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Merge remote-tracking branch 'upstream/3.4' into merge-3.4

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This commit is contained in:
Alexander Alekhin
2019-06-05 19:11:22 +03:00
parent e329c84d5e 51631b90af
commit f3de2b4be7
155 changed files with 5856 additions and 1800 deletions
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modules/dnn/src/layers/convolution_layer.cpp
+74 -34
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@@ -62,7 +62,7 @@ namespace dnn
class BaseConvolutionLayerImpl : public ConvolutionLayer
{
public:
bool newWeightAndBias;
bool fusedWeights, fusedBias;
std::vector<double> weightsMultipliers;
BaseConvolutionLayerImpl(const LayerParams &params)
{
@@ -91,7 +91,8 @@ public:
CV_Assert(adjustPad.width < stride.width &&
adjustPad.height < stride.height);
}
newWeightAndBias = false;
fusedWeights = false;
fusedBias = false;
}
virtual void finalize(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr) CV_OVERRIDE
@@ -134,6 +135,8 @@ public:
}
pad = Size(pads_begin[1], pads_begin[0]);
}
fusedWeights = false;
fusedBias = false;
}
bool hasBias() const
@@ -156,6 +159,8 @@ public:
if (!w.empty() || !b.empty())
{
fuseWeights(w, b);
fusedWeights = fusedWeights || !w.empty();
fusedBias = fusedBias || (hasBias() && !w.empty()) || !b.empty();
return true;
}
return false;
@@ -216,7 +221,6 @@ public:
std::vector<float> biasvec;
std::vector<float> reluslope;
Ptr<ActivationLayer> activ;
bool fusedBias;
#ifdef HAVE_OPENCL
Ptr<OCL4DNNConvSpatial<float> > convolutionOp;
@@ -227,7 +231,6 @@ public:
#endif
ConvolutionLayerImpl(const LayerParams &params) : BaseConvolutionLayerImpl(params)
{
fusedBias = false;
#ifdef HAVE_OPENCL
newActiv = false;
activType = OCL4DNN_CONV_FUSED_ACTIV_NONE;
@@ -413,9 +416,6 @@ public:
for (int i = 0; i < outCn; ++i)
biasvec[i] += b.at<float>(i);
}
newWeightAndBias = !w.empty() || !b.empty();
fusedBias = hasBias() || !b.empty();
biasvec[outCn] = biasvec[outCn+1] = biasvec[outCn-1];
}
@@ -549,12 +549,12 @@ public:
InferenceEngine::Layout::NCDHW;
auto ieWeights = wrapToInfEngineBlob(blobs[0], layout);
if (newWeightAndBias)
if (fusedWeights)
{
if (weightsMat.isContinuous())
{
Mat fusedWeights = weightsMat.reshape(1, blobs[0].dims, blobs[0].size);
ieWeights = wrapToInfEngineBlob(fusedWeights, layout);
Mat cvWeights = weightsMat.reshape(1, blobs[0].dims, blobs[0].size);
ieWeights = wrapToInfEngineBlob(cvWeights, layout);
}
else
{
@@ -564,8 +564,8 @@ public:
ieWeights->allocate();
Mat newWeights = infEngineBlobToMat(ieWeights).reshape(1, outCn);
Mat fusedWeights = weightsMat.colRange(0, newWeights.cols);
fusedWeights.copyTo(newWeights);
Mat cvWeights = weightsMat.colRange(0, newWeights.cols);
cvWeights.copyTo(newWeights);
}
}
InferenceEngine::Blob::Ptr ieBiases;
@@ -1089,17 +1089,18 @@ public:
}
}
if ( newWeightAndBias )
if (fusedWeights)
{
weightsMat.copyTo(umat_blobs[0]);
if ( fusedBias )
{
if ( umat_blobs.size() < 2 )
umat_blobs.resize(2);
umat_blobs[1] = UMat(biasvec, true);
}
convolutionOp->setBias(fusedBias || hasBias());
newWeightAndBias = false;
fusedWeights = false;
}
if (fusedBias)
{
if ( umat_blobs.size() < 2 )
umat_blobs.resize(2);
umat_blobs[1] = UMat(biasvec, true);
convolutionOp->setBias(true);
fusedBias = false;
}
if ( newActiv )
@@ -1144,7 +1145,7 @@ public:
return convolutionOp->Forward(inpMat,
inputs.size() == 2 ? inputs[1] : UMat(),
umat_blobs[0],
(hasBias() || fusedBias) ? umat_blobs[1] : UMat(),
umat_blobs.size() > 1 ? umat_blobs[1] : UMat(),
outMat,
batch_size);
}
@@ -1249,16 +1250,34 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
#ifdef HAVE_INF_ENGINE
const int outGroupCn = blobs[0].size[1]; // Weights are in IOHW layout
const int group = numOutput / outGroupCn;
if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
{
if (kernel_size.size() == 3)
CV_Error(Error::StsNotImplemented, "Unsupported deconvolution3D layer");
if (INF_ENGINE_RELEASE >= 2018050000 && (adjustPad.height || adjustPad.width))
return false;
{
if (padMode.empty())
{
if (preferableTarget != DNN_TARGET_CPU && group != 1)
{
if ((adjustPad.height && pad.height) || (adjustPad.width && pad.width))
return false;
}
return pad.width >= adjustPad.width && pad.height >= adjustPad.height;
}
else if (padMode == "SAME")
{
return kernel.width >= pad.width + 1 + adjustPad.width &&
kernel.height >= pad.height + 1 + adjustPad.height;
}
else if (padMode == "VALID")
return false;
}
const int outGroupCn = blobs[0].size[1]; // Weights are in IOHW layout
const int group = numOutput / outGroupCn;
if (group != 1)
{
return preferableTarget == DNN_TARGET_CPU;
@@ -1376,8 +1395,6 @@ public:
{
cv::add(biasesMat, b.reshape(1, numOutput), biasesMat);
}
newWeightAndBias = !w.empty() || !b.empty();
}
class MatMulInvoker : public ParallelLoopBody
@@ -1645,14 +1662,15 @@ public:
if (umat_weights.empty())
{
if (newWeightAndBias)
{
if (fusedWeights)
weightsMat.copyTo(umat_weights);
else
transpose(blobs[0].reshape(1, inpCn), umat_weights);
if (fusedBias)
biasesMat.copyTo(umat_biases);
}
else
{
transpose(blobs[0].reshape(1, inpCn), umat_weights);
if (hasBias())
blobs[1].reshape(1, outCn).copyTo(umat_biases);
else
@@ -1852,6 +1870,19 @@ public:
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)
{
ieWeights = InferenceEngine::make_shared_blob<float>(
InferenceEngine::Precision::FP32, InferenceEngine::Layout::OIHW,
ieWeights->dims());
ieWeights->allocate();
int inpCn = blobs[0].size[0];
Mat newWeights = infEngineBlobToMat(ieWeights).reshape(1, inpCn);
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;
@@ -1862,14 +1893,23 @@ public:
ieLayer.setStrides(strides);
ieLayer.setDilation(dilations);
ieLayer.setPaddingsBegin(pads_begin);
ieLayer.setPaddingsEnd(pads_end);
if (padMode.empty())
{
ieLayer.setPaddingsEnd({pads_end[0] - adjust_pads[0], pads_end[1] - adjust_pads[1]});
}
else if (padMode == "SAME")
{
ieLayer.setPaddingsEnd({kernel_size[0] - pads_begin[0] - 1 - adjust_pads[0],
kernel_size[1] - pads_begin[1] - 1 - adjust_pads[1]});
}
ieLayer.setGroup((size_t)group);
ieLayer.setOutDepth((size_t)numOutput);
InferenceEngine::Builder::Layer l = ieLayer;
addConstantData("weights", wrapToInfEngineBlob(blobs[0], InferenceEngine::Layout::OIHW), l);
addConstantData("weights", ieWeights, l);
if (hasBias())
addConstantData("biases", wrapToInfEngineBlob(blobs[1], {(size_t)numOutput}, InferenceEngine::Layout::C), l);
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