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Merge pull request #14792 from dkurt:dnn_ie_min_version_r5

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* Remove Inference Engine 2018R3 and 2018R4

* Fix 2018R5
This commit is contained in:
Dmitry Kurtaev
2019-06-14 18:17:02 +03:00
committed by Alexander Alekhin
parent 54a14c877c
commit eba696a41e
35 changed files with 216 additions and 1608 deletions
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modules/dnn/src/layers/convolution_layer.cpp
+6 -94
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@@ -252,8 +252,7 @@ public:
{
if (kernel_size.size() == 3)
return preferableTarget == DNN_TARGET_CPU;
return INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R4) ||
(preferableTarget != DNN_TARGET_MYRIAD || dilation.width == dilation.height);
return (preferableTarget != DNN_TARGET_MYRIAD || dilation.width == dilation.height);
}
else
#endif
@@ -460,9 +459,9 @@ public:
return Ptr<BackendNode>();
}
#ifdef HAVE_INF_ENGINE
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> > &inputs) CV_OVERRIDE
{
#ifdef HAVE_INF_ENGINE
InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
CV_Assert(input->dims.size() == 4 || input->dims.size() == 5);
@@ -501,7 +500,6 @@ public:
ieBiases = wrapToInfEngineBlob(biasesMat, {(size_t)outCn}, InferenceEngine::Layout::C);
}
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
InferenceEngine::Builder::ConvolutionLayer ieLayer(name);
ieLayer.setKernel(kernel_size);
@@ -521,51 +519,8 @@ public:
l.getParameters()["auto_pad"] = padMode == "VALID" ? std::string("valid") : std::string("same_upper");
return Ptr<BackendNode>(new InfEngineBackendNode(l));
#else
InferenceEngine::LayerParams lp;
lp.name = name;
lp.type = "Convolution";
lp.precision = InferenceEngine::Precision::FP32;
std::shared_ptr<InferenceEngine::ConvolutionLayer> ieLayer(new InferenceEngine::ConvolutionLayer(lp));
#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2018R3)
ieLayer->_kernel.insert(InferenceEngine::X_AXIS, kernel.width);
ieLayer->_kernel.insert(InferenceEngine::Y_AXIS, kernel.height);
ieLayer->_stride.insert(InferenceEngine::X_AXIS, stride.width);
ieLayer->_stride.insert(InferenceEngine::Y_AXIS, stride.height);
ieLayer->_padding.insert(InferenceEngine::X_AXIS, pad.width);
ieLayer->_padding.insert(InferenceEngine::Y_AXIS, pad.height);
ieLayer->_pads_end.insert(InferenceEngine::X_AXIS, pad.width);
ieLayer->_pads_end.insert(InferenceEngine::Y_AXIS, pad.height);
ieLayer->_dilation.insert(InferenceEngine::X_AXIS, dilation.width);
ieLayer->_dilation.insert(InferenceEngine::Y_AXIS, dilation.height);
ieLayer->params["output"] = format("%d", outCn);
ieLayer->params["kernel"] = format("%d,%d,%d,%d", outCn, inpGroupCn, kernel.height, kernel.width);
ieLayer->params["pads_begin"] = format("%d,%d", pad.height, pad.width);
ieLayer->params["pads_end"] = format("%d,%d", pad.height, pad.width);
ieLayer->params["strides"] = format("%d,%d", stride.height, stride.width);
ieLayer->params["dilations"] = format("%d,%d", dilation.height, dilation.width);
#else
ieLayer->_kernel_x = kernel.width;
ieLayer->_kernel_y = kernel.height;
ieLayer->_stride_x = stride.width;
ieLayer->_stride_y = stride.height;
ieLayer->_padding_x = pad.width;
ieLayer->_padding_y = pad.height;
ieLayer->_dilation_x = dilation.width;
ieLayer->_dilation_y = dilation.height;
#endif
ieLayer->_out_depth = outCn;
ieLayer->_group = group;
ieLayer->_weights = ieWeights;
if (ieBiases)
ieLayer->_biases = ieBiases;
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif
#endif // HAVE_INF_ENGINE
return Ptr<BackendNode>();
}
#endif // HAVE_INF_ENGINE
class ParallelConv : public cv::ParallelLoopBody
{
@@ -1184,7 +1139,7 @@ public:
if (kernel_size.size() == 3)
CV_Error(Error::StsNotImplemented, "Unsupported deconvolution3D layer");
if (INF_ENGINE_RELEASE >= 2018050000 && (adjustPad.height || adjustPad.width))
if (adjustPad.height || adjustPad.width)
{
if (padMode.empty())
{
@@ -1793,9 +1748,9 @@ public:
return Ptr<BackendNode>();
}
#ifdef HAVE_INF_ENGINE
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> > &) CV_OVERRIDE
{
#ifdef HAVE_INF_ENGINE
auto ieWeights = wrapToInfEngineBlob(blobs[0], InferenceEngine::Layout::OIHW);
if (fusedWeights)
{
@@ -1809,7 +1764,6 @@ public:
transpose(weightsMat, newWeights);
}
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
const int outGroupCn = blobs[0].size[1]; // Weights are in IOHW layout
const int group = numOutput / outGroupCn;
@@ -1837,50 +1791,8 @@ public:
if (hasBias())
addConstantData("biases", wrapToInfEngineBlob(biasesMat, {(size_t)numOutput}, InferenceEngine::Layout::C), l);
return Ptr<BackendNode>(new InfEngineBackendNode(l));
#else
const int outGroupCn = blobs[0].size[1]; // Weights are in IOHW layout
const int group = numOutput / outGroupCn;
InferenceEngine::LayerParams lp;
lp.name = name;
lp.type = "Deconvolution";
lp.precision = InferenceEngine::Precision::FP32;
std::shared_ptr<InferenceEngine::DeconvolutionLayer> ieLayer(new InferenceEngine::DeconvolutionLayer(lp));
#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2018R3)
ieLayer->_kernel.insert(InferenceEngine::X_AXIS, kernel.width);
ieLayer->_kernel.insert(InferenceEngine::Y_AXIS, kernel.height);
ieLayer->_stride.insert(InferenceEngine::X_AXIS, stride.width);
ieLayer->_stride.insert(InferenceEngine::Y_AXIS, stride.height);
ieLayer->_padding.insert(InferenceEngine::X_AXIS, pad.width);
ieLayer->_padding.insert(InferenceEngine::Y_AXIS, pad.height);
ieLayer->_pads_end.insert(InferenceEngine::X_AXIS, pad.width);
ieLayer->_pads_end.insert(InferenceEngine::Y_AXIS, pad.height);
ieLayer->_dilation.insert(InferenceEngine::X_AXIS, dilation.width);
ieLayer->_dilation.insert(InferenceEngine::Y_AXIS, dilation.height);
#else
ieLayer->_kernel_x = kernel.width;
ieLayer->_kernel_y = kernel.height;
ieLayer->_stride_x = stride.width;
ieLayer->_stride_y = stride.height;
ieLayer->_padding_x = pad.width;
ieLayer->_padding_y = pad.height;
ieLayer->_dilation_x = dilation.width;
ieLayer->_dilation_y = dilation.height;
#endif
ieLayer->_out_depth = numOutput;
ieLayer->_group = group;
ieLayer->_weights = wrapToInfEngineBlob(blobs[0], InferenceEngine::Layout::OIHW);
if (hasBias())
{
ieLayer->_biases = wrapToInfEngineBlob(blobs[1], {(size_t)numOutput}, InferenceEngine::Layout::C);
}
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif
#endif // HAVE_INF_ENGINE
return Ptr<BackendNode>();
}
#endif // HAVE_INF_ENGINE
virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
const std::vector<MatShape> &outputs) const CV_OVERRIDE