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Intel Inference Engine deep learning backend (#10608)

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* Intel Inference Engine deep learning backend.

* OpenFace network using Inference Engine backend
This commit is contained in:
Dmitry Kurtaev
2018-02-06 11:57:35 +03:00
committed by Vadim Pisarevsky
parent 292dfc2d72
commit 10e1de74d2
26 changed files with 1379 additions and 49 deletions
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modules/dnn/perf/perf_net.cpp
+57 -18
View File
@@ -13,14 +13,7 @@
namespace
{
#ifdef HAVE_HALIDE
#define TEST_DNN_BACKEND DNN_BACKEND_DEFAULT, DNN_BACKEND_HALIDE
#else
#define TEST_DNN_BACKEND DNN_BACKEND_DEFAULT
#endif
#define TEST_DNN_TARGET DNN_TARGET_CPU, DNN_TARGET_OPENCL
CV_ENUM(DNNBackend, DNN_BACKEND_DEFAULT, DNN_BACKEND_HALIDE)
CV_ENUM(DNNBackend, DNN_BACKEND_DEFAULT, DNN_BACKEND_HALIDE, DNN_BACKEND_INFERENCE_ENGINE)
CV_ENUM(DNNTarget, DNN_TARGET_CPU, DNN_TARGET_OPENCL)
class DNNTestNetwork : public ::perf::TestBaseWithParam< tuple<DNNBackend, DNNTarget> >
@@ -31,13 +24,16 @@ public:
dnn::Net net;
void processNet(std::string weights, std::string proto, std::string halide_scheduler,
const Mat& input, const std::string& outputLayer,
const std::string& framework)
DNNTestNetwork()
{
backend = (dnn::Backend)(int)get<0>(GetParam());
target = (dnn::Target)(int)get<1>(GetParam());
}
void processNet(std::string weights, std::string proto, std::string halide_scheduler,
const Mat& input, const std::string& outputLayer,
const std::string& framework)
{
if (backend == DNN_BACKEND_DEFAULT && target == DNN_TARGET_OPENCL)
{
#if defined(HAVE_OPENCL)
@@ -47,6 +43,8 @@ public:
throw ::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);
@@ -117,7 +115,7 @@ PERF_TEST_P_(DNNTestNetwork, GoogLeNet)
"", Mat(cv::Size(224, 224), CV_32FC3), "prob", "caffe");
}
PERF_TEST_P_(DNNTestNetwork, ResNet50)
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");
@@ -131,6 +129,7 @@ PERF_TEST_P_(DNNTestNetwork, SqueezeNet_v1_1)
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");
@@ -138,12 +137,14 @@ PERF_TEST_P_(DNNTestNetwork, Inception_5h)
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");
}
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");
}
@@ -162,15 +163,53 @@ PERF_TEST_P_(DNNTestNetwork, MobileNet_SSD_Caffe)
PERF_TEST_P_(DNNTestNetwork, MobileNet_SSD_TensorFlow)
{
if (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), "", "tensorflow");
}
INSTANTIATE_TEST_CASE_P(/*nothing*/, DNNTestNetwork,
testing::Combine(
::testing::Values(TEST_DNN_BACKEND),
DNNTarget::all()
)
);
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");
}
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");
}
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");
}
PERF_TEST_P_(DNNTestNetwork, OpenPose_pose_mpi_faster_4_stages)
{
if (backend == DNN_BACKEND_HALIDE) 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", "",
Mat(cv::Size(368, 368), CV_32FC3), "", "caffe");
}
const tuple<DNNBackend, DNNTarget> testCases[] = {
#ifdef HAVE_HALIDE
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_HALIDE, DNN_TARGET_CPU),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_HALIDE, DNN_TARGET_OPENCL),
#endif
#ifdef HAVE_INF_ENGINE
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_CPU),
#endif
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_DEFAULT, DNN_TARGET_CPU),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_DEFAULT, DNN_TARGET_OPENCL)
};
INSTANTIATE_TEST_CASE_P(/*nothing*/, DNNTestNetwork, testing::ValuesIn(testCases));
} // namespace