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Merge pull request #9019 from alalek:dnn_trace

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This commit is contained in:
Maksim Shabunin 2017-06-29 07:33:46 +00:00
commit ace0701a46
36 changed files with 224 additions and 24 deletions
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6
modules/dnn/src/caffe/caffe_importer.cpp
View File

@ -43,7 +43,7 @@
using namespace cv;
using namespace cv::dnn;
#if HAVE_PROTOBUF
#ifdef HAVE_PROTOBUF
#include "caffe.pb.h"
#include <iostream>
@ -82,6 +82,8 @@ public:
CaffeImporter(const char *pototxt, const char *caffeModel)
{
CV_TRACE_FUNCTION();
ReadNetParamsFromTextFileOrDie(pototxt, &net);
if (caffeModel && caffeModel[0])
@ -264,6 +266,8 @@ public:
void populateNet(Net dstNet)
{
CV_TRACE_FUNCTION();
int layersSize = net.layer_size();
layerCounter.clear();
addedBlobs.clear();

2
modules/dnn/src/caffe/caffe_io.cpp
View File

@ -87,7 +87,7 @@
//
//M*/
#if HAVE_PROTOBUF
#ifdef HAVE_PROTOBUF
#include <google/protobuf/io/coded_stream.h>
#include <google/protobuf/io/zero_copy_stream_impl.h>
#include <google/protobuf/text_format.h>

2
modules/dnn/src/caffe/caffe_io.hpp
View File

@ -89,7 +89,7 @@
#ifndef __OPENCV_DNN_CAFFE_IO_HPP__
#define __OPENCV_DNN_CAFFE_IO_HPP__
#if HAVE_PROTOBUF
#ifdef HAVE_PROTOBUF
#include "caffe.pb.h"

118
modules/dnn/src/dnn.cpp
View File

@ -90,6 +90,7 @@ static String toString(const T &v)
Mat blobFromImage(const Mat& image, double scalefactor, const Size& size,
const Scalar& mean, bool swapRB)
{
CV_TRACE_FUNCTION();
std::vector<Mat> images(1, image);
return blobFromImages(images, scalefactor, size, mean, swapRB);
}
@ -97,6 +98,7 @@ Mat blobFromImage(const Mat& image, double scalefactor, const Size& size,
Mat blobFromImages(const std::vector<Mat>& images_, double scalefactor, Size size,
const Scalar& mean_, bool swapRB)
{
CV_TRACE_FUNCTION();
std::vector<Mat> images = images_;
for (int i = 0; i < images.size(); i++)
{
@ -207,6 +209,8 @@ class BackendWrapManager
public:
Ptr<BackendWrapper> wrap(const Mat& m, int backendId, int targetId)
{
CV_TRACE_FUNCTION();
CV_Assert(backendId != DNN_BACKEND_DEFAULT);
std::map<void*, Ptr<BackendWrapper> >::iterator hostsIt;
@ -261,6 +265,8 @@ public:
void reset()
{
CV_TRACE_FUNCTION();
hostWrappers.clear();
extraWrappers.clear();
}
@ -321,6 +327,8 @@ struct LayerData
LayerData(int _id, const String &_name, const String &_type, LayerParams &_params)
: id(_id), name(_name), type(_type), params(_params), flag(0)
{
CV_TRACE_FUNCTION();
//add logging info
params.name = name;
params.type = type;
@ -349,6 +357,9 @@ struct LayerData
Ptr<Layer> getLayerInstance()
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(type, "type", type.c_str());
if (layerInstance)
return layerInstance;
@ -500,6 +511,8 @@ public:
void allocateBlobsForLayer(LayerData &ld, const LayerShapes& layerShapes,
std::vector<LayerPin>& pinsForInternalBlobs)
{
CV_TRACE_FUNCTION();
pinsForInternalBlobs.clear();
std::vector<Mat>& outputBlobs = ld.outputBlobs,
@ -578,6 +591,8 @@ public:
// Clear internal state. Calls before an every reallocation.
void reset()
{
CV_TRACE_FUNCTION();
refCounter.clear();
reuseMap.clear();
memHosts.clear();
@ -639,6 +654,8 @@ struct Net::Impl
void compileHalide()
{
CV_TRACE_FUNCTION();
CV_Assert(preferableBackend == DNN_BACKEND_HALIDE);
HalideScheduler scheduler(halideConfigFile);
@ -666,6 +683,8 @@ struct Net::Impl
void clear()
{
CV_TRACE_FUNCTION();
MapIdToLayerData::iterator it;
for (it = layers.begin(); it != layers.end(); it++)
{
@ -694,6 +713,8 @@ struct Net::Impl
void setUpNet(const std::vector<LayerPin>& blobsToKeep_ = std::vector<LayerPin>())
{
CV_TRACE_FUNCTION();
if (!netWasAllocated || this->blobsToKeep != blobsToKeep_)
{
clear();
@ -862,6 +883,8 @@ struct Net::Impl
void computeNetOutputLayers()
{
CV_TRACE_FUNCTION();
netOutputs.clear();
MapIdToLayerData::iterator it;
@ -883,6 +906,8 @@ struct Net::Impl
void initBackend()
{
CV_TRACE_FUNCTION();
backendWrapper.reset();
if (preferableBackend == DNN_BACKEND_DEFAULT)
{
@ -953,6 +978,8 @@ struct Net::Impl
void allocateLayer(int lid, const LayersShapesMap& layersShapes)
{
CV_TRACE_FUNCTION();
LayerData &ld = layers[lid];
//already allocated
@ -1026,6 +1053,8 @@ struct Net::Impl
void fuseLayers(const std::vector<LayerPin>& blobsToKeep_)
{
CV_TRACE_FUNCTION();
// 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;
@ -1094,6 +1123,8 @@ struct Net::Impl
void allocateLayers(const std::vector<LayerPin>& blobsToKeep_)
{
CV_TRACE_FUNCTION();
MapIdToLayerData::iterator it;
for (it = layers.begin(); it != layers.end(); it++)
it->second.flag = 0;
@ -1131,6 +1162,8 @@ struct Net::Impl
void forwardLayer(LayerData &ld)
{
CV_TRACE_FUNCTION();
Ptr<Layer> layer = ld.layerInstance;
if (preferableBackend == DNN_BACKEND_DEFAULT ||
@ -1159,6 +1192,8 @@ struct Net::Impl
void forwardToLayer(LayerData &ld, bool clearFlags = true)
{
CV_TRACE_FUNCTION();
if (clearFlags)
{
MapIdToLayerData::iterator it;
@ -1186,6 +1221,8 @@ struct Net::Impl
void forwardAll()
{
CV_TRACE_FUNCTION();
forwardToLayer(layers.rbegin()->second, true);
}
@ -1247,6 +1284,8 @@ struct Net::Impl
Mat getBlob(const LayerPin& pin)
{
CV_TRACE_FUNCTION();
if (!pin.valid())
CV_Error(Error::StsObjectNotFound, "Requested blob not found");
@ -1285,6 +1324,8 @@ Net::~Net()
int Net::addLayer(const String &name, const String &type, LayerParams &params)
{
CV_TRACE_FUNCTION();
if (name.find('.') != String::npos)
{
CV_Error(Error::StsBadArg, "Added layer name \"" + name + "\" must not contain dot symbol");
@ -1306,6 +1347,8 @@ int Net::addLayer(const String &name, const String &type, LayerParams &params)
int Net::addLayerToPrev(const String &name, const String &type, LayerParams &params)
{
CV_TRACE_FUNCTION();
int prvLid = impl->lastLayerId;
int newLid = this->addLayer(name, type, params);
this->connect(prvLid, 0, newLid, 0);
@ -1314,11 +1357,15 @@ int Net::addLayerToPrev(const String &name, const String &type, LayerParams &par
void Net::connect(int outLayerId, int outNum, int inpLayerId, int inpNum)
{
CV_TRACE_FUNCTION();
impl->connect(outLayerId, outNum, inpLayerId, inpNum);
}
void Net::connect(String _outPin, String _inPin)
{
CV_TRACE_FUNCTION();
LayerPin outPin = impl->getPinByAlias(_outPin);
LayerPin inpPin = impl->getPinByAlias(_inPin);
@ -1329,6 +1376,8 @@ void Net::connect(String _outPin, String _inPin)
Mat Net::forward(const String& outputName)
{
CV_TRACE_FUNCTION();
String layerName = outputName;
if (layerName.empty())
@ -1342,6 +1391,8 @@ Mat Net::forward(const String& outputName)
void Net::forward(std::vector<Mat>& outputBlobs, const String& outputName)
{
CV_TRACE_FUNCTION();
impl->setUpNet();
String layerName = outputName;
@ -1359,6 +1410,8 @@ void Net::forward(std::vector<Mat>& outputBlobs, const String& outputName)
void Net::forward(std::vector<Mat>& outputBlobs,
const std::vector<String>& outBlobNames)
{
CV_TRACE_FUNCTION();
std::vector<LayerPin> pins;
for (int i = 0; i < outBlobNames.size(); i++)
{
@ -1381,6 +1434,8 @@ void Net::forward(std::vector<Mat>& outputBlobs,
void Net::forward(std::vector<std::vector<Mat> >& outputBlobs,
const std::vector<String>& outBlobNames)
{
CV_TRACE_FUNCTION();
std::vector<LayerPin> pins;
for (int i = 0; i < outBlobNames.size(); i++)
{
@ -1407,6 +1462,9 @@ void Net::forward(std::vector<std::vector<Mat> >& outputBlobs,
void Net::setPreferableBackend(int backendId)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG(backendId);
impl->netWasAllocated = impl->netWasAllocated &&
impl->preferableBackend == backendId;
impl->preferableBackend = backendId;
@ -1414,6 +1472,9 @@ void Net::setPreferableBackend(int backendId)
void Net::setPreferableTarget(int targetId)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG(targetId);
impl->netWasAllocated = impl->netWasAllocated &&
impl->preferableTarget == targetId;
impl->preferableTarget = targetId;
@ -1421,11 +1482,16 @@ void Net::setPreferableTarget(int targetId)
void Net::setInputsNames(const std::vector<String> &inputBlobNames)
{
CV_TRACE_FUNCTION();
impl->netInputLayer->setNames(inputBlobNames);
}
void Net::setInput(const Mat &blob_, const String& name)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
LayerPin pin;
pin.lid = 0;
pin.oid = impl->resolvePinOutputName(impl->getLayerData(pin.lid), name);
@ -1595,6 +1661,8 @@ void Net::getLayerShapes(const ShapesVec& netInputShapes,
int64 Net::getFLOPS(const std::vector<MatShape>& netInputShapes) const
{
CV_TRACE_FUNCTION();
int64 flops = 0;
std::vector<int> ids;
std::vector<std::vector<MatShape> > inShapes, outShapes;
@ -1670,6 +1738,8 @@ void Net::getMemoryConsumption(const int layerId,
const std::vector<MatShape>& netInputShapes,
size_t& weights, size_t& blobs) const
{
CV_TRACE_FUNCTION();
Impl::MapIdToLayerData::iterator layer = impl->layers.find(layerId);
CV_Assert(layer != impl->layers.end());
@ -1692,6 +1762,8 @@ void Net::getMemoryConsumption(const int layerId,
void Net::getMemoryConsumption(const std::vector<MatShape>& netInputShapes,
size_t& weights, size_t& blobs) const
{
CV_TRACE_FUNCTION();
std::vector<int> layerIds;
std::vector<size_t> w, b;
getMemoryConsumption(netInputShapes, layerIds, w, b);
@ -1723,6 +1795,8 @@ void Net::getMemoryConsumption(const std::vector<MatShape>& netInputShapes,
std::vector<int>& layerIds, std::vector<size_t>& weights,
std::vector<size_t>& blobs) const
{
CV_TRACE_FUNCTION();
layerIds.clear();
weights.clear();
blobs.clear();
@ -1762,6 +1836,9 @@ void Net::getMemoryConsumption(const MatShape& netInputShape, std::vector<int>&
void Net::setHalideScheduler(const String& scheduler)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(scheduler, "scheduler", scheduler.c_str());
impl->halideConfigFile = scheduler;
}
@ -1810,6 +1887,8 @@ void Layer::applyHalideScheduler(Ptr<BackendNode>& node, const std::vector<Mat*>
const std::vector<Mat> &outputs, int targetId) const
{
#ifdef HAVE_HALIDE
CV_TRACE_FUNCTION();
Halide::Var x("x"), y("y"), c("c"), n("n"), co("co"), ci("ci"),
xo("xo"), xi("xi"), yo("yo"), yi("yi"), tile("tile");
Halide::Func& top = node.dynamicCast<HalideBackendNode>()->funcs.back();
@ -1891,6 +1970,8 @@ static void vecToPVec(const std::vector<T> &v, std::vector<T*> &pv)
void Layer::finalize(const std::vector<Mat> &inputs, std::vector<Mat> &outputs)
{
CV_TRACE_FUNCTION();
std::vector<Mat*> inputsp;
vecToPVec(inputs, inputsp);
this->finalize(inputsp, outputs);
@ -1903,6 +1984,8 @@ void Layer::finalize(const std::vector<Mat*> &input, std::vector<Mat> &output)
std::vector<Mat> Layer::finalize(const std::vector<Mat> &inputs)
{
CV_TRACE_FUNCTION();
std::vector<Mat> outputs;
this->finalize(inputs, outputs);
return outputs;
@ -1910,6 +1993,8 @@ std::vector<Mat> Layer::finalize(const std::vector<Mat> &inputs)
void Layer::forward(const std::vector<Mat> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals)
{
CV_TRACE_FUNCTION();
std::vector<Mat*> inputsp;
vecToPVec(inputs, inputsp);
this->forward(inputsp, outputs, internals);
@ -1917,6 +2002,8 @@ void Layer::forward(const std::vector<Mat> &inputs, std::vector<Mat> &outputs, s
void Layer::run(const std::vector<Mat> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals)
{
CV_TRACE_FUNCTION();
std::vector<Mat*> inputsp;
vecToPVec(inputs, inputsp);
this->finalize(inputsp, outputs);
@ -1972,32 +2059,41 @@ static LayerFactory_Impl& getLayerFactoryImpl()
return *instance;
}
void LayerFactory::registerLayer(const String &_type, Constuctor constructor)
void LayerFactory::registerLayer(const String &type, Constuctor constructor)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(type, "type", type.c_str());
cv::AutoLock lock(getLayerFactoryMutex());
String type = _type.toLowerCase();
LayerFactory_Impl::const_iterator it = getLayerFactoryImpl().find(type);
String type_ = type.toLowerCase();
LayerFactory_Impl::const_iterator it = getLayerFactoryImpl().find(type_);
if (it != getLayerFactoryImpl().end() && it->second != constructor)
{
CV_Error(cv::Error::StsBadArg, "Layer \"" + type + "\" already was registered");
CV_Error(cv::Error::StsBadArg, "Layer \"" + type_ + "\" already was registered");
}
getLayerFactoryImpl().insert(std::make_pair(type, constructor));
getLayerFactoryImpl().insert(std::make_pair(type_, constructor));
}
void LayerFactory::unregisterLayer(const String &_type)
void LayerFactory::unregisterLayer(const String &type)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(type, "type", type.c_str());
cv::AutoLock lock(getLayerFactoryMutex());
String type = _type.toLowerCase();
getLayerFactoryImpl().erase(type);
String type_ = type.toLowerCase();
getLayerFactoryImpl().erase(type_);
}
Ptr<Layer> LayerFactory::createLayerInstance(const String &_type, LayerParams& params)
Ptr<Layer> LayerFactory::createLayerInstance(const String &type, LayerParams& params)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(type, "type", type.c_str());
cv::AutoLock lock(getLayerFactoryMutex());
String type = _type.toLowerCase();
LayerFactory_Impl::const_iterator it = getLayerFactoryImpl().find(type);
String type_ = type.toLowerCase();
LayerFactory_Impl::const_iterator it = getLayerFactoryImpl().find(type_);
if (it != getLayerFactoryImpl().end())
{

2
modules/dnn/src/init.cpp
View File

@ -60,6 +60,8 @@ Mutex* __initialization_mutex_initializer = &getInitializationMutex();
void initializeLayerFactory()
{
CV_TRACE_FUNCTION();
CV_DNN_REGISTER_LAYER_CLASS(Slice, SliceLayer);
CV_DNN_REGISTER_LAYER_CLASS(Split, SplitLayer);
CV_DNN_REGISTER_LAYER_CLASS(Concat, ConcatLayer);

3
modules/dnn/src/layers/batch_norm_layer.cpp
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@ -104,6 +104,9 @@ public:
void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
CV_Assert(blobs.size() >= 2);
CV_Assert(inputs.size() == 1);

3
modules/dnn/src/layers/blank_layer.cpp
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@ -64,6 +64,9 @@ public:
void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
for (int i = 0, n = outputs.size(); i < n; ++i)
if (outputs[i].data != inputs[i]->data)
inputs[i]->copyTo(outputs[i]);

3
modules/dnn/src/layers/concat_layer.cpp
View File

@ -96,6 +96,9 @@ public:
void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
int cAxis = clamp(axis, inputs[0]->dims);
Mat& outMat = outputs[0];
std::vector<Range> ranges(outputs[0].dims, Range::all());

6
modules/dnn/src/layers/convolution_layer.cpp
View File

@ -627,6 +627,9 @@ public:
void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
/*printf("conv %s: input (%d x %d x %d x %d), kernel (%d x %d), pad (%d x %d), stride (%d x %d), dilation (%d x %d)\n",
name.c_str(), inputs[0]->size[0], inputs[0]->size[1], inputs[0]->size[2], inputs[0]->size[3],
kernel.width, kernel.height, pad.width, pad.height,
@ -1013,6 +1016,9 @@ public:
void forward(std::vector<Mat *> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
int outCn = blobs[0].size[0];
int inpCn = inputs[0]->size[1];
bool is1x1flag = is1x1();

3
modules/dnn/src/layers/crop_layer.cpp
View File

@ -135,6 +135,9 @@ public:
void forward(std::vector<Mat *> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
Mat &input = *inputs[0];
Mat &output = outputs[0];

3
modules/dnn/src/layers/detection_output_layer.cpp
View File

@ -206,6 +206,9 @@ public:
void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
const float* locationData = inputs[0]->ptr<float>();
const float* confidenceData = inputs[1]->ptr<float>();
const float* priorData = inputs[2]->ptr<float>();

2
modules/dnn/src/layers/elementwise_layers.cpp
View File

@ -156,6 +156,8 @@ public:
void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals)
{
CV_TRACE_FUNCTION();
for (size_t i = 0; i < inputs.size(); i++)
{
const Mat &src = *inputs[i];

3
modules/dnn/src/layers/eltwise_layer.cpp
View File

@ -251,6 +251,9 @@ public:
void forward(std::vector<Mat *> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
CV_Assert(outputs.size() == 1);
const int nstripes = getNumThreads();
EltwiseInvoker::run((const Mat**)&inputs[0], (int)inputs.size(), outputs[0],

3
modules/dnn/src/layers/flatten_layer.cpp
View File

@ -106,6 +106,9 @@ public:
void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
for (size_t i = 0; i < inputs.size(); i++)
{
MatShape outShape = shape(outputs[i]);

3
modules/dnn/src/layers/fully_connected_layer.cpp
View File

@ -233,6 +233,9 @@ public:
void forward(std::vector<Mat*> &input, std::vector<Mat> &output, std::vector<Mat> &)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
int axisCan = clamp(axis, input[0]->dims);
int outerSize = input[0]->total(0, axisCan);

3
modules/dnn/src/layers/lrn_layer.cpp
View File

@ -86,6 +86,9 @@ public:
void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
CV_Assert(inputs.size() == outputs.size());
for (int i = 0; i < inputs.size(); i++)
{

3
modules/dnn/src/layers/max_unpooling_layer.cpp
View File

@ -57,6 +57,9 @@ public:
void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
CV_Assert(inputs.size() == 2);
Mat& input = *inputs[0];
Mat& indices = *inputs[1];

3
modules/dnn/src/layers/mvn_layer.cpp
View File

@ -62,6 +62,9 @@ public:
void forward(std::vector<Mat *> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
for (size_t inpIdx = 0; inpIdx < inputs.size(); inpIdx++)
{
Mat &inpBlob = *inputs[inpIdx];

3
modules/dnn/src/layers/normalize_bbox_layer.cpp
View File

@ -142,6 +142,9 @@ public:
void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
checkInputs(inputs);
Mat& buffer = internals[0], sumChannelMultiplier = internals[1],

3
modules/dnn/src/layers/padding_layer.cpp
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@ -61,6 +61,9 @@ public:
void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
for(int i = 0; i < inputs.size(); i++)
{
outputs[i] = paddingValue;

3
modules/dnn/src/layers/permute_layer.cpp
View File

@ -245,6 +245,9 @@ public:
void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
size_t k, ninputs = inputs.size();
if(!_needsPermute)
{

3
modules/dnn/src/layers/pooling_layer.cpp
View File

@ -106,6 +106,9 @@ public:
void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
for (size_t ii = 0; ii < inputs.size(); ii++)
{
switch (type)

3
modules/dnn/src/layers/prior_box_layer.cpp
View File

@ -228,6 +228,9 @@ public:
void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
int _layerWidth = inputs[0]->size[3];
int _layerHeight = inputs[0]->size[2];

6
modules/dnn/src/layers/recurrent_layers.cpp
View File

@ -221,6 +221,9 @@ public:
void forward(std::vector<Mat*> &input, std::vector<Mat> &output, std::vector<Mat> &internals)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
const Mat &Wh = blobs[0];
const Mat &Wx = blobs[1];
const Mat &bias = blobs[2];
@ -406,6 +409,9 @@ public:
void forward(std::vector<Mat*> &input, std::vector<Mat> &output, std::vector<Mat> &internals)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
Mat xTs = input[0]->reshape(1, numSamplesTotal);
Mat oTs = output[0].reshape(1, numSamplesTotal);
Mat hTs = produceH ? output[1].reshape(1, numSamplesTotal) : Mat();

3
modules/dnn/src/layers/reshape_layer.cpp
View File

@ -196,6 +196,9 @@ public:
void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
for (size_t i = 0; i < inputs.size(); i++)
{
Mat srcBlob = *inputs[i];

3
modules/dnn/src/layers/scale_layer.cpp
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@ -45,6 +45,9 @@ public:
void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
CV_Assert(blobs.size() == 1 + hasBias);
for (size_t ii = 0; ii < outputs.size(); ii++)

3
modules/dnn/src/layers/shift_layer.cpp
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@ -38,6 +38,9 @@ public:
virtual void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
CV_Assert(inputs.size() > 0);
CV_Assert(blobs.size() > 0);

3
modules/dnn/src/layers/slice_layer.cpp
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@ -118,6 +118,9 @@ public:
void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
const Mat& inpMat = *inputs[0];
std::vector<Range> ranges(inpMat.dims, Range::all());
int cAxis = clamp(axis, inpMat.dims);

3
modules/dnn/src/layers/softmax_layer.cpp
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@ -84,6 +84,9 @@ public:
void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
const Mat &src = *inputs[0];
Mat &dst = outputs[0];

3
modules/dnn/src/layers/split_layer.cpp
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@ -80,6 +80,9 @@ public:
void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
for (size_t i = 0; i < outputs.size(); i++)
{
CV_Assert(inputs[0]->total() == outputs[i].total());

1
modules/dnn/src/precomp.hpp
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@ -40,6 +40,7 @@
//M*/
#include <opencv2/core.hpp>
#include <opencv2/core/utils/trace.hpp>
#include "cvconfig.h"
#include <opencv2/dnn.hpp>
#include <opencv2/dnn/all_layers.hpp>

2
modules/dnn/src/tensorflow/tf_importer.cpp
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@ -13,7 +13,7 @@ Implementation of Tensorflow models parser
using namespace cv;
using namespace cv::dnn;
#if HAVE_PROTOBUF
#ifdef HAVE_PROTOBUF
#include "graph.pb.h"
#include <iostream>

2
modules/dnn/src/tensorflow/tf_io.cpp
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@ -9,7 +9,7 @@
Implementation of various functions which are related to Tensorflow models reading.
*/
#if HAVE_PROTOBUF
#ifdef HAVE_PROTOBUF
#include <google/protobuf/io/coded_stream.h>
#include <google/protobuf/io/zero_copy_stream_impl.h>
#include <google/protobuf/text_format.h>

2
modules/dnn/src/tensorflow/tf_io.hpp
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@ -11,7 +11,7 @@ Declaration of various functions which are related to Tensorflow models reading.
#ifndef __OPENCV_DNN_TF_IO_HPP__
#define __OPENCV_DNN_TF_IO_HPP__
#if HAVE_PROTOBUF
#ifdef HAVE_PROTOBUF
#include "graph.pb.h"

6
modules/dnn/src/torch/torch_importer.cpp
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@ -115,6 +115,8 @@ struct TorchImporter : public ::cv::dnn::Importer
TorchImporter(String filename, bool isBinary)
{
CV_TRACE_FUNCTION();
rootModule = curModule = NULL;
moduleCounter = 0;
@ -966,6 +968,8 @@ struct TorchImporter : public ::cv::dnn::Importer
void populateNet(Net net_)
{
CV_TRACE_FUNCTION();
if (rootModule == NULL)
{
rootModule = new Module("Sequential");
@ -1014,6 +1018,8 @@ Mat readTorchBlob(const String&, bool)
Net readNetFromTorch(const String &model, bool isBinary)
{
CV_TRACE_FUNCTION();
Ptr<Importer> importer = createTorchImporter(model, isBinary);
Net net;
if (importer)

15
samples/dnn/caffe_googlenet.cpp
View File

@ -41,6 +41,7 @@
#include <opencv2/dnn.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/core/utils/trace.hpp>
using namespace cv;
using namespace cv::dnn;
@ -84,6 +85,8 @@ static std::vector<String> readClassNames(const char *filename = "synset_words.t
int main(int argc, char **argv)
{
CV_TRACE_FUNCTION();
String modelTxt = "bvlc_googlenet.prototxt";
String modelBin = "bvlc_googlenet.caffemodel";
String imageFile = (argc > 1) ? argv[1] : "space_shuttle.jpg";
@ -117,13 +120,20 @@ int main(int argc, char **argv)
Scalar(104, 117, 123)); //Convert Mat to batch of images
//! [Prepare blob]
Mat prob;
cv::TickMeter t;
for (int i = 0; i < 10; i++)
{
CV_TRACE_REGION("forward");
//! [Set input blob]
net.setInput(inputBlob, "data"); //set the network input
//! [Set input blob]
t.start();
//! [Make forward pass]
Mat prob = net.forward("prob"); //compute output
prob = net.forward("prob"); //compute output
//! [Make forward pass]
t.stop();
}
//! [Gather output]
int classId;
@ -136,6 +146,7 @@ int main(int argc, char **argv)
std::cout << "Best class: #" << classId << " '" << classNames.at(classId) << "'" << std::endl;
std::cout << "Probability: " << classProb * 100 << "%" << std::endl;
//! [Print results]
std::cout << "Time: " << (double)t.getTimeMilli() / t.getCounter() << " ms (average from " << t.getCounter() << " iterations)" << std::endl;
return 0;
} //main