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Merge pull request #13692 from dkurt:dnn_do_not_crash_myriad_in_tests

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Alexander Alekhin
2019-01-28 18:34:20 +00:00
parent 02e9636f1b ff775b2e54
commit 3585522b24
5 changed files with 54 additions and 63 deletions
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modules/dnn/src/dnn.cpp
+7 -1
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@@ -142,7 +142,13 @@ private:
#else
cv::dnn::Net net;
cv::dnn::LayerParams lp;
net.addLayerToPrev("testLayer", "Identity", lp);
lp.set("kernel_size", 1);
lp.set("num_output", 1);
lp.set("bias_term", false);
lp.type = "Convolution";
lp.name = "testLayer";
lp.blobs.push_back(Mat({1, 2, 1, 1}, CV_32F, Scalar(1)));
net.addLayerToPrev(lp.name, lp.type, lp);
net.setPreferableBackend(cv::dnn::DNN_BACKEND_INFERENCE_ENGINE);
net.setPreferableTarget(target);
static int inpDims[] = {1, 2, 3, 4};
modules/dnn/src/layers/convolution_layer.cpp
+14 -14
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@@ -481,13 +481,13 @@ public:
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
InferenceEngine::Builder::ConvolutionLayer ieLayer(name);
ieLayer.setKernel({kernel.height, kernel.width});
ieLayer.setStrides({stride.height, stride.width});
ieLayer.setDilation({dilation.height, dilation.width});
ieLayer.setPaddingsBegin({pad.height, pad.width});
ieLayer.setPaddingsEnd({pad.height, pad.width});
ieLayer.setGroup(group);
ieLayer.setOutDepth(outCn);
ieLayer.setKernel({(size_t)kernel.height, (size_t)kernel.width});
ieLayer.setStrides({(size_t)stride.height, (size_t)stride.width});
ieLayer.setDilation({(size_t)dilation.height, (size_t)dilation.width});
ieLayer.setPaddingsBegin({(size_t)pad.height, (size_t)pad.width});
ieLayer.setPaddingsEnd({(size_t)pad.height, (size_t)pad.width});
ieLayer.setGroup((size_t)group);
ieLayer.setOutDepth((size_t)outCn);
ieLayer.setWeights(ieWeights);
if (ieBiases)
@@ -1713,13 +1713,13 @@ public:
InferenceEngine::Builder::DeconvolutionLayer ieLayer(name);
ieLayer.setKernel({kernel.height, kernel.width});
ieLayer.setStrides({stride.height, stride.width});
ieLayer.setDilation({dilation.height, dilation.width});
ieLayer.setPaddingsBegin({pad.height, pad.width});
ieLayer.setPaddingsEnd({pad.height, pad.width});
ieLayer.setGroup(group);
ieLayer.setOutDepth(numOutput);
ieLayer.setKernel({(size_t)kernel.height, (size_t)kernel.width});
ieLayer.setStrides({(size_t)stride.height, (size_t)stride.width});
ieLayer.setDilation({(size_t)dilation.height, (size_t)dilation.width});
ieLayer.setPaddingsBegin({(size_t)pad.height, (size_t)pad.width});
ieLayer.setPaddingsEnd({(size_t)pad.height, (size_t)pad.width});
ieLayer.setGroup((size_t)group);
ieLayer.setOutDepth((size_t)numOutput);
ieLayer.setWeights(wrapToInfEngineBlob(blobs[0], InferenceEngine::Layout::OIHW));
if (hasBias())
modules/dnn/src/layers/pooling_layer.cpp
+4 -4
View File
@@ -261,10 +261,10 @@ public:
if (type == MAX || type == AVE)
{
InferenceEngine::Builder::PoolingLayer ieLayer(name);
ieLayer.setKernel({kernel.height, kernel.width});
ieLayer.setStrides({stride.height, stride.width});
ieLayer.setPaddingsBegin({pad_t, pad_l});
ieLayer.setPaddingsEnd({pad_b, pad_r});
ieLayer.setKernel({(size_t)kernel.height, (size_t)kernel.width});
ieLayer.setStrides({(size_t)stride.height, (size_t)stride.width});
ieLayer.setPaddingsBegin({(size_t)pad_t, (size_t)pad_l});
ieLayer.setPaddingsEnd({(size_t)pad_b, (size_t)pad_r});
ieLayer.setPoolingType(type == MAX ?
InferenceEngine::Builder::PoolingLayer::PoolingType::MAX :
InferenceEngine::Builder::PoolingLayer::PoolingType::AVG);
modules/dnn/src/op_inf_engine.cpp
+3 -3
View File
@@ -82,7 +82,7 @@ void InfEngineBackendNet::connect(const std::vector<Ptr<BackendWrapper> >& input
CV_Assert(it != layers.end());
const int layerId = it->second;
for (int i = 0; i < inpWrappers.size(); ++i)
for (size_t i = 0; i < inpWrappers.size(); ++i)
{
const auto& inp = inpWrappers[i];
const std::string& inpName = inp->dataPtr->name;
@@ -103,7 +103,7 @@ void InfEngineBackendNet::connect(const std::vector<Ptr<BackendWrapper> >& input
else
inpId = it->second;
netBuilder.connect(inpId, {layerId, i});
netBuilder.connect((size_t)inpId, {(size_t)layerId, i});
unconnectedLayersIds.erase(inpId);
}
CV_Assert(!outputs.empty());
@@ -119,7 +119,7 @@ void InfEngineBackendNet::init(int targetId)
for (int id : unconnectedLayersIds)
{
InferenceEngine::Builder::OutputLayer outLayer("myconv1");
netBuilder.addLayer({id}, outLayer);
netBuilder.addLayer({InferenceEngine::PortInfo(id)}, outLayer);
}
cnn = InferenceEngine::CNNNetwork(InferenceEngine::Builder::convertToICNNNetwork(netBuilder.build()));
}