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Merge pull request #14626 from dkurt:dnn_ie_slice_layer

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This commit is contained in:
Alexander Alekhin 2019-05-27 16:27:14 +00:00
commit 483e9f341c
3 changed files with 45 additions and 26 deletions
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3
modules/dnn/perf/perf_net.cpp
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@ -214,8 +214,7 @@ PERF_TEST_P_(DNNTestNetwork, EAST_text_detection)
PERF_TEST_P_(DNNTestNetwork, FastNeuralStyle_eccv16)
{
if (backend == DNN_BACKEND_HALIDE ||
(backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16) ||
(backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD))
(backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16))
throw SkipTestException("");
processNet("dnn/fast_neural_style_eccv16_starry_night.t7", "", "", Mat(cv::Size(320, 240), CV_32FC3));
}

66
modules/dnn/src/layers/slice_layer.cpp
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@ -110,15 +110,9 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
{
return INF_ENGINE_VER_MAJOR_LT(INF_ENGINE_RELEASE_2018R5) &&
sliceRanges.size() == 1 && sliceRanges[0].size() == 4;
}
else
#endif
return backendId == DNN_BACKEND_OPENCV;
return backendId == DNN_BACKEND_OPENCV ||
(backendId == DNN_BACKEND_INFERENCE_ENGINE &&
sliceRanges.size() == 1 && sliceRanges[0].size() == 4);
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
@ -264,39 +258,65 @@ public:
#ifdef HAVE_INF_ENGINE
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
{
#if INF_ENGINE_VER_MAJOR_LT(INF_ENGINE_RELEASE_2018R5)
InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
InferenceEngine::LayerParams lp;
lp.name = name;
lp.type = "Crop";
lp.precision = InferenceEngine::Precision::FP32;
std::shared_ptr<InferenceEngine::CropLayer> ieLayer(new InferenceEngine::CropLayer(lp));
CV_Assert(sliceRanges.size() == 1);
std::vector<size_t> axes, offsets, dims;
int from, to, step;
int numDims = sliceRanges[0].size();
if (preferableTarget == DNN_TARGET_MYRIAD)
{
from = 1;
to = sliceRanges[0].size() + 1;
to = numDims;
step = 1;
}
else
{
from = sliceRanges[0].size() - 1;
from = numDims - 1;
to = -1;
step = -1;
}
for (int i = from; i != to; i += step)
{
ieLayer->axis.push_back(i);
ieLayer->offset.push_back(sliceRanges[0][i].start);
ieLayer->dim.push_back(sliceRanges[0][i].end - sliceRanges[0][i].start);
axes.push_back(i);
offsets.push_back(sliceRanges[0][i].start);
dims.push_back(sliceRanges[0][i].size());
}
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
std::vector<size_t> outShape(numDims);
for (int i = 0; i < numDims; ++i)
outShape[numDims - 1 - i] = sliceRanges[0][i].size();
InferenceEngine::Builder::Layer ieLayer(name);
ieLayer.setName(name);
ieLayer.setType("Crop");
ieLayer.getParameters()["axis"] = axes;
ieLayer.getParameters()["dim"] = dims;
ieLayer.getParameters()["offset"] = offsets;
ieLayer.setInputPorts(std::vector<InferenceEngine::Port>(2));
ieLayer.setOutputPorts(std::vector<InferenceEngine::Port>(1));
ieLayer.getInputPorts()[1].setParameter("type", "weights");
// Fake blob which will be moved to inputs (as weights).
auto shapeSource = InferenceEngine::make_shared_blob<float>(
InferenceEngine::Precision::FP32,
InferenceEngine::Layout::ANY, outShape);
shapeSource->allocate();
addConstantData("weights", shapeSource, ieLayer);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#else
return Ptr<BackendNode>();
InferenceEngine::LayerParams lp;
lp.name = name;
lp.type = "Crop";
lp.precision = InferenceEngine::Precision::FP32;
std::shared_ptr<InferenceEngine::CropLayer> ieLayer(new InferenceEngine::CropLayer(lp));
ieLayer->axis = axes;
ieLayer->offset = offsets;
ieLayer->dim = dims;
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif // IE < R5
return Ptr<BackendNode>();
}
#endif
};

2
modules/dnn/test/test_backends.cpp
View File

@ -396,7 +396,7 @@ TEST_P(DNNTestNetwork, FastNeuralStyle_eccv16)
Mat img = imread(findDataFile("dnn/googlenet_1.png", false));
Mat inp = blobFromImage(img, 1.0, Size(320, 240), Scalar(103.939, 116.779, 123.68), false, false);
// Output image has values in range [-143.526, 148.539].
float l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.3 : 4e-5;
float l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.4 : 4e-5;
float lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 7.28 : 2e-3;
processNet("dnn/fast_neural_style_eccv16_starry_night.t7", "", inp, "", "", l1, lInf);
}