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opencv_contrib
Commit b0d008ce authored by Aleksandr Rybnikov's avatar Aleksandr Rybnikov
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Enabled tests for intermediate blobs in goolgenet

parent e551d15c
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Showing with 82 additions and 73 deletions
modules/dnn/src/dnn.cpp
View file @ b0d008ce
......@@ -324,7 +324,6 @@ struct LayerData
//add logging info
params.name = name;
params.type = type;
skip = false;
}
int id;
......@@ -347,7 +346,6 @@ struct LayerData
std::map<int, bool> skipFlags;
int flag;
bool skip;
Ptr<Layer> getLayerInstance()
{
......@@ -666,18 +664,39 @@ struct Net::Impl
}
}
void setUpNet(const std::vector<LayerPin>& blobsToKeep_ = std::vector<LayerPin>())
void clear()
{
if (!netWasAllocated || this->blobsToKeep != blobsToKeep_)
MapIdToLayerData::iterator it;
for (it = layers.begin(); it != layers.end(); it++)
{
MapIdToLayerData::iterator it;
for (it = layers.begin(); it != layers.end(); it++)
if (it->second.id != 0) {
it->second.outputBlobs.clear();
it->second.internals.clear();
}
it->second.skipFlags.clear();
it->second.consumers.clear();
Ptr<ConvolutionLayer> convLayer = it->second.layerInstance.dynamicCast<ConvolutionLayer>();
if( !convLayer.empty() )
{
if (it->second.id != 0) {
it->second.outputBlobs.clear();
it->second.internals.clear();
}
convLayer->setActivation(Ptr<ActivationLayer>());
convLayer->setBatchNorm(Ptr<BatchNormLayer>());
}
Ptr<PoolingLayer> poolingLayer = it->second.layerInstance.dynamicCast<PoolingLayer>();
if( !poolingLayer.empty() )
{
poolingLayer->computeMaxIdx = true;
}
}
}
void setUpNet(const std::vector<LayerPin>& blobsToKeep_ = std::vector<LayerPin>())
{
if (!netWasAllocated || this->blobsToKeep != blobsToKeep_)
{
clear();
allocateLayers(blobsToKeep_);
computeNetOutputLayers();
......@@ -1005,69 +1024,41 @@ struct Net::Impl
ld.flag = 1;
}
void allocateLayers(const std::vector<LayerPin>& blobsToKeep_)
void fuseLayers(const std::vector<LayerPin>& blobsToKeep_)
{
MapIdToLayerData::iterator it;
for (it = layers.begin(); it != layers.end(); it++)
it->second.flag = 0;
CV_Assert(!layers[0].outputBlobs.empty());
ShapesVec inputShapes;
for(int i = 0; i < layers[0].outputBlobs.size(); i++)
{
CV_Assert(layers[0].outputBlobs[i].total());
inputShapes.push_back(shape(layers[0].outputBlobs[i]));
}
LayersShapesMap layersShapes;
getLayersShapes(inputShapes, layersShapes);
blobManager.reset();
for (it = layers.begin(); it != layers.end(); ++it)
{
const LayerData& ld = it->second;
blobManager.addReferences(ld.inputBlobsId);
}
for (int i = 0; i < blobsToKeep_.size(); i++)
{
blobManager.addReference(blobsToKeep_[i]);
}
for (it = layers.begin(); it != layers.end(); it++)
{
int lid = it->first;
allocateLayer(lid, layersShapes);
}
// scan through all the layers. If there is convolution layer followed by the activation layer,
// we try to embed this activation into the convolution and disable separate execution of the activation
std::vector<String> outnames;
std::set<LayerPin> pinsToKeep(blobsToKeep_.begin(),
blobsToKeep_.end());
MapIdToLayerData::iterator it;
for (it = layers.begin(); it != layers.end(); it++)
{
int lid = it->first;
LayerData& ld = layers[lid];
if( ld.skip )
if( ld.skipFlags[DNN_BACKEND_DEFAULT] )
{
//printf("skipping %s\n", ld.layerInstance->name.c_str());
continue;
}
//printf("analyzing %s\n", ld.layerInstance->name.c_str());
if( ld.consumers.size() == 0 )
outnames.push_back(ld.layerInstance->name);
Ptr<ConvolutionLayer> convLayer = ld.layerInstance.dynamicCast<ConvolutionLayer>();
if( !convLayer.empty() && ld.consumers.size() == 1 )
LayerPin lp(lid, 0);
if( !convLayer.empty() && ld.consumers.size() == 1 &&
pinsToKeep.count(lp) == 0 )
{
LayerData* nextData = &layers[ld.consumers[0].lid];
Ptr<BatchNormLayer> nextBNormLayer =
nextData->layerInstance.dynamicCast<BatchNormLayer>();
if( !nextBNormLayer.empty() )
LayerPin lpNext(ld.consumers[0].lid, 0);
if( !nextBNormLayer.empty() && pinsToKeep.count(lpNext) == 0 )
{
LayerData* bnormData = nextData;
nextData = 0;
if( convLayer->setBatchNorm(nextBNormLayer) )
{
//printf("fused convolution (%s) and batch norm (%s)\n", convLayer->name.c_str(), nextBNormLayer->name.c_str());
bnormData->skip = true;
bnormData->skipFlags[DNN_BACKEND_DEFAULT] = true;
ld.outputBlobs = layers[lpNext.lid].outputBlobs;
if( bnormData->consumers.size() == 1 )
nextData = &layers[bnormData->consumers[0].lid];
}
......@@ -1079,8 +1070,8 @@ struct Net::Impl
if( !nextActivLayer.empty() && convLayer->setActivation(nextActivLayer) )
{
//printf("fused convolution (%s) and activation (%s)\n", convLayer->name.c_str(), nextActivLayer->name.c_str());
nextData->skip = true;
nextData->skipFlags[DNN_BACKEND_DEFAULT] = true;
ld.outputBlobs = layers[lpNext.lid].outputBlobs;
}
}
Ptr<PoolingLayer> poolingLayer = ld.layerInstance.dynamicCast<PoolingLayer>();
......@@ -1096,10 +1087,43 @@ struct Net::Impl
poolingLayer->computeMaxIdx = false;
}
}
/*printf("outputs: ");
for( size_t j = 0; j < outnames.size(); j++ )
printf("%s ", outnames[j].c_str());
printf("\n");*/
}
void allocateLayers(const std::vector<LayerPin>& blobsToKeep_)
{
MapIdToLayerData::iterator it;
for (it = layers.begin(); it != layers.end(); it++)
it->second.flag = 0;
CV_Assert(!layers[0].outputBlobs.empty());
ShapesVec inputShapes;
for(int i = 0; i < layers[0].outputBlobs.size(); i++)
{
CV_Assert(layers[0].outputBlobs[i].total());
inputShapes.push_back(shape(layers[0].outputBlobs[i]));
}
LayersShapesMap layersShapes;
getLayersShapes(inputShapes, layersShapes);
blobManager.reset();
for (it = layers.begin(); it != layers.end(); ++it)
{
const LayerData& ld = it->second;
blobManager.addReferences(ld.inputBlobsId);
}
for (int i = 0; i < blobsToKeep_.size(); i++)
{
blobManager.addReference(blobsToKeep_[i]);
}
for (it = layers.begin(); it != layers.end(); it++)
{
int lid = it->first;
allocateLayer(lid, layersShapes);
}
fuseLayers(blobsToKeep_);
}
void forwardLayer(LayerData &ld)
......@@ -1109,7 +1133,7 @@ struct Net::Impl
if (preferableBackend == DNN_BACKEND_DEFAULT ||
!layer->supportBackend(preferableBackend))
{
if( !ld.skip )
if( !ld.skipFlags[DNN_BACKEND_DEFAULT] )
layer->forward(ld.inputBlobs, ld.outputBlobs, ld.internals);
}
else if (!ld.skipFlags[preferableBackend])
......@@ -1300,20 +1324,6 @@ void Net::connect(String _outPin, String _inPin)
impl->connect(outPin.lid, outPin.oid, inpPin.lid, inpPin.oid);
}
//void Net::forward(LayerId toLayer)
//{
// if (!impl->netWasAllocated)
// {
// impl->setUpNet();
// }
// if (toLayer.isString() && toLayer.get<String>().empty())
// impl->forwardAll();
// else
// impl->forwardLayer(impl->getLayerData(toLayer));
//}
Mat Net::forward(const String& outputName)
{
String layerName = outputName;
......
modules/dnn/test/test_googlenet.cpp
View file @ b0d008ce
......@@ -95,8 +95,7 @@ static void launchGoogleNetTest()
std::replace( filename.begin(), filename.end(), '/', '#');
Mat ref = blobFromNPY(_tf("googlenet_" + filename + ".npy"));
// TODO: disabled the check for now, because it conflicts with the layer fusion
// normAssert(outs[i], ref, "", 1E-4, 1E-2);
normAssert(outs[i], ref, "", 1E-4, 1E-2);
}
}
......
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