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Replace convertFp16 from dnn::Net::setInput()

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This commit is contained in:
Dmitry Kurtaev 2018-07-09 14:35:54 +03:00
parent 0fd74fa177
commit 362d4f5395
1 changed files with 73 additions and 34 deletions
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107
modules/dnn/src/dnn.cpp
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@ -409,8 +409,44 @@ struct LayerData
struct DataLayer : public Layer
{
void finalize(const std::vector<Mat*>&, std::vector<Mat>&) CV_OVERRIDE {}
void forward(std::vector<Mat*>&, std::vector<Mat>&, std::vector<Mat> &) CV_OVERRIDE {}
void forward(InputArrayOfArrays inputs, OutputArrayOfArrays outputs, OutputArrayOfArrays internals) CV_OVERRIDE {}
void forward(InputArrayOfArrays inputs, OutputArrayOfArrays outputs, OutputArrayOfArrays internals) CV_OVERRIDE
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget),
forward_ocl(inputs, outputs, internals));
Layer::forward_fallback(inputs, outputs, internals);
}
void forward(std::vector<Mat*>&, std::vector<Mat>& outputs, std::vector<Mat> &) CV_OVERRIDE
{
for (int i = 0; i < inputsData.size(); ++i)
{
if (inputsData[i].type() == CV_32F && outputs[i].type() == CV_16S)
{
convertFp16(inputsData[i], outputs[i]);
}
}
}
#ifdef HAVE_OPENCL
bool forward_ocl(InputArrayOfArrays, OutputArrayOfArrays outputs_, OutputArrayOfArrays internals_)
{
if (outputs_.depth() == CV_16S)
{
std::vector<UMat> outputs;
outputs_.getUMatVector(outputs);
for (int i = 0; i < inputsData.size(); ++i)
{
convertFp16(inputsData[i], outputs[i]);
}
}
return true;
}
#endif
int outputNameToIndex(const String& tgtName) CV_OVERRIDE
{
@ -434,6 +470,7 @@ struct DataLayer : public Layer
}
std::vector<String> outNames;
std::vector<Mat> inputsData;
};
struct BlobManager
@ -848,9 +885,6 @@ struct Net::Impl
poolingLayer->computeMaxIdx = true;
}
}
it = layers.find(0);
CV_Assert(it != layers.end());
it->second.skip = true;
layersTimings.clear();
}
@ -1355,15 +1389,27 @@ struct Net::Impl
allocateLayer(*i, layersShapes);
//bind inputs
ld.inputBlobs.resize(ninputs);
ld.inputBlobsWrappers.resize(ninputs);
for (size_t i = 0; i < ninputs; i++)
if (ld.id == 0) // DataLayer
{
LayerPin from = ld.inputBlobsId[i];
CV_Assert(from.valid());
CV_DbgAssert(layers.count(from.lid) && (int)layers[from.lid].outputBlobs.size() > from.oid);
ld.inputBlobs[i] = &layers[from.lid].outputBlobs[from.oid];
ld.inputBlobsWrappers[i] = layers[from.lid].outputBlobsWrappers[from.oid];
ninputs = netInputLayer->inputsData.size();
ld.inputBlobsWrappers.resize(ninputs);
for (size_t i = 0; i < ninputs; i++)
{
ld.inputBlobsWrappers[i] = wrap(netInputLayer->inputsData[i]);
}
}
else
{
ld.inputBlobs.resize(ninputs);
ld.inputBlobsWrappers.resize(ninputs);
for (size_t i = 0; i < ninputs; i++)
{
LayerPin from = ld.inputBlobsId[i];
CV_Assert(from.valid());
CV_DbgAssert(layers.count(from.lid) && (int)layers[from.lid].outputBlobs.size() > from.oid);
ld.inputBlobs[i] = &layers[from.lid].outputBlobs[from.oid];
ld.inputBlobsWrappers[i] = layers[from.lid].outputBlobsWrappers[from.oid];
}
}
LayersShapesMap::const_iterator layerShapesIt = layersShapes.find(lid);
@ -1731,15 +1777,14 @@ struct Net::Impl
ShapesVec inputShapes;
for(int i = 0; i < layers[0].outputBlobs.size(); i++)
{
CV_Assert(layers[0].outputBlobs[i].total());
if (layers[0].outputBlobs[i].depth() == CV_32F &&
preferableBackend == DNN_BACKEND_OPENCV &&
Mat& inp = layers[0].outputBlobs[i];
CV_Assert(inp.total());
if (preferableBackend == DNN_BACKEND_OPENCV &&
preferableTarget == DNN_TARGET_OPENCL_FP16)
{
Mat mat = layers[0].outputBlobs[i].clone();
convertFp16(mat, layers[0].outputBlobs[i]);
layers[0].outputBlobs[i].create(inp.dims, inp.size, CV_16S);
}
inputShapes.push_back(shape(layers[0].outputBlobs[i]));
inputShapes.push_back(shape(inp));
}
LayersShapesMap layersShapes;
getLayersShapes(inputShapes, layersShapes);
@ -2271,28 +2316,22 @@ void Net::setInput(InputArray blob, const String& name)
CV_Error(Error::StsObjectNotFound, "Requested blob \"" + name + "\" not found");
LayerData &ld = impl->layers[pin.lid];
ld.outputBlobs.resize( std::max(pin.oid+1, (int)ld.requiredOutputs.size()) );
ld.outputBlobsWrappers.resize(ld.outputBlobs.size());
MatShape prevShape = shape(ld.outputBlobs[pin.oid]);
Mat blob_;
if (impl->preferableBackend == DNN_BACKEND_OPENCV &&
impl->preferableTarget == DNN_TARGET_OPENCL_FP16)
{
Mat blob_mat = blob.getMat();
convertFp16(blob_mat, blob_);
}
else
{
blob_ = blob.getMat();
}
const int numInputs = std::max(pin.oid+1, (int)ld.requiredOutputs.size());
ld.outputBlobs.resize(numInputs);
ld.outputBlobsWrappers.resize(numInputs);
impl->netInputLayer->inputsData.resize(numInputs);
MatShape prevShape = shape(impl->netInputLayer->inputsData[pin.oid]);
Mat blob_ = blob.getMat();
bool oldShape = prevShape == shape(blob_);
if (oldShape)
{
blob_.copyTo(ld.outputBlobs[pin.oid]);
blob_.copyTo(impl->netInputLayer->inputsData[pin.oid]);
}
else
{
ld.outputBlobs[pin.oid] = blob_.clone();
impl->netInputLayer->inputsData[pin.oid] = ld.outputBlobs[pin.oid];
}
if (!ld.outputBlobsWrappers[pin.oid].empty())