BACKEND_HALIDE) throw SkipTestException("");
if (backend == DNN_BACKEND_HALIDE ||
backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_CPU)
throw SkipTestException("");
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",
@ -177,35 +189,45 @@ TEST_P(DNNTestNetwork, SSD_VGG16)
TEST_P(DNNTestNetwork, OpenPose_pose_coco)
{
if (backend == DNN_BACKEND_HALIDE) throw SkipTestException("");
double l1 = target == DNN_TARGET_OPENCL_FP16 ? 3e-5 : 1e-5;
double lInf = target == DNN_TARGET_OPENCL_FP16 ? 3e-3 : 1e-4;
processNet("dnn/openpose_pose_coco.caffemodel", "dnn/openpose_pose_coco.prototxt",
Size(368, 368), "");
Size(368, 368), "", "", l1, lInf);
}
TEST_P(DNNTestNetwork, OpenPose_pose_mpi)
{
if (backend == DNN_BACKEND_HALIDE) throw SkipTestException("");
double l1 = target == DNN_TARGET_OPENCL_FP16 ? 4e-5 : 1e-5;
double lInf = target == DNN_TARGET_OPENCL_FP16 ? 7e-3 : 1e-4;
processNet("dnn/openpose_pose_mpi.caffemodel", "dnn/openpose_pose_mpi.prototxt",
Size(368, 368), "");
Size(368, 368), "", "", l1, lInf);
}
TEST_P(DNNTestNetwork, OpenPose_pose_mpi_faster_4_stages)
{
if (backend == DNN_BACKEND_HALIDE) throw SkipTestException("");
double l1 = target == DNN_TARGET_OPENCL_FP16 ? 5e-5 : 1e-5;
double lInf = target == DNN_TARGET_OPENCL_FP16 ? 5e-3 : 1e-4;
// 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), "");
Size(368, 368), "", "", l1, lInf);
}
TEST_P(DNNTestNetwork, OpenFace)
{
if (backend == DNN_BACKEND_HALIDE) throw SkipTestException("");
if (backend == DNN_BACKEND_HALIDE ||
backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_CPU)
throw SkipTestException("");
processNet("dnn/openface_nn4.small2.v1.t7", "", Size(96, 96), "");
}
TEST_P(DNNTestNetwork, opencv_face_detector)
{
if (backend == DNN_BACKEND_HALIDE) throw SkipTestException("");
if (backend == DNN_BACKEND_HALIDE ||
backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_CPU)
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",
@ -214,13 +236,23 @@ TEST_P(DNNTestNetwork, opencv_face_detector)
TEST_P(DNNTestNetwork, Inception_v2_SSD_TensorFlow)
{
if (backend == DNN_BACKEND_HALIDE) throw SkipTestException("");
if (backend == DNN_BACKEND_HALIDE ||
backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_CPU)
throw SkipTestException("");
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");
}
TEST_P(DNNTestNetwork, DenseNet_121)
{
if (backend == DNN_BACKEND_HALIDE ||
backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16)
throw SkipTestException("");
processNet("dnn/DenseNet_121.caffemodel", "dnn/DenseNet_121.prototxt", Size(224, 224), "", "caffe");
}
const tuple<DNNBackend, DNNTarget> testCases[] = {
#ifdef HAVE_HALIDE
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_HALIDE, DNN_TARGET_CPU),
@ -228,6 +260,8 @@ const tuple<DNNBackend, DNNTarget> testCases[] = {
#endif
#ifdef HAVE_INF_ENGINE
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_CPU),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL_FP16),
#endif
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_DEFAULT, DNN_TARGET_OPENCL)
};

2
modules/dnn/test/test_precomp.hpp
Unescape View File

@ -53,7 +53,7 @@ namespace opencv_test {
using namespace cv::dnn;
CV_ENUM(DNNBackend, DNN_BACKEND_DEFAULT, DNN_BACKEND_HALIDE, DNN_BACKEND_INFERENCE_ENGINE)
CV_ENUM(DNNTarget, DNN_TARGET_CPU, DNN_TARGET_OPENCL)
CV_ENUM(DNNTarget, DNN_TARGET_CPU, DNN_TARGET_OPENCL, DNN_TARGET_OPENCL_FP16)
static testing::internal::ParamGenerator<DNNTarget> availableDnnTargets()
{

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