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Update Intel's Inference Engine deep learning backend (#11587)

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* Update Intel's Inference Engine deep learning backend

* Remove cpu_extension dependency

* Update Darknet accuracy tests
pull/11635/head
Dmitry Kurtaev 7 years ago committed by Vadim Pisarevsky
parent 80770aacd7
commit f96f934426
20 changed files with 280 additions and 113 deletions
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  1. 6
      cmake/OpenCVDetectInferenceEngine.cmake
  2. 7
      modules/dnn/include/opencv2/dnn/dnn.hpp
  3. 68
      modules/dnn/perf/perf_net.cpp
  4. 2
      modules/dnn/src/darknet/darknet_io.cpp
  5. 6
      modules/dnn/src/dnn.cpp
  6. 10
      modules/dnn/src/layers/elementwise_layers.cpp
  7. 33
      modules/dnn/src/layers/prior_box_layer.cpp
  8. 5
      modules/dnn/src/layers/region_layer.cpp
  9. 27
      modules/dnn/src/layers/resize_nearest_neighbor_layer.cpp
  10. 47
      modules/dnn/src/op_inf_engine.cpp
  11. 83
      modules/dnn/test/test_backends.cpp
  12. 22
      modules/dnn/test/test_common.hpp
  13. 63
      modules/dnn/test/test_darknet_importer.cpp
  14. 2
      modules/dnn/test/test_precomp.hpp
  15. 2
      samples/dnn/classification.cpp
  16. 2
      samples/dnn/classification.py
  17. 2
      samples/dnn/object_detection.cpp
  18. 2
      samples/dnn/object_detection.py
  19. 2
      samples/dnn/segmentation.cpp
  20. 2
      samples/dnn/segmentation.py

6
cmake/OpenCVDetectInferenceEngine.cmake
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@ -41,8 +41,7 @@ set(INF_ENGINE_INCLUDE_DIRS "${INF_ENGINE_ROOT_DIR}/include" CACHE PATH "Path to
if(NOT INF_ENGINE_ROOT_DIR
OR NOT EXISTS "${INF_ENGINE_ROOT_DIR}"
OR NOT EXISTS "${INF_ENGINE_INCLUDE_DIRS}"
OR NOT EXISTS "${INF_ENGINE_INCLUDE_DIRS}/inference_engine.hpp"
OR NOT EXISTS "${INF_ENGINE_ROOT_DIR}/include/inference_engine.hpp"
)
ie_fail()
endif()
@ -52,10 +51,7 @@ set(INF_ENGINE_LIBRARIES "")
set(ie_lib_list inference_engine)
link_directories(
${INTEL_CVSDK_DIR}/external/mklml_lnx/lib
${INTEL_CVSDK_DIR}/inference_engine/external/mklml_lnx/lib
${INTEL_CVSDK_DIR}/inference_engine/external/mkltiny_lnx/lib
${INTEL_CVSDK_DIR}/external/cldnn/lib
${INTEL_CVSDK_DIR}/inference_engine/external/cldnn/lib
)

7
modules/dnn/include/opencv2/dnn/dnn.hpp
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@ -81,7 +81,8 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
{
DNN_TARGET_CPU,
DNN_TARGET_OPENCL,
DNN_TARGET_OPENCL_FP16
DNN_TARGET_OPENCL_FP16,
DNN_TARGET_MYRIAD
};
/** @brief This class provides all data needed to initialize layer.
@ -700,13 +701,13 @@ 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)
* * `*.bin` (DLDT, https://software.intel.com/openvino-toolkit)
* @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)
* * `*.xml` (DLDT, https://software.intel.com/openvino-toolkit)
* @param[in] framework Explicit framework name tag to determine a format.
* @returns Net object.
*

68
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, DNN_TARGET_OPENCL_FP16)
CV_ENUM(DNNTarget, DNN_TARGET_CPU, DNN_TARGET_OPENCL, DNN_TARGET_OPENCL_FP16, DNN_TARGET_MYRIAD)
class DNNTestNetwork : public ::perf::TestBaseWithParam< tuple<DNNBackend, DNNTarget> >
{
@ -29,6 +29,28 @@ public:
target = (dnn::Target)(int)get<1>(GetParam());
}
static bool checkMyriadTarget()
{
#ifndef HAVE_INF_ENGINE
return false;
#endif
cv::dnn::Net net;
cv::dnn::LayerParams lp;
net.addLayerToPrev("testLayer", "Identity", lp);
net.setPreferableBackend(cv::dnn::DNN_BACKEND_INFERENCE_ENGINE);
net.setPreferableTarget(cv::dnn::DNN_TARGET_MYRIAD);
net.setInput(cv::Mat::zeros(1, 1, CV_32FC1));
try
{
net.forward();
}
catch(...)
{
return false;
}
return true;
}
void processNet(std::string weights, std::string proto, std::string halide_scheduler,
const Mat& input, const std::string& outputLayer = "")
{
@ -41,6 +63,13 @@ public:
throw cvtest::SkipTestException("OpenCL is not available/disabled in OpenCV");
}
}
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
{
if (!checkMyriadTarget())
{
throw SkipTestException("Myriad is not available/disabled in OpenCV");
}
}
randu(input, 0.0f, 1.0f);
@ -87,8 +116,6 @@ 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));
}
@ -130,7 +157,6 @@ PERF_TEST_P_(DNNTestNetwork, ENet)
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));
}
@ -146,18 +172,17 @@ PERF_TEST_P_(DNNTestNetwork, OpenFace)
PERF_TEST_P_(DNNTestNetwork, MobileNet_SSD_Caffe)
{
if (backend == DNN_BACKEND_HALIDE ||
backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_CPU)
if (backend == DNN_BACKEND_HALIDE)
throw SkipTestException("");
processNet("dnn/MobileNetSSD_deploy.caffemodel", "dnn/MobileNetSSD_deploy.prototxt", "",
Mat(cv::Size(300, 300), CV_32FC3));
}
// TODO: update MobileNet model.
PERF_TEST_P_(DNNTestNetwork, MobileNet_SSD_TensorFlow)
{
if (backend == DNN_BACKEND_DEFAULT && target == DNN_TARGET_OPENCL ||
backend == DNN_BACKEND_HALIDE ||
backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_CPU)
if (backend == DNN_BACKEND_HALIDE ||
backend == DNN_BACKEND_INFERENCE_ENGINE)
throw SkipTestException("");
processNet("dnn/ssd_mobilenet_v1_coco.pb", "ssd_mobilenet_v1_coco.pbtxt", "",
Mat(cv::Size(300, 300), CV_32FC3));
@ -166,7 +191,8 @@ PERF_TEST_P_(DNNTestNetwork, MobileNet_SSD_TensorFlow)
PERF_TEST_P_(DNNTestNetwork, DenseNet_121)
{
if (backend == DNN_BACKEND_HALIDE ||
backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16)
backend == DNN_BACKEND_INFERENCE_ENGINE && (target == DNN_TARGET_OPENCL_FP16 ||
target == DNN_TARGET_MYRIAD))
throw SkipTestException("");
processNet("dnn/DenseNet_121.caffemodel", "dnn/DenseNet_121.prototxt", "",
Mat(cv::Size(224, 224), CV_32FC3));
@ -174,21 +200,27 @@ PERF_TEST_P_(DNNTestNetwork, DenseNet_121)
PERF_TEST_P_(DNNTestNetwork, OpenPose_pose_coco)
{
if (backend == DNN_BACKEND_HALIDE) throw SkipTestException("");
if (backend == DNN_BACKEND_HALIDE ||
backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException("");
processNet("dnn/openpose_pose_coco.caffemodel", "dnn/openpose_pose_coco.prototxt", "",
Mat(cv::Size(368, 368), CV_32FC3));
}
PERF_TEST_P_(DNNTestNetwork, OpenPose_pose_mpi)
{
if (backend == DNN_BACKEND_HALIDE) throw SkipTestException("");
if (backend == DNN_BACKEND_HALIDE ||
backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException("");
processNet("dnn/openpose_pose_mpi.caffemodel", "dnn/openpose_pose_mpi.prototxt", "",
Mat(cv::Size(368, 368), CV_32FC3));
}
PERF_TEST_P_(DNNTestNetwork, OpenPose_pose_mpi_faster_4_stages)
{
if (backend == DNN_BACKEND_HALIDE) throw SkipTestException("");
if (backend == DNN_BACKEND_HALIDE ||
backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException("");
// 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", "",
@ -197,8 +229,7 @@ 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_INFERENCE_ENGINE && target != DNN_TARGET_CPU)
if (backend == DNN_BACKEND_HALIDE)
throw SkipTestException("");
processNet("dnn/opencv_face_detector.caffemodel", "dnn/opencv_face_detector.prototxt", "",
Mat(cv::Size(300, 300), CV_32FC3));
@ -207,7 +238,8 @@ PERF_TEST_P_(DNNTestNetwork, opencv_face_detector)
PERF_TEST_P_(DNNTestNetwork, Inception_v2_SSD_TensorFlow)
{
if (backend == DNN_BACKEND_HALIDE ||
backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_CPU)
(backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL) ||
(backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16))
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));
@ -215,7 +247,8 @@ PERF_TEST_P_(DNNTestNetwork, Inception_v2_SSD_TensorFlow)
PERF_TEST_P_(DNNTestNetwork, YOLOv3)
{
if (backend != DNN_BACKEND_DEFAULT)
if (backend == DNN_BACKEND_HALIDE ||
backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException("");
Mat sample = imread(findDataFile("dnn/dog416.png", false));
Mat inp;
@ -232,6 +265,7 @@ const tuple<DNNBackend, DNNTarget> testCases[] = {
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),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_MYRIAD),
#endif
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_DEFAULT, DNN_TARGET_CPU),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_DEFAULT, DNN_TARGET_OPENCL),

2
modules/dnn/src/darknet/darknet_io.cpp
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@ -288,7 +288,7 @@ namespace cv {
permute_params.set("order", paramOrder);
darknet::LayerParameter lp;
std::string layer_name = cv::format("premute_%d", layer_id);
std::string layer_name = cv::format("permute_%d", layer_id);
lp.layer_name = layer_name;
lp.layer_type = permute_params.type;
lp.layerParams = permute_params;

6
modules/dnn/src/dnn.cpp
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@ -1182,7 +1182,9 @@ struct Net::Impl
for (it = layers.begin(); it != layers.end(); ++it)
{
LayerData &ld = it->second;
bool fused = ld.skip && ld.id != 0;
if (ld.id == 0)
continue;
bool fused = ld.skip;
Ptr<Layer> layer = ld.layerInstance;
if (!layer->supportBackend(preferableBackend))
@ -1259,7 +1261,7 @@ struct Net::Impl
CV_Assert(!ieNode.empty());
ieNode->net = net;
if (preferableTarget == DNN_TARGET_OPENCL_FP16 && !fused)
if ((preferableTarget == DNN_TARGET_OPENCL_FP16 || preferableTarget == DNN_TARGET_MYRIAD) && !fused)
{
ieNode->layer->precision = InferenceEngine::Precision::FP16;
auto weightableLayer = std::dynamic_pointer_cast<InferenceEngine::WeightableLayer>(ieNode->layer);

10
modules/dnn/src/layers/elementwise_layers.cpp
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@ -117,7 +117,7 @@ public:
{
return backendId == DNN_BACKEND_DEFAULT ||
backendId == DNN_BACKEND_HALIDE && haveHalide() ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine() && this->type != "Sigmoid";
backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine();
}
virtual Ptr<BackendNode> tryAttach(const Ptr<BackendNode>& node) CV_OVERRIDE
@ -334,6 +334,7 @@ struct ReLUFunctor
lp.type = "ReLU";
std::shared_ptr<InferenceEngine::ReLULayer> ieLayer(new InferenceEngine::ReLULayer(lp));
ieLayer->negative_slope = slope;
ieLayer->params["negative_slope"] = format("%f", slope);
return ieLayer;
}
#endif // HAVE_INF_ENGINE
@ -431,6 +432,8 @@ struct ReLU6Functor
std::shared_ptr<InferenceEngine::ClampLayer> ieLayer(new InferenceEngine::ClampLayer(lp));
ieLayer->min_value = minValue;
ieLayer->max_value = maxValue;
ieLayer->params["min"] = format("%f", minValue);
ieLayer->params["max"] = format("%f", maxValue);
return ieLayer;
}
#endif // HAVE_INF_ENGINE
@ -556,8 +559,9 @@ struct SigmoidFunctor
#ifdef HAVE_INF_ENGINE
InferenceEngine::CNNLayerPtr initInfEngine(InferenceEngine::LayerParams& lp)
{
CV_Error(Error::StsNotImplemented, "Sigmoid");
return InferenceEngine::CNNLayerPtr();
lp.type = "Sigmoid";
std::shared_ptr<InferenceEngine::CNNLayer> ieLayer(new InferenceEngine::CNNLayer(lp));
return ieLayer;
}
#endif // HAVE_INF_ENGINE

33
modules/dnn/src/layers/prior_box_layer.cpp
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@ -271,7 +271,7 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_DEFAULT ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine() && !_explicitSizes;
backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine();
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
@ -484,18 +484,33 @@ public:
#ifdef HAVE_INF_ENGINE
InferenceEngine::LayerParams lp;
lp.name = name;
lp.type = "PriorBox";
lp.type = _explicitSizes ? "PriorBoxClustered" : "PriorBox";
lp.precision = InferenceEngine::Precision::FP32;
std::shared_ptr<InferenceEngine::CNNLayer> ieLayer(new InferenceEngine::CNNLayer(lp));
ieLayer->params["min_size"] = format("%f", _minSize);
ieLayer->params["max_size"] = _maxSize > 0 ? format("%f", _maxSize) : "";
if (!_aspectRatios.empty())
if (_explicitSizes)
{
CV_Assert(!_boxWidths.empty(), !_boxHeights.empty(),
_boxWidths.size() == _boxHeights.size());
ieLayer->params["width"] = format("%f", _boxWidths[0]);
ieLayer->params["height"] = format("%f", _boxHeights[0]);
for (int i = 1; i < _boxWidths.size(); ++i)
{
ieLayer->params["width"] += format(",%f", _boxWidths[i]);
ieLayer->params["height"] += format(",%f", _boxHeights[i]);
}
}
else
{
ieLayer->params["aspect_ratio"] = format("%f", _aspectRatios[0]);
for (int i = 1; i < _aspectRatios.size(); ++i)
ieLayer->params["aspect_ratio"] += format(",%f", _aspectRatios[i]);
ieLayer->params["min_size"] = format("%f", _minSize);
ieLayer->params["max_size"] = _maxSize > 0 ? format("%f", _maxSize) : "";
if (!_aspectRatios.empty())
{
ieLayer->params["aspect_ratio"] = format("%f", _aspectRatios[0]);
for (int i = 1; i < _aspectRatios.size(); ++i)
ieLayer->params["aspect_ratio"] += format(",%f", _aspectRatios[i]);
}
}
ieLayer->params["flip"] = "0"; // We already flipped aspect ratios.

5
modules/dnn/src/layers/region_layer.cpp
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@ -95,11 +95,6 @@ public:
return false;
}
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_DEFAULT;
}
float logistic_activate(float x) { return 1.F / (1.F + exp(-x)); }
void softmax_activate(const float* input, const int n, const float temp, float* output)

27
modules/dnn/src/layers/resize_nearest_neighbor_layer.cpp
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@ -6,6 +6,7 @@
// Third party copyrights are property of their respective owners.
#include "../precomp.hpp"
#include "layers_common.hpp"
#include "../op_inf_engine.hpp"
#include <opencv2/imgproc.hpp>
namespace cv { namespace dnn {
@ -39,6 +40,12 @@ public:
return (outputs[0][2] == inputs[0][2]) && (outputs[0][3] == inputs[0][3]);
}
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_DEFAULT ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine();
}
virtual void finalize(const std::vector<Mat*>& inputs, std::vector<Mat> &outputs) CV_OVERRIDE
{
if (!outWidth && !outHeight)
@ -75,6 +82,26 @@ public:
}
}
}
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{
#ifdef HAVE_INF_ENGINE
InferenceEngine::LayerParams lp;
lp.name = name;
lp.type = "Resample";
lp.precision = InferenceEngine::Precision::FP32;
std::shared_ptr<InferenceEngine::CNNLayer> ieLayer(new InferenceEngine::CNNLayer(lp));
ieLayer->params["type"] = "caffe.ResampleParameter.NEAREST";
ieLayer->params["antialias"] = "0";
ieLayer->params["width"] = cv::format("%d", outWidth);
ieLayer->params["height"] = cv::format("%d", outHeight);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif // HAVE_INF_ENGINE
return Ptr<BackendNode>();
}
private:
int outWidth, outHeight, zoomFactor;
bool alignCorners;

47
modules/dnn/src/op_inf_engine.cpp
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@ -18,11 +18,6 @@ namespace cv { namespace dnn {
#ifdef HAVE_INF_ENGINE
static int infEngineVersion()
{
return std::atoi(InferenceEngine::GetInferenceEngineVersion()->buildNumber);
}
InfEngineBackendNode::InfEngineBackendNode(const InferenceEngine::CNNLayerPtr& _layer)
: BackendNode(DNN_BACKEND_INFERENCE_ENGINE), layer(_layer) {}
@ -59,27 +54,23 @@ infEngineWrappers(const std::vector<Ptr<BackendWrapper> >& ptrs)
return wrappers;
}
static InferenceEngine::Layout estimateLayout(const Mat& m)
{
if (m.dims == 4)
return InferenceEngine::Layout::NCHW;
else if (m.dims == 2)
return InferenceEngine::Layout::NC;
else
return InferenceEngine::Layout::ANY;
}
static InferenceEngine::DataPtr wrapToInfEngineDataNode(const Mat& m, const std::string& name = "")
{
std::vector<size_t> reversedShape(&m.size[0], &m.size[0] + m.dims);
std::reverse(reversedShape.begin(), reversedShape.end());
if (infEngineVersion() > 5855)
{
InferenceEngine::Layout l = InferenceEngine::Layout::ANY;
if (m.dims == 4)
l = InferenceEngine::Layout::NCHW;
else if (m.dims == 2)
l = InferenceEngine::Layout::NC;
return InferenceEngine::DataPtr(
new InferenceEngine::Data(name, reversedShape, InferenceEngine::Precision::FP32, l)
);
}
else
{
return InferenceEngine::DataPtr(
new InferenceEngine::Data(name, reversedShape, InferenceEngine::Precision::FP32)
);
}
return InferenceEngine::DataPtr(
new InferenceEngine::Data(name, reversedShape, InferenceEngine::Precision::FP32, estimateLayout(m))
);
}
InferenceEngine::TBlob<float>::Ptr wrapToInfEngineBlob(const Mat& m, const std::vector<size_t>& shape,
@ -108,7 +99,7 @@ InfEngineBackendWrapper::InfEngineBackendWrapper(int targetId, const cv::Mat& m)
: BackendWrapper(DNN_BACKEND_INFERENCE_ENGINE, targetId)
{
dataPtr = wrapToInfEngineDataNode(m);
blob = wrapToInfEngineBlob(m);
blob = wrapToInfEngineBlob(m, estimateLayout(m));
}
InfEngineBackendWrapper::~InfEngineBackendWrapper()
@ -252,7 +243,8 @@ InfEngineBackendNet::getLayerByName(const char *layerName, InferenceEngine::CNNL
void InfEngineBackendNet::setTargetDevice(InferenceEngine::TargetDevice device) noexcept
{
if (device != InferenceEngine::TargetDevice::eCPU &&
device != InferenceEngine::TargetDevice::eGPU)
device != InferenceEngine::TargetDevice::eGPU &&
device != InferenceEngine::TargetDevice::eMYRIAD)
CV_Error(Error::StsNotImplemented, "");
targetDevice = device;
}
@ -352,6 +344,11 @@ void InfEngineBackendNet::init(int 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;
case DNN_TARGET_MYRIAD:
{
setPrecision(InferenceEngine::Precision::FP16);
setTargetDevice(InferenceEngine::TargetDevice::eMYRIAD); break;
}
default:
CV_Error(Error::StsError, format("Unknown target identifier: %d", targetId));
}
@ -368,7 +365,7 @@ void InfEngineBackendNet::initPlugin(InferenceEngine::ICNNNetwork& net)
InferenceEngine::ResponseDesc resp;
plugin = InferenceEngine::PluginDispatcher({""}).getSuitablePlugin(targetDevice);
if (infEngineVersion() > 5855 && targetDevice == InferenceEngine::TargetDevice::eCPU)
if (targetDevice == InferenceEngine::TargetDevice::eCPU)
{
#ifdef _WIN32
InferenceEngine::IExtensionPtr extension =

83
modules/dnn/test/test_backends.cpp
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@ -49,7 +49,14 @@ public:
throw SkipTestException("OpenCL is not available/disabled in OpenCV");
}
}
if (target == DNN_TARGET_OPENCL_FP16)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
{
if (!checkMyriadTarget())
{
throw SkipTestException("Myriad is not available/disabled in OpenCV");
}
}
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)
{
l1 = l1 == 0.0 ? 4e-3 : l1;
lInf = lInf == 0.0 ? 2e-2 : lInf;
@ -80,10 +87,7 @@ public:
}
Mat out = net.forward(outputLayer).clone();
if (outputLayer == "detection_out")
normAssertDetections(outDefault, out, "First run", 0.2, l1, lInf);
else
normAssert(outDefault, out, "First run", l1, lInf);
check(outDefault, out, outputLayer, l1, lInf, "First run");
// Test 2: change input.
float* inpData = (float*)inp.data;
@ -97,18 +101,33 @@ public:
net.setInput(inp);
outDefault = netDefault.forward(outputLayer).clone();
out = net.forward(outputLayer).clone();
check(outDefault, out, outputLayer, l1, lInf, "Second run");
}
void check(Mat& ref, Mat& out, const std::string& outputLayer, double l1, double lInf, const char* msg)
{
if (outputLayer == "detection_out")
normAssertDetections(outDefault, out, "Second run", 0.2, l1, lInf);
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
{
// Inference Engine produces detections terminated by a row which starts from -1.
out = out.reshape(1, out.total() / 7);
int numDetections = 0;
while (numDetections < out.rows && out.at<float>(numDetections, 0) != -1)
{
numDetections += 1;
}
out = out.rowRange(0, numDetections);
}
normAssertDetections(ref, out, msg, 0.2, l1, lInf);
}
else
normAssert(outDefault, out, "Second run", l1, lInf);
normAssert(ref, out, msg, l1, lInf);
}
};
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" :
@ -158,8 +177,7 @@ TEST_P(DNNTestNetwork, ENet)
TEST_P(DNNTestNetwork, MobileNet_SSD_Caffe)
{
if (backend == DNN_BACKEND_HALIDE ||
backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_CPU)
if (backend == DNN_BACKEND_HALIDE)
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);
@ -170,10 +188,11 @@ TEST_P(DNNTestNetwork, MobileNet_SSD_Caffe)
inp, "detection_out", "", l1, lInf);
}
// TODO: update MobileNet model.
TEST_P(DNNTestNetwork, MobileNet_SSD_TensorFlow)
{
if (backend == DNN_BACKEND_HALIDE ||
backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_CPU)
backend == DNN_BACKEND_INFERENCE_ENGINE)
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);
@ -185,31 +204,38 @@ TEST_P(DNNTestNetwork, MobileNet_SSD_TensorFlow)
TEST_P(DNNTestNetwork, SSD_VGG16)
{
if ((backend == DNN_BACKEND_DEFAULT && target == DNN_TARGET_OPENCL_FP16) ||
(backend == DNN_BACKEND_HALIDE && target == DNN_TARGET_CPU) ||
(backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_CPU))
if (backend == DNN_BACKEND_HALIDE && target == DNN_TARGET_CPU)
throw SkipTestException("");
double scoreThreshold = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.0252 : 0.0;
Mat sample = imread(findDataFile("dnn/street.png", false));
Mat inp = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false);
processNet("dnn/VGG_ILSVRC2016_SSD_300x300_iter_440000.caffemodel",
"dnn/ssd_vgg16.prototxt", Size(300, 300), "detection_out");
"dnn/ssd_vgg16.prototxt", inp, "detection_out", "", scoreThreshold);
}
TEST_P(DNNTestNetwork, OpenPose_pose_coco)
{
if (backend == DNN_BACKEND_HALIDE) throw SkipTestException("");
if (backend == DNN_BACKEND_HALIDE ||
backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException("");
processNet("dnn/openpose_pose_coco.caffemodel", "dnn/openpose_pose_coco.prototxt",
Size(368, 368));
}
TEST_P(DNNTestNetwork, OpenPose_pose_mpi)
{
if (backend == DNN_BACKEND_HALIDE) throw SkipTestException("");
if (backend == DNN_BACKEND_HALIDE ||
backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException("");
processNet("dnn/openpose_pose_mpi.caffemodel", "dnn/openpose_pose_mpi.prototxt",
Size(368, 368));
}
TEST_P(DNNTestNetwork, OpenPose_pose_mpi_faster_4_stages)
{
if (backend == DNN_BACKEND_HALIDE) throw SkipTestException("");
if (backend == DNN_BACKEND_HALIDE ||
backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException("");
// 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",
@ -226,11 +252,13 @@ TEST_P(DNNTestNetwork, OpenFace)
TEST_P(DNNTestNetwork, opencv_face_detector)
{
if (backend == DNN_BACKEND_HALIDE ||
backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_CPU)
if (backend == DNN_BACKEND_HALIDE)
throw SkipTestException("");
Size inpSize;
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
inpSize = Size(300, 300);
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);
Mat inp = blobFromImage(img, 1.0, inpSize, Scalar(104.0, 177.0, 123.0), false, false);
processNet("dnn/opencv_face_detector.caffemodel", "dnn/opencv_face_detector.prototxt",
inp, "detection_out");
}
@ -238,12 +266,13 @@ TEST_P(DNNTestNetwork, opencv_face_detector)
TEST_P(DNNTestNetwork, Inception_v2_SSD_TensorFlow)
{
if (backend == DNN_BACKEND_HALIDE ||
backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_CPU)
(backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL) ||
(backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16))
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);
float l1 = (backend == DNN_BACKEND_DEFAULT && target == DNN_TARGET_OPENCL_FP16) ? 0.008 : 0.0;
float lInf = (backend == DNN_BACKEND_DEFAULT && target == DNN_TARGET_OPENCL_FP16) ? 0.07 : 0.0;
float l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.008 : 0.0;
float lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.07 : 0.0;
processNet("dnn/ssd_inception_v2_coco_2017_11_17.pb", "dnn/ssd_inception_v2_coco_2017_11_17.pbtxt",
inp, "detection_out", "", l1, lInf);
}
@ -252,7 +281,8 @@ TEST_P(DNNTestNetwork, DenseNet_121)
{
if ((backend == DNN_BACKEND_HALIDE) ||
(backend == DNN_BACKEND_DEFAULT && target == DNN_TARGET_OPENCL_FP16) ||
(backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16))
(backend == DNN_BACKEND_INFERENCE_ENGINE && (target == DNN_TARGET_OPENCL_FP16 ||
target == DNN_TARGET_MYRIAD)))
throw SkipTestException("");
processNet("dnn/DenseNet_121.caffemodel", "dnn/DenseNet_121.prototxt", Size(224, 224), "", "caffe");
}
@ -266,6 +296,7 @@ const tuple<DNNBackend, DNNTarget> testCases[] = {
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),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_MYRIAD),
#endif
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_DEFAULT, DNN_TARGET_OPENCL),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_DEFAULT, DNN_TARGET_OPENCL_FP16)

22
modules/dnn/test/test_common.hpp
Unescape View File

@ -147,6 +147,28 @@ inline void normAssertDetections(cv::Mat ref, cv::Mat out, const char *comment =
testBoxes, comment, confThreshold, scores_diff, boxes_iou_diff);
}
inline bool checkMyriadTarget()
{
#ifndef HAVE_INF_ENGINE
return false;
#endif
cv::dnn::Net net;
cv::dnn::LayerParams lp;
net.addLayerToPrev("testLayer", "Identity", lp);
net.setPreferableBackend(cv::dnn::DNN_BACKEND_INFERENCE_ENGINE);
net.setPreferableTarget(cv::dnn::DNN_TARGET_MYRIAD);
net.setInput(cv::Mat::zeros(1, 1, CV_32FC1));
try
{
net.forward();
}
catch(...)
{
return false;
}
return true;
}
inline bool readFileInMemory(const std::string& filename, std::string& content)
{
std::ios::openmode mode = std::ios::in | std::ios::binary;

63
modules/dnn/test/test_darknet_importer.cpp
Unescape View File

@ -71,13 +71,31 @@ static void testDarknetModel(const std::string& cfg, const std::string& weights,
const std::vector<int>& refClassIds,
const std::vector<float>& refConfidences,
const std::vector<Rect2d>& refBoxes,
int targetId, float confThreshold = 0.24)
int backendId, int targetId, float scoreDiff = 0.0,
float iouDiff = 0.0, float confThreshold = 0.24)
{
if (backendId == DNN_BACKEND_DEFAULT && targetId == DNN_TARGET_OPENCL)
{
#ifdef HAVE_OPENCL
if (!cv::ocl::useOpenCL())
#endif
{
throw SkipTestException("OpenCL is not available/disabled in OpenCV");
}
}
if (backendId == DNN_BACKEND_INFERENCE_ENGINE && targetId == DNN_TARGET_MYRIAD)
{
if (!checkMyriadTarget())
{
throw SkipTestException("Myriad is not available/disabled in OpenCV");
}
}
Mat sample = imread(_tf("dog416.png"));
Mat inp = blobFromImage(sample, 1.0/255, Size(416, 416), Scalar(), true, false);
Net net = readNet(findDataFile("dnn/" + cfg, false),
findDataFile("dnn/" + weights, false));
net.setPreferableBackend(backendId);
net.setPreferableTarget(targetId);
net.setInput(inp);
std::vector<Mat> outs;
@ -108,14 +126,17 @@ static void testDarknetModel(const std::string& cfg, const std::string& weights,
}
}
normAssertDetections(refClassIds, refConfidences, refBoxes, classIds,
confidences, boxes, "", confThreshold, 8e-5, 3e-5);
confidences, boxes, "", confThreshold, scoreDiff, iouDiff);
}
typedef testing::TestWithParam<DNNTarget> Test_Darknet_nets;
typedef testing::TestWithParam<tuple<DNNBackend, DNNTarget> > Test_Darknet_nets;
TEST_P(Test_Darknet_nets, YoloVoc)
{
int targetId = GetParam();
int backendId = get<0>(GetParam());
int targetId = get<1>(GetParam());
if (backendId == DNN_BACKEND_INFERENCE_ENGINE && targetId == DNN_TARGET_MYRIAD)
throw SkipTestException("");
std::vector<cv::String> outNames(1, "detection_out");
std::vector<int> classIds(3);
@ -124,26 +145,34 @@ TEST_P(Test_Darknet_nets, YoloVoc)
classIds[0] = 6; confidences[0] = 0.750469f; boxes[0] = Rect2d(0.577374, 0.127391, 0.325575, 0.173418); // a car
classIds[1] = 1; confidences[1] = 0.780879f; boxes[1] = Rect2d(0.270762, 0.264102, 0.461713, 0.48131); // a bycicle
classIds[2] = 11; confidences[2] = 0.901615f; boxes[2] = Rect2d(0.1386, 0.338509, 0.282737, 0.60028); // a dog
double scoreDiff = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 7e-3 : 8e-5;
double iouDiff = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 0.013 : 3e-5;
testDarknetModel("yolo-voc.cfg", "yolo-voc.weights", outNames,
classIds, confidences, boxes, targetId);
classIds, confidences, boxes, backendId, targetId, scoreDiff, iouDiff);
}
TEST_P(Test_Darknet_nets, TinyYoloVoc)
{
int targetId = GetParam();
int backendId = get<0>(GetParam());
int targetId = get<1>(GetParam());
std::vector<cv::String> outNames(1, "detection_out");
std::vector<int> classIds(2);
std::vector<float> confidences(2);
std::vector<Rect2d> boxes(2);
classIds[0] = 6; confidences[0] = 0.761967f; boxes[0] = Rect2d(0.579042, 0.159161, 0.31544, 0.160779); // a car
classIds[1] = 11; confidences[1] = 0.780595f; boxes[1] = Rect2d(0.129696, 0.386467, 0.315579, 0.534527); // a dog
double scoreDiff = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 8e-3 : 8e-5;
double iouDiff = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 8e-3 : 3e-5;
testDarknetModel("tiny-yolo-voc.cfg", "tiny-yolo-voc.weights", outNames,
classIds, confidences, boxes, targetId);
classIds, confidences, boxes, backendId, targetId, scoreDiff, iouDiff);
}
TEST_P(Test_Darknet_nets, YOLOv3)
{
int targetId = GetParam();
int backendId = get<0>(GetParam());
int targetId = get<1>(GetParam());
if (backendId == DNN_BACKEND_INFERENCE_ENGINE && targetId == DNN_TARGET_MYRIAD)
throw SkipTestException("");
std::vector<cv::String> outNames(3);
outNames[0] = "yolo_82";
outNames[1] = "yolo_94";
@ -155,11 +184,25 @@ TEST_P(Test_Darknet_nets, YOLOv3)
classIds[0] = 7; confidences[0] = 0.952983f; boxes[0] = Rect2d(0.614622, 0.150257, 0.286747, 0.138994); // a truck
classIds[1] = 1; confidences[1] = 0.987908f; boxes[1] = Rect2d(0.150913, 0.221933, 0.591342, 0.524327); // a bycicle
classIds[2] = 16; confidences[2] = 0.998836f; boxes[2] = Rect2d(0.160024, 0.389964, 0.257861, 0.553752); // a dog (COCO)
double scoreDiff = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 4e-3 : 8e-5;
double iouDiff = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 0.011 : 3e-5;
testDarknetModel("yolov3.cfg", "yolov3.weights", outNames,
classIds, confidences, boxes, targetId);
classIds, confidences, boxes, backendId, targetId, scoreDiff, iouDiff);
}
INSTANTIATE_TEST_CASE_P(/**/, Test_Darknet_nets, availableDnnTargets());
const tuple<DNNBackend, DNNTarget> testCases[] = {
#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),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_MYRIAD),
#endif
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_DEFAULT, DNN_TARGET_CPU),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_DEFAULT, DNN_TARGET_OPENCL),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_DEFAULT, DNN_TARGET_OPENCL_FP16)
};
INSTANTIATE_TEST_CASE_P(/**/, Test_Darknet_nets, testing::ValuesIn(testCases));
static void testDarknetLayer(const std::string& name, bool hasWeights = false)
{

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, DNN_TARGET_OPENCL_FP16)
CV_ENUM(DNNTarget, DNN_TARGET_CPU, DNN_TARGET_OPENCL, DNN_TARGET_OPENCL_FP16, DNN_TARGET_MYRIAD)
static testing::internal::ParamGenerator<DNNTarget> availableDnnTargets()
{

2
samples/dnn/classification.cpp
Unescape View File

@ -23,7 +23,7 @@ const char* keys =
"{ backend | 0 | Choose one of computation backends: "
"0: default C++ backend, "
"1: Halide language (http://halide-lang.org/), "
"2: Intel's Deep Learning Inference Engine (https://software.seek.intel.com/deep-learning-deployment)}"
"2: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit)}"
"{ target | 0 | Choose one of target computation devices: "
"0: CPU target (by default),"
"1: OpenCL }";

2
samples/dnn/classification.py
Unescape View File

@ -34,7 +34,7 @@ parser.add_argument('--backend', choices=backends, default=cv.dnn.DNN_BACKEND_DE
help="Choose one of computation backends: "
"%d: default C++ backend, "
"%d: Halide language (http://halide-lang.org/), "
"%d: Intel's Deep Learning Inference Engine (https://software.seek.intel.com/deep-learning-deployment)" % backends)
"%d: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit)" % backends)
parser.add_argument('--target', choices=targets, default=cv.dnn.DNN_TARGET_CPU, type=int,
help='Choose one of target computation devices: '
'%d: CPU target (by default), '

2
samples/dnn/object_detection.cpp
Unescape View File

@ -25,7 +25,7 @@ const char* keys =
"{ backend | 0 | Choose one of computation backends: "
"0: default C++ backend, "
"1: Halide language (http://halide-lang.org/), "
"2: Intel's Deep Learning Inference Engine (https://software.seek.intel.com/deep-learning-deployment)}"
"2: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit)}"
"{ target | 0 | Choose one of target computation devices: "
"0: CPU target (by default),"
"1: OpenCL }";

2
samples/dnn/object_detection.py
Unescape View File

@ -35,7 +35,7 @@ parser.add_argument('--backend', choices=backends, default=cv.dnn.DNN_BACKEND_DE
help="Choose one of computation backends: "
"%d: default C++ backend, "
"%d: Halide language (http://halide-lang.org/), "
"%d: Intel's Deep Learning Inference Engine (https://software.seek.intel.com/deep-learning-deployment)" % backends)
"%d: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit)" % backends)
parser.add_argument('--target', choices=targets, default=cv.dnn.DNN_TARGET_CPU, type=int,
help='Choose one of target computation devices: '
'%d: CPU target (by default), '

2
samples/dnn/segmentation.cpp
Unescape View File

@ -26,7 +26,7 @@ const char* keys =
"{ backend | 0 | Choose one of computation backends: "
"0: default C++ backend, "
"1: Halide language (http://halide-lang.org/), "
"2: Intel's Deep Learning Inference Engine (https://software.seek.intel.com/deep-learning-deployment)}"
"2: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit)}"
"{ target | 0 | Choose one of target computation devices: "
"0: CPU target (by default),"
"1: OpenCL }";

2
samples/dnn/segmentation.py
Unescape View File

@ -36,7 +36,7 @@ parser.add_argument('--backend', choices=backends, default=cv.dnn.DNN_BACKEND_DE
help="Choose one of computation backends: "
"%d: default C++ backend, "
"%d: Halide language (http://halide-lang.org/), "
"%d: Intel's Deep Learning Inference Engine (https://software.seek.intel.com/deep-learning-deployment)" % backends)
"%d: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit)" % backends)
parser.add_argument('--target', choices=targets, default=cv.dnn.DNN_TARGET_CPU, type=int,
help='Choose one of target computation devices: '
'%d: CPU target (by default), '

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