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opencv/modules/dnn/src/dnn.cpp

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
#include "op_halide.hpp"
#include "op_inf_engine.hpp"
#include "halide_scheduler.hpp"
#include <set>
#include <algorithm>
#include <iostream>
#include <sstream>
#include <iterator>
#include <numeric>
#include <opencv2/dnn/shape_utils.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/core/utils/configuration.private.hpp>
#include <opencv2/core/utils/logger.hpp>
namespace cv {
namespace dnn {
CV__DNN_INLINE_NS_BEGIN
// this option is useful to run valgrind memory errors detection
static bool DNN_DISABLE_MEMORY_OPTIMIZATIONS = utils::getConfigurationParameterBool("OPENCV_DNN_DISABLE_MEMORY_OPTIMIZATIONS", false);
#ifdef HAVE_OPENCL
static bool DNN_OPENCL_ALLOW_ALL_DEVICES = utils::getConfigurationParameterBool("OPENCV_DNN_OPENCL_ALLOW_ALL_DEVICES", false);
#endif
static int PARAM_DNN_BACKEND_DEFAULT = (int)utils::getConfigurationParameterSizeT("OPENCV_DNN_BACKEND_DEFAULT",
#ifdef HAVE_INF_ENGINE
(size_t)DNN_BACKEND_INFERENCE_ENGINE
#else
(size_t)DNN_BACKEND_OPENCV
#endif
);
// Additional checks (slowdowns execution!)
static bool DNN_CHECK_NAN_INF = utils::getConfigurationParameterBool("OPENCV_DNN_CHECK_NAN_INF", false);
static bool DNN_CHECK_NAN_INF_DUMP = utils::getConfigurationParameterBool("OPENCV_DNN_CHECK_NAN_INF_DUMP", false);
static bool DNN_CHECK_NAN_INF_RAISE_ERROR = utils::getConfigurationParameterBool("OPENCV_DNN_CHECK_NAN_INF_RAISE_ERROR", false);
using std::vector;
using std::map;
using std::make_pair;
using std::set;
namespace
{
typedef std::vector<MatShape> ShapesVec;
struct LayerShapes
{
ShapesVec in, out, internal;
// No guarantees that layer which support in-place computations
// will be computed in-place (input.data_ptr == output.data_ptr).
// If layer said that it could work in-place and layers after it
// no longer use input blob, we'll set output = input.
bool supportInPlace;
LayerShapes() {supportInPlace = false;}
};
}
Mat blobFromImage(InputArray image, double scalefactor, const Size& size,
const Scalar& mean, bool swapRB, bool crop, int ddepth)
{
CV_TRACE_FUNCTION();
Mat blob;
blobFromImage(image, blob, scalefactor, size, mean, swapRB, crop, ddepth);
return blob;
}
void blobFromImage(InputArray image, OutputArray blob, double scalefactor,
const Size& size, const Scalar& mean, bool swapRB, bool crop, int ddepth)
{
CV_TRACE_FUNCTION();
std::vector<Mat> images(1, image.getMat());
blobFromImages(images, blob, scalefactor, size, mean, swapRB, crop, ddepth);
}
Mat blobFromImages(InputArrayOfArrays images, double scalefactor, Size size,
const Scalar& mean, bool swapRB, bool crop, int ddepth)
{
CV_TRACE_FUNCTION();
Mat blob;
blobFromImages(images, blob, scalefactor, size, mean, swapRB, crop, ddepth);
return blob;
}
void blobFromImages(InputArrayOfArrays images_, OutputArray blob_, double scalefactor,
Size size, const Scalar& mean_, bool swapRB, bool crop, int ddepth)
{
CV_TRACE_FUNCTION();
CV_CheckType(ddepth, ddepth == CV_32F || ddepth == CV_8U, "Blob depth should be CV_32F or CV_8U");
if (ddepth == CV_8U)
{
CV_CheckEQ(scalefactor, 1.0, "Scaling is not supported for CV_8U blob depth");
CV_Assert(mean_ == Scalar() && "Mean subtraction is not supported for CV_8U blob depth");
}
std::vector<Mat> images;
images_.getMatVector(images);
CV_Assert(!images.empty());
for (int i = 0; i < images.size(); i++)
{
Size imgSize = images[i].size();
if (size == Size())
size = imgSize;
if (size != imgSize)
{
if(crop)
{
float resizeFactor = std::max(size.width / (float)imgSize.width,
size.height / (float)imgSize.height);
resize(images[i], images[i], Size(), resizeFactor, resizeFactor, INTER_LINEAR);
Rect crop(Point(0.5 * (images[i].cols - size.width),
0.5 * (images[i].rows - size.height)),
size);
images[i] = images[i](crop);
}
else
resize(images[i], images[i], size, 0, 0, INTER_LINEAR);
}
if(images[i].depth() == CV_8U && ddepth == CV_32F)
images[i].convertTo(images[i], CV_32F);
Scalar mean = mean_;
if (swapRB)
std::swap(mean[0], mean[2]);
images[i] -= mean;
images[i] *= scalefactor;
}
size_t i, nimages = images.size();
Mat image0 = images[0];
int nch = image0.channels();
CV_Assert(image0.dims == 2);
Mat image;
if (nch == 3 || nch == 4)
{
int sz[] = { (int)nimages, nch, image0.rows, image0.cols };
blob_.create(4, sz, ddepth);
Mat blob = blob_.getMat();
Mat ch[4];
for( i = 0; i < nimages; i++ )
{
image = images[i];
CV_Assert(image.depth() == blob_.depth());
nch = image.channels();
CV_Assert(image.dims == 2 && (nch == 3 || nch == 4));
CV_Assert(image.size() == image0.size());
for( int j = 0; j < nch; j++ )
ch[j] = Mat(image.rows, image.cols, ddepth, blob.ptr((int)i, j));
if(swapRB)
std::swap(ch[0], ch[2]);
split(image, ch);
}
}
else
{
CV_Assert(nch == 1);
int sz[] = { (int)nimages, 1, image0.rows, image0.cols };
blob_.create(4, sz, ddepth);
Mat blob = blob_.getMat();
for( i = 0; i < nimages; i++ )
{
Mat image = images[i];
CV_Assert(image.depth() == blob_.depth());
nch = image.channels();
CV_Assert(image.dims == 2 && (nch == 1));
CV_Assert(image.size() == image0.size());
image.copyTo(Mat(image.rows, image.cols, ddepth, blob.ptr((int)i, 0)));
}
}
}
void imagesFromBlob(const cv::Mat& blob_, OutputArrayOfArrays images_)
{
CV_TRACE_FUNCTION();
//A blob is a 4 dimensional matrix in floating point precision
//blob_[0] = batchSize = nbOfImages
//blob_[1] = nbOfChannels
//blob_[2] = height
//blob_[3] = width
CV_Assert(blob_.depth() == CV_32F);
CV_Assert(blob_.dims == 4);
images_.create(cv::Size(1, blob_.size[0]), blob_.depth());
std::vector<Mat> vectorOfChannels(blob_.size[1]);
for (int n = 0; n < blob_.size[0]; ++n)
{
for (int c = 0; c < blob_.size[1]; ++c)
{
vectorOfChannels[c] = getPlane(blob_, n, c);
}
cv::merge(vectorOfChannels, images_.getMatRef(n));
}
}
class OpenCLBackendWrapper : public BackendWrapper
{
public:
OpenCLBackendWrapper(Mat& m) : BackendWrapper(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL)
{
m.copyTo(umat);
host = &m;
hostDirty = false;
}
OpenCLBackendWrapper(const Ptr<BackendWrapper>& baseBuffer, Mat& m)
: BackendWrapper(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL)
{
Ptr<OpenCLBackendWrapper> base = baseBuffer.dynamicCast<OpenCLBackendWrapper>();
CV_Assert(!base.empty());
host = &m;
int shape[] = {1, (int)base->umat.total()};
umat = base->umat.reshape(1, 2, &shape[0])
.colRange(0, host->total())
.reshape(1, host->dims, &host->size[0]);
hostDirty = false;
}
static Ptr<BackendWrapper> create(Mat& m)
{
return Ptr<BackendWrapper>(new OpenCLBackendWrapper(m));
}
static Ptr<BackendWrapper> create(const Ptr<BackendWrapper>& baseBuffer, Mat& m)
{
return Ptr<BackendWrapper>(new OpenCLBackendWrapper(baseBuffer, m));
}
static std::vector<UMat> getUMatVector(const std::vector<Ptr<BackendWrapper> >& wrappers)
{
const int numWrappers = wrappers.size();
std::vector<UMat> mats(wrappers.size());
for (int i = 0; i < numWrappers; ++i)
{
Ptr<OpenCLBackendWrapper> umatWrapper = wrappers[i].dynamicCast<OpenCLBackendWrapper>();
CV_Assert(!umatWrapper.empty());
umatWrapper->copyToDevice();
mats[i] = umatWrapper->umat;
}
return mats;
}
// Replaces all umats in wrappers to specific ones.
static void update(const std::vector<Ptr<BackendWrapper> >& wrappers,
const std::vector<UMat>& umats)
{
CV_Assert(wrappers.size() == umats.size());
for (int i = 0, n = umats.size(); i < n; ++i)
{
Ptr<OpenCLBackendWrapper> umatWrapper = wrappers[i].dynamicCast<OpenCLBackendWrapper>();
CV_Assert(!umatWrapper.empty());
umatWrapper->umat = umats[i];
}
}
~OpenCLBackendWrapper() {}
// Copies data from device to a host memory.
virtual void copyToHost() CV_OVERRIDE
{
umat.copyTo(*host);
}
virtual void setHostDirty() CV_OVERRIDE
{
hostDirty = true;
};
void copyToDevice()
{
if (hostDirty)
{
host->copyTo(umat);
hostDirty = false;
}
}
private:
UMat umat;
Mat* host;
bool hostDirty;
};
struct LayerPin
{
int lid;
int oid;
LayerPin(int layerId = -1, int outputId = -1)
: lid(layerId), oid(outputId) {}
bool valid() const
{
return (lid >= 0 && oid >= 0);
}
bool equal(const LayerPin &r) const
{
return (lid == r.lid && oid == r.oid);
}
bool operator<(const LayerPin &r) const
{
return lid < r.lid || lid == r.lid && oid < r.oid;
}
bool operator ==(const LayerPin &r) const
{
return lid == r.lid && oid == r.oid;
}
};
struct LayerData
{
LayerData() : id(-1), skip(false), flag(0) {}
LayerData(int _id, const String &_name, const String &_type, LayerParams &_params)
: id(_id), name(_name), type(_type), params(_params), skip(false), flag(0)
{
CV_TRACE_FUNCTION();
//add logging info
params.name = name;
params.type = type;
}
int id;
String name;
String type;
LayerParams params;
std::vector<LayerPin> inputBlobsId;
std::set<int> inputLayersId;
std::set<int> requiredOutputs;
std::vector<LayerPin> consumers;
std::vector<Ptr<BackendWrapper> > outputBlobsWrappers;
std::vector<Ptr<BackendWrapper> > inputBlobsWrappers;
std::vector<Ptr<BackendWrapper> > internalBlobsWrappers;
Ptr<Layer> layerInstance;
std::vector<Mat> outputBlobs;
std::vector<Mat*> inputBlobs;
std::vector<Mat> internals;
// Computation nodes of implemented backends (except DEFAULT).
std::map<int, Ptr<BackendNode> > backendNodes;
// Flag for skip layer computation for specific backend.
bool skip;
int flag;
Ptr<Layer> getLayerInstance()
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(type, "type", type.c_str());
if (layerInstance)
return layerInstance;
layerInstance = LayerFactory::createLayerInstance(type, params);
if (!layerInstance)
{
CV_Error(Error::StsError, "Can't create layer \"" + name + "\" of type \"" + type + "\"");
}
return layerInstance;
}
};
//fake layer containing network input blobs
struct DataLayer : public Layer
{
DataLayer() : Layer()
{
skip = false;
}
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && inputsData.size() == 1;
}
void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) 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_arr, outputs_arr, internals_arr))
if (outputs_arr.depth() == CV_16S)
{
forward_fallback(inputs_arr, outputs_arr, internals_arr);
return;
}
std::vector<Mat> outputs, internals;
outputs_arr.getMatVector(outputs);
internals_arr.getMatVector(internals);
// Supported modes:
// | Input type | Output type |
// | fp32 | fp32 |
// | uint8 | fp32 |
for (int i = 0; i < inputsData.size(); ++i)
{
double scale = scaleFactors[i];
Scalar& mean = means[i];
CV_Assert(mean == Scalar() || inputsData[i].size[1] <= 4);
CV_CheckTypeEQ(outputs[i].type(), CV_32FC1, "");
bool singleMean = true;
for (int j = 1; j < std::min(4, inputsData[i].size[1]) && singleMean; ++j)
{
singleMean = mean[j] == mean[j - 1];
}
if (singleMean)
{
inputsData[i].convertTo(outputs[i], CV_32F, scale, -mean[0] * scale);
}
else
{
for (int n = 0; n < inputsData[i].size[0]; ++n)
for (int c = 0; c < inputsData[i].size[1]; ++c)
{
Mat inp = getPlane(inputsData[i], n, c);
Mat out = getPlane(outputs[i], n, c);
inp.convertTo(out, CV_32F, scale, -mean[c] * scale);
}
}
}
}
#ifdef HAVE_OPENCL
std::vector<Mat> tmp_expressions;
bool forward_ocl(InputArrayOfArrays, OutputArrayOfArrays outputs_, OutputArrayOfArrays internals_)
{
// Supported modes:
// | Input type | Output type |
// | fp32 | fp32 |
// | fp32 | fp16 |
// | uint8 | fp32 |
std::vector<UMat> outputs;
outputs_.getUMatVector(outputs);
tmp_expressions.clear();
for (int i = 0; i < inputsData.size(); ++i)
{
Mat inputData = inputsData[i];
double scale = scaleFactors[i];
Scalar& mean = means[i];
CV_Assert(mean == Scalar() || inputsData[i].size[1] <= 4);
bool singleMean = true;
for (int j = 1; j < std::min(4, inputsData[i].size[1]) && singleMean; ++j)
{
singleMean = mean[j] == mean[j - 1];
}
if (outputs_.depth() == CV_16S)
{
if (singleMean)
{
tmp_expressions.push_back(Mat(scale * (inputsData[i] - mean[0])));
convertFp16(tmp_expressions.back(), outputs[i]);
}
else
{
for (int n = 0; n < inputsData[i].size[0]; ++n)
for (int c = 0; c < inputsData[i].size[1]; ++c)
{
Mat inp = getPlane(inputsData[i], n, c);
std::vector<cv::Range> plane(4, Range::all());
plane[0] = Range(n, n + 1);
plane[1] = Range(c, c + 1);
UMat out = outputs[i](plane).reshape(1, inp.dims, inp.size);
tmp_expressions.push_back(scale * (inp - mean[c]));
convertFp16(tmp_expressions.back(), out);
}
}
}
else
{
CV_Assert(outputs_.depth() == CV_32F);
if (singleMean)
{
inputsData[i].convertTo(outputs[i], CV_32F, scale, -mean[0] * scale);
}
else
{
for (int n = 0; n < inputsData[i].size[0]; ++n)
for (int c = 0; c < inputsData[i].size[1]; ++c)
{
Mat inp = getPlane(inputsData[i], n, c);
std::vector<cv::Range> plane(4, Range::all());
plane[0] = Range(n, n + 1);
plane[1] = Range(c, c + 1);
UMat out = outputs[i](plane).reshape(1, inp.dims, inp.size);
inp.convertTo(out, CV_32F, scale, -mean[c] * scale);
}
}
}
}
return true;
}
#endif
int outputNameToIndex(const String& tgtName) CV_OVERRIDE
{
int idx = (int)(std::find(outNames.begin(), outNames.end(), tgtName) - outNames.begin());
return (idx < (int)outNames.size()) ? idx : -1;
}
void setNames(const std::vector<String> &names)
{
outNames.assign(names.begin(), names.end());
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
const int requiredOutputs,
std::vector<MatShape> &outputs,
std::vector<MatShape> &internals) const CV_OVERRIDE
{
CV_Assert(inputs.size() == requiredOutputs);
outputs.assign(inputs.begin(), inputs.end());
return false;
}
virtual void finalize(InputArrayOfArrays, OutputArrayOfArrays outputs_arr) CV_OVERRIDE
{
std::vector<Mat> outputs;
outputs_arr.getMatVector(outputs);
CV_Assert_N(outputs.size() == scaleFactors.size(), outputs.size() == means.size(),
inputsData.size() == outputs.size());
skip = true;
for (int i = 0; skip && i < inputsData.size(); ++i)
{
if (inputsData[i].data != outputs[i].data || scaleFactors[i] != 1.0 || means[i] != Scalar())
skip = false;
}
}
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{
#ifdef HAVE_INF_ENGINE
InferenceEngine::LayerParams lp;
lp.name = name;
lp.type = "ScaleShift";
lp.precision = InferenceEngine::Precision::FP32;
std::shared_ptr<InferenceEngine::ScaleShiftLayer> ieLayer(new InferenceEngine::ScaleShiftLayer(lp));
CV_CheckEQ(inputsData.size(), (size_t)1, "");
CV_CheckEQ(inputsData[0].dims, 4, "");
const size_t numChannels = inputsData[0].size[1];
CV_Assert(numChannels <= 4);
// Scale
auto weights = InferenceEngine::make_shared_blob<float>(InferenceEngine::Precision::FP32,
{numChannels});
weights->allocate();
weights->set(std::vector<float>(numChannels, scaleFactors[0]));
ieLayer->_weights = weights;
// Mean subtraction
auto biases = InferenceEngine::make_shared_blob<float>(InferenceEngine::Precision::FP32,
{numChannels});
biases->allocate();
std::vector<float> biasesVec(numChannels);
for (int i = 0; i < numChannels; ++i)
{
biasesVec[i] = -means[0][i] * scaleFactors[0];
}
biases->set(biasesVec);
ieLayer->_biases = biases;
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif // HAVE_INF_ENGINE
return Ptr<BackendNode>();
}
std::vector<String> outNames;
// Preprocessing parameters for each network's input.
std::vector<double> scaleFactors;
std::vector<Scalar> means;
std::vector<Mat> inputsData;
bool skip;
};
struct BlobManager
{
public:
// Increase references counter to layer output.
void addReference(const LayerPin& lp)
{
std::map<LayerPin, int>::iterator it = refCounter.find(lp);
if (it == refCounter.end())
refCounter[lp] = 1;
else
it->second += 1;
}
void addReferences(const std::vector<LayerPin>& pins)
{
for (int i = 0; i < pins.size(); i++)
{
addReference(pins[i]);
}
}
// Returns number of references to allocated memory that used in specific
// layer blob.
int numReferences(const LayerPin& lp)
{
std::map<LayerPin, LayerPin>::iterator mapIt = reuseMap.find(lp);
CV_Assert(mapIt != reuseMap.end());
LayerPin memHost = mapIt->second;
std::map<LayerPin, int>::iterator refIt = refCounter.find(memHost);
CV_Assert(refIt != refCounter.end());
return refIt->second;
}
// Reuse data allocated in <host> inside the <user> blob.
void reuse(const LayerPin& host, const LayerPin& user)
{
CV_Assert(reuseMap.find(user) == reuseMap.end());
CV_Assert(reuseMap.find(host) != reuseMap.end());
LayerPin memHost = reuseMap[host];
reuseMap[user] = memHost;
if (refCounter.find(memHost) != refCounter.end())
{
std::map<LayerPin, int>::iterator userRefIt = refCounter.find(user);
if (userRefIt != refCounter.end())
{
refCounter[memHost] += userRefIt->second;
refCounter.erase(userRefIt);
}
else
refCounter[memHost] += 1;
}
}
// Decrease references counter to allocated memory inside specific blob.
void releaseReference(const LayerPin& lp)
{
std::map<LayerPin, LayerPin>::iterator mapIt = reuseMap.find(lp);
CV_Assert(mapIt != reuseMap.end());
std::map<LayerPin, int>::iterator refIt = refCounter.find(mapIt->second);
CV_Assert(refIt != refCounter.end());
CV_Assert(refIt->second > 0);
refIt->second -= 1;
}
void releaseReferences(const std::vector<LayerPin>& pins)
{
for (int i = 0; i < pins.size(); i++)
{
releaseReference(pins[i]);
}
}
void reuseOrCreate(const MatShape& shape, const LayerPin& lp, Mat& dst, bool use_half)
{
if (!DNN_DISABLE_MEMORY_OPTIMIZATIONS)
{
Mat bestBlob;
LayerPin bestBlobPin;
std::map<LayerPin, Mat>::iterator hostIt;
std::map<LayerPin, int>::iterator refIt;
const int targetTotal = total(shape);
int bestBlobTotal = INT_MAX;
for (hostIt = memHosts.begin(); hostIt != memHosts.end(); ++hostIt)
{
refIt = refCounter.find(hostIt->first);
// Use only blobs that had references before because if not,
// it might be used as output.
if (refIt != refCounter.end() && refIt->second == 0)
{
Mat& unusedBlob = hostIt->second;
if (unusedBlob.total() >= targetTotal &&
unusedBlob.total() < bestBlobTotal)
{
bestBlobPin = hostIt->first;
bestBlob = unusedBlob;
bestBlobTotal = unusedBlob.total();
}
}
}
if (!bestBlob.empty())
{
reuse(bestBlobPin, lp);
dst = bestBlob.reshape(1, 1).colRange(0, targetTotal).reshape(1, shape);
return;
}
}
{
// if dst already has been allocated with total(shape) elements,
// it won't be recreated and pointer of dst.data remains the same.
dst.create(shape, use_half ? CV_16S : CV_32F);
addHost(lp, dst);
}
}
void allocateBlobsForLayer(LayerData &ld, const LayerShapes& layerShapes,
std::vector<LayerPin>& pinsForInternalBlobs,
bool use_half = false)
{
CV_TRACE_FUNCTION();
pinsForInternalBlobs.clear();
std::vector<Mat>& outputBlobs = ld.outputBlobs,
&internalBlobs = ld.internals;
const ShapesVec& outShapes = layerShapes.out,
internalShapes = layerShapes.internal;
outputBlobs.resize(std::max((size_t)1, outShapes.size())); //layer produce at least one output blob
internalBlobs.resize(internalShapes.size());
CV_Assert(ld.requiredOutputs.size() <= outShapes.size());
// Check that layer could work in-place.
bool inPlace = false;
if (layerShapes.supportInPlace)
{
if (ld.inputBlobs.size() == 1)
{
// Get number of references to the input memory.
int numRef = numReferences(ld.inputBlobsId[0]);
// If current layer is one and only customer of this blob.
inPlace = numRef == 1;
}
}
ShapesVec shapes(outShapes);
shapes.insert(shapes.end(), internalShapes.begin(), internalShapes.end());
std::vector<Mat*> blobs;
for(int i = 0; i < outputBlobs.size(); i++)
{
blobs.push_back(&outputBlobs[i]);
}
for(int i = 0; i < internalBlobs.size(); i++)
{
blobs.push_back(&internalBlobs[i]);
if (total(internalShapes[i]))
{
pinsForInternalBlobs.push_back(LayerPin(ld.id, ld.outputBlobs.size() + i));
}
}
addReferences(pinsForInternalBlobs);
std::map<int, std::vector<int> > idxSizes;
for(int i = 0; i < shapes.size(); i++)
{
idxSizes[total(shapes[i])].push_back(i);
}
std::map<int, std::vector<int> >::reverse_iterator it;
for(it = idxSizes.rbegin(); it != idxSizes.rend(); it++)
{
for(int j = 0; j < it->second.size(); j++)
{
int index = it->second[j];
if (total(shapes[index]))
{
LayerPin blobPin(ld.id, index);
if (index < outShapes.size() && inPlace)
{
CV_Assert(ld.inputBlobs[0]->total() == total(shapes[index]));
ld.outputBlobs[index] = ld.inputBlobs[0]->reshape(1, shapes[index]);
reuse(ld.inputBlobsId[0], blobPin);
}
else
reuseOrCreate(shapes[index], blobPin, *blobs[index], use_half);
}
}
}
}
// Clear internal state. Calls before an every reallocation.
void reset()
{
CV_TRACE_FUNCTION();
refCounter.clear();
reuseMap.clear();
memHosts.clear();
}
private:
// Register allocated memory.
void addHost(const LayerPin& lp, const Mat& mat)
{
CV_Assert(memHosts.find(lp) == memHosts.end());
reuseMap[lp] = lp;
memHosts[lp] = mat;
}
std::map<LayerPin, int> refCounter;
// Maps pin to origin blob (for whom memory was allocated firstly).
// For origin blobs key == value.
std::map<LayerPin, LayerPin> reuseMap;
std::map<LayerPin, Mat> memHosts;
};
static Ptr<BackendWrapper> wrapMat(int backendId, int targetId, cv::Mat& m)
{
if (backendId == DNN_BACKEND_OPENCV)
{
if (targetId == DNN_TARGET_CPU)
return Ptr<BackendWrapper>();
else if (IS_DNN_OPENCL_TARGET(targetId))
return OpenCLBackendWrapper::create(m);
else
CV_Error(Error::StsNotImplemented, "Unknown target identifier");
}
else if (backendId == DNN_BACKEND_HALIDE)
{
CV_Assert(haveHalide());
#ifdef HAVE_HALIDE
return Ptr<BackendWrapper>(new HalideBackendWrapper(targetId, m));
#endif // HAVE_HALIDE
}
else if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
{
CV_Assert(haveInfEngine());
#ifdef HAVE_INF_ENGINE
return Ptr<BackendWrapper>(new InfEngineBackendWrapper(targetId, m));
#endif // HAVE_INF_ENGINE
}
else
CV_Error(Error::StsNotImplemented, "Unknown backend identifier");
return Ptr<BackendWrapper>();
}
struct Net::Impl
{
typedef std::map<int, LayerShapes> LayersShapesMap;
typedef std::map<int, LayerData> MapIdToLayerData;
Impl()
{
//allocate fake net input layer
netInputLayer = Ptr<DataLayer>(new DataLayer());
LayerData &inpl = layers.insert( make_pair(0, LayerData()) ).first->second;
inpl.id = 0;
netInputLayer->name = inpl.name = "_input";
inpl.type = "__NetInputLayer__";
inpl.layerInstance = netInputLayer;
layerNameToId.insert(std::make_pair(inpl.name, inpl.id));
lastLayerId = 0;
netWasAllocated = false;
fusion = true;
preferableBackend = DNN_BACKEND_DEFAULT;
preferableTarget = DNN_TARGET_CPU;
skipInfEngineInit = false;
}
Ptr<DataLayer> netInputLayer;
std::vector<LayerPin> blobsToKeep;
MapIdToLayerData layers;
std::map<String, int> layerNameToId;
BlobManager blobManager;
int preferableBackend;
int preferableTarget;
String halideConfigFile;
bool skipInfEngineInit;
// Map host data to backend specific wrapper.
std::map<void*, Ptr<BackendWrapper> > backendWrappers;
int lastLayerId;
bool netWasAllocated;
bool fusion;
std::vector<int64> layersTimings;
Mat output_blob;
Ptr<BackendWrapper> wrap(Mat& host)
{
if (preferableBackend == DNN_BACKEND_OPENCV && preferableTarget == DNN_TARGET_CPU)
return Ptr<BackendWrapper>();
MatShape shape(host.dims);
for (int i = 0; i < host.dims; ++i)
shape[i] = host.size[i];
void* data = host.data;
if (backendWrappers.find(data) != backendWrappers.end())
{
Ptr<BackendWrapper> baseBuffer = backendWrappers[data];
if (preferableBackend == DNN_BACKEND_OPENCV)
{
CV_Assert(IS_DNN_OPENCL_TARGET(preferableTarget));
return OpenCLBackendWrapper::create(baseBuffer, host);
}
else if (preferableBackend == DNN_BACKEND_HALIDE)
{
CV_Assert(haveHalide());
#ifdef HAVE_HALIDE
return Ptr<BackendWrapper>(new HalideBackendWrapper(baseBuffer, shape));
#endif // HAVE_HALIDE
}
else if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE)
{
return wrapMat(preferableBackend, preferableTarget, host);
}
else
CV_Error(Error::StsNotImplemented, "Unknown backend identifier");
}
Ptr<BackendWrapper> wrapper = wrapMat(preferableBackend, preferableTarget, host);
backendWrappers[data] = wrapper;
return wrapper;
}
#ifdef HAVE_HALIDE
void compileHalide()
{
CV_TRACE_FUNCTION();
CV_Assert(preferableBackend == DNN_BACKEND_HALIDE);
HalideScheduler scheduler(halideConfigFile);
std::vector< std::reference_wrapper<LayerData> > compileList; compileList.reserve(64);
for (MapIdToLayerData::iterator it = layers.begin(); it != layers.end(); ++it)
{
LayerData &ld = it->second;
Ptr<Layer> layer = ld.layerInstance;
if (layer->supportBackend(DNN_BACKEND_HALIDE) && !ld.skip)
{
CV_Assert(!ld.backendNodes[DNN_BACKEND_HALIDE].empty());
bool scheduled = scheduler.process(ld.backendNodes[DNN_BACKEND_HALIDE]);
if (!scheduled)
{
// Use automatic scheduling provided by layer.
layer->applyHalideScheduler(ld.backendNodes[DNN_BACKEND_HALIDE],
ld.inputBlobs, ld.outputBlobs,
preferableTarget);
}
compileList.emplace_back(ld);
}
}
std::atomic<int> progress(0);
auto fn = ([&] () -> void
{
for (;;)
{
int id = progress.fetch_add(1);
if ((size_t)id >= compileList.size())
return;
const LayerData& ld = compileList[id].get();
Ptr<BackendNode> node = ld.backendNodes.find(DNN_BACKEND_HALIDE)->second;
dnn::compileHalide(ld.outputBlobs, node, preferableTarget);
}
});
size_t num_threads = std::min(compileList.size(), (size_t)std::thread::hardware_concurrency());
num_threads = std::max((size_t)1u, std::min((size_t)8u, num_threads));
std::vector<std::thread> threads(num_threads - 1);
for (auto& t: threads) t = std::thread(fn);
fn(); // process own tasks
for (auto& t: threads) t.join();
}
#endif
void clear()
{
CV_TRACE_FUNCTION();
MapIdToLayerData::iterator it;
for (it = layers.begin(); it != layers.end(); it++)
{
if (it->second.id != 0) {
it->second.inputBlobs.clear();
it->second.outputBlobs.clear();
it->second.internals.clear();
}
it->second.skip = false;
//it->second.consumers.clear();
Ptr<Layer> currLayer = it->second.layerInstance;
if( currLayer.empty() )
continue;
currLayer->unsetAttached();
Ptr<PoolingLayer> poolingLayer = currLayer.dynamicCast<PoolingLayer>();
if( !poolingLayer.empty() )
{
poolingLayer->computeMaxIdx = true;
}
}
layersTimings.clear();
}
void setUpNet(const std::vector<LayerPin>& blobsToKeep_ = std::vector<LayerPin>())
{
CV_TRACE_FUNCTION();
if (preferableBackend == DNN_BACKEND_DEFAULT)
preferableBackend = (Backend)PARAM_DNN_BACKEND_DEFAULT;
CV_Assert(preferableBackend != DNN_BACKEND_OPENCV ||
preferableTarget == DNN_TARGET_CPU ||
preferableTarget == DNN_TARGET_OPENCL ||
preferableTarget == DNN_TARGET_OPENCL_FP16);
CV_Assert(preferableBackend != DNN_BACKEND_HALIDE ||
preferableTarget == DNN_TARGET_CPU ||
preferableTarget == DNN_TARGET_OPENCL);
CV_Assert(preferableBackend != DNN_BACKEND_INFERENCE_ENGINE ||
preferableTarget == DNN_TARGET_CPU ||
preferableTarget == DNN_TARGET_OPENCL ||
preferableTarget == DNN_TARGET_OPENCL_FP16 ||
preferableTarget == DNN_TARGET_MYRIAD);
if (!netWasAllocated || this->blobsToKeep != blobsToKeep_)
{
if (preferableBackend == DNN_BACKEND_OPENCV && IS_DNN_OPENCL_TARGET(preferableTarget))
#ifndef HAVE_OPENCL
{
CV_LOG_WARNING(NULL, "DNN: OpenCL target is not available in this OpenCV build, switching to CPU.");
preferableTarget = DNN_TARGET_CPU;
}
#else
{
if (!DNN_OPENCL_ALLOW_ALL_DEVICES)
{
// Current implementation is only valid for GPU (#11494)
if (ocl::Device::getDefault().type() != ocl::Device::TYPE_GPU)
{
CV_LOG_WARNING(NULL, "DNN: OpenCL target is not supported with current OpenCL device (tested with GPUs only), switching to CPU.");
preferableTarget = DNN_TARGET_CPU;
}
else if (preferableTarget == DNN_TARGET_OPENCL_FP16 && !ocl::Device::getDefault().isIntel())
{
CV_LOG_WARNING(NULL,
"DNN: OpenCL target with fp16 precision is not supported "
"with current OpenCL device (tested with Intel GPUs only), "
"switching to OpenCL with fp32 precision.");
preferableTarget = DNN_TARGET_OPENCL;
}
}
}
#endif
clear();
allocateLayers(blobsToKeep_);
MapIdToLayerData::iterator it = layers.find(0);
CV_Assert(it != layers.end());
it->second.skip = netInputLayer->skip;
initBackend();
if (!netWasAllocated )
{
#ifdef HAVE_HALIDE
if (preferableBackend == DNN_BACKEND_HALIDE)
compileHalide();
#else
CV_Assert(preferableBackend != DNN_BACKEND_HALIDE);
#endif
}
netWasAllocated = true;
this->blobsToKeep = blobsToKeep_;
}
}
int getLayerId(const String &layerName)
{
std::map<String, int>::iterator it = layerNameToId.find(layerName);
return (it != layerNameToId.end()) ? it->second : -1;
}
int getLayerId(int id)
{
MapIdToLayerData::iterator it = layers.find(id);
return (it != layers.end()) ? id : -1;
}
int getLayerId(DictValue &layerDesc)
{
if (layerDesc.isInt())
return getLayerId(layerDesc.get<int>());
else if (layerDesc.isString())
return getLayerId(layerDesc.get<String>());
CV_Assert(layerDesc.isInt() || layerDesc.isString());
return -1;
}
String getLayerName(int id)
{
MapIdToLayerData::iterator it = layers.find(id);
return (it != layers.end()) ? it->second.name : "(unknown layer)";
}
LayerData& getLayerData(int id)
{
MapIdToLayerData::iterator it = layers.find(id);
if (it == layers.end())
CV_Error(Error::StsObjectNotFound, format("Layer with requested id=%d not found", id));
return it->second;
}
LayerData& getLayerData(const String &layerName)
{
int id = getLayerId(layerName);
if (id < 0)
CV_Error(Error::StsError, "Requested layer \"" + layerName + "\" not found");
return getLayerData(id);
}
LayerData& getLayerData(const DictValue &layerDesc)
{
CV_Assert(layerDesc.isInt() || layerDesc.isString());
if (layerDesc.isInt())
return getLayerData(layerDesc.get<int>());
else /*if (layerDesc.isString())*/
return getLayerData(layerDesc.get<String>());
}
static void addLayerInput(LayerData &ld, int inNum, LayerPin from)
{
if ((int)ld.inputBlobsId.size() <= inNum)
{
ld.inputBlobsId.resize(inNum + 1);
}
else
{
LayerPin storedFrom = ld.inputBlobsId[inNum];
if (storedFrom.valid() && !storedFrom.equal(from))
CV_Error(Error::StsError, format("Input #%d of layer \"%s\" already was connected",
inNum, ld.name.c_str()));
}
ld.inputBlobsId[inNum] = from;
}
int resolvePinOutputName(LayerData &ld, const String &outName)
{
if (outName.empty())
return 0;
return ld.getLayerInstance()->outputNameToIndex(outName);
}
LayerPin getPinByAlias(const String &layerName)
{
LayerPin pin;
pin.lid = (layerName.empty()) ? 0 : getLayerId(layerName);
if (pin.lid >= 0)
pin.oid = resolvePinOutputName(getLayerData(pin.lid), layerName);
return pin;
}
std::vector<LayerPin> getLayerOutPins(const String &layerName)
{
int lid = (layerName.empty()) ? 0 : getLayerId(layerName);
std::vector<LayerPin> pins;
for (int i = 0; i < layers[lid].outputBlobs.size(); i++)
{
pins.push_back(LayerPin(lid, i));
}
return pins;
}
void connect(int outLayerId, int outNum, int inLayerId, int inNum)
{
CV_Assert(outLayerId < inLayerId);
LayerData &ldOut = getLayerData(outLayerId);
LayerData &ldInp = getLayerData(inLayerId);
addLayerInput(ldInp, inNum, LayerPin(outLayerId, outNum));
ldOut.requiredOutputs.insert(outNum);
ldOut.consumers.push_back(LayerPin(inLayerId, outNum));
}
void initBackend()
{
CV_TRACE_FUNCTION();
if (preferableBackend == DNN_BACKEND_OPENCV)
CV_Assert(preferableTarget == DNN_TARGET_CPU || IS_DNN_OPENCL_TARGET(preferableTarget));
else if (preferableBackend == DNN_BACKEND_HALIDE)
initHalideBackend();
else if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE)
initInfEngineBackend();
else
CV_Error(Error::StsNotImplemented, "Unknown backend identifier");
}
void initHalideBackend()
{
CV_TRACE_FUNCTION();
CV_Assert_N(preferableBackend == DNN_BACKEND_HALIDE, haveHalide());
// Iterator to current layer.
MapIdToLayerData::iterator it = layers.begin();
// Iterator to base layer for fusion. In example, in case of conv+bn+relu
// it'll be a conv layer.
MapIdToLayerData::iterator baseIt = layers.begin();
for (; it != layers.end(); it++)
{
LayerData &ldTop = it->second;
Ptr<Layer> layerTop = ldTop.layerInstance;
if (!layerTop->supportBackend(preferableBackend))
{
// Move base iterator to layer that don't support preferable
// backend to prevent fusion over layer of different backend.
baseIt = it;
continue;
}
// Try to do layers fusion.
LayerData &ldBot = baseIt->second;
Ptr<Layer> layerBot = ldBot.layerInstance;
// 1. Check that bottom and top from the same backends.
if (it != layers.begin() && layerBot->supportBackend(preferableBackend))
{
// 2. Check that current layer works in-place.
bool inPlace = ldTop.inputBlobs.size() == 1 &&
ldBot.outputBlobs.size() == 1 &&
ldTop.inputBlobs[0]->data ==
ldBot.outputBlobs[0].data;
if (inPlace)
{
// 3. Try to attach node.
CV_Assert(!ldBot.backendNodes[preferableBackend].empty());
Ptr<BackendNode> fusedNode =
layerTop->tryAttach(ldBot.backendNodes[preferableBackend]);
if (!fusedNode.empty())
{
ldTop.skip = true;
ldBot.backendNodes[preferableBackend] = fusedNode;
ldBot.outputBlobsWrappers = ldTop.outputBlobsWrappers;
continue;
}
}
}
// No layers fusion.
ldTop.skip = false;
ldTop.backendNodes[DNN_BACKEND_HALIDE] =
layerTop->initHalide(ldTop.inputBlobsWrappers);
baseIt = it;
}
}
#ifdef HAVE_INF_ENGINE
// Before launching Inference Engine graph we need to specify output blobs.
// This function requests output blobs based on inputs references of
// layers from default backend or layers from different graphs.
void addInfEngineNetOutputs(LayerData &ld)
{
Ptr<InfEngineBackendNet> layerNet;
if (ld.backendNodes.find(preferableBackend) != ld.backendNodes.end())
{
Ptr<BackendNode> node = ld.backendNodes[preferableBackend];
if (!node.empty())
{
Ptr<InfEngineBackendNode> ieNode = node.dynamicCast<InfEngineBackendNode>();
CV_Assert(!ieNode.empty()); CV_Assert(!ieNode->net.empty());
layerNet = ieNode->net;
}
}
// For an every input reference we check that it belongs to one of
// the Inference Engine backend graphs. Request an output blob if it is.
// Do nothing if layer's input is from the same graph.
for (int i = 0; i < ld.inputBlobsId.size(); ++i)
{
LayerData &inpLd = layers[ld.inputBlobsId[i].lid];
Ptr<BackendNode> inpNode = inpLd.backendNodes[preferableBackend];
if (!inpNode.empty())
{
Ptr<InfEngineBackendNode> ieInpNode = inpNode.dynamicCast<InfEngineBackendNode>();
CV_Assert(!ieInpNode.empty()); CV_Assert(!ieInpNode->net.empty());
if (layerNet != ieInpNode->net)
{
// layerNet is empty or nodes are from different graphs.
ieInpNode->net->addOutput(ieInpNode->layer->name);
}
}
}
}
#endif // HAVE_INF_ENGINE
void initInfEngineBackend()
{
CV_TRACE_FUNCTION();
CV_Assert_N(preferableBackend == DNN_BACKEND_INFERENCE_ENGINE, haveInfEngine());
#ifdef HAVE_INF_ENGINE
MapIdToLayerData::iterator it;
Ptr<InfEngineBackendNet> net;
for (it = layers.begin(); it != layers.end(); ++it)
{
LayerData &ld = it->second;
if (ld.id == 0)
{
CV_Assert((netInputLayer->outNames.empty() && ld.outputBlobsWrappers.size() == 1) ||
(netInputLayer->outNames.size() == ld.outputBlobsWrappers.size()));
for (int i = 0; i < ld.outputBlobsWrappers.size(); ++i)
{
InferenceEngine::DataPtr dataPtr = infEngineDataNode(ld.outputBlobsWrappers[i]);
dataPtr->name = netInputLayer->outNames.empty() ? ld.name : netInputLayer->outNames[i];
}
}
else
{
for (int i = 0; i < ld.outputBlobsWrappers.size(); ++i)
{
InferenceEngine::DataPtr dataPtr = infEngineDataNode(ld.outputBlobsWrappers[i]);
dataPtr->name = ld.name;
}
}
}
if (skipInfEngineInit)
{
Ptr<BackendNode> node = layers[lastLayerId].backendNodes[preferableBackend];
CV_Assert(!node.empty());
Ptr<InfEngineBackendNode> ieNode = node.dynamicCast<InfEngineBackendNode>();
CV_Assert(!ieNode.empty());
for (it = layers.begin(); it != layers.end(); ++it)
{
LayerData &ld = it->second;
if (ld.id == 0)
{
for (int i = 0; i < ld.inputBlobsWrappers.size(); ++i)
{
InferenceEngine::DataPtr dataPtr = infEngineDataNode(ld.inputBlobsWrappers[i]);
dataPtr->name = netInputLayer->outNames[i];
}
}
else
{
for (int i = 0; i < ld.outputBlobsWrappers.size(); ++i)
{
InferenceEngine::DataPtr dataPtr = infEngineDataNode(ld.outputBlobsWrappers[i]);
dataPtr->name = ld.name;
}
}
ieNode->net->addBlobs(ld.inputBlobsWrappers);
ieNode->net->addBlobs(ld.outputBlobsWrappers);
ld.skip = true;
}
layers[lastLayerId].skip = false;
ieNode->net->init(preferableTarget);
return;
}
// Build Inference Engine networks from sets of layers that support this
// backend. Split a whole model on several Inference Engine networks if
// some of layers is not implemented.
// Set of all input and output blobs wrappers for current network.
std::map<LayerPin, Ptr<BackendWrapper> > netBlobsWrappers;
for (it = layers.begin(); it != layers.end(); ++it)
{
LayerData &ld = it->second;
if (ld.id == 0 && ld.skip)
continue;
bool fused = ld.skip;
Ptr<Layer> layer = ld.layerInstance;
if (!fused && !layer->supportBackend(preferableBackend))
{
addInfEngineNetOutputs(ld);
net = Ptr<InfEngineBackendNet>();
netBlobsWrappers.clear();
layer->preferableTarget = DNN_TARGET_CPU;
continue;
}
ld.skip = true; // Initially skip all Inference Engine supported layers.
// Create a new network if one of inputs from different Inference Engine graph.
for (int i = 0; i < ld.inputBlobsId.size(); ++i)
{
LayerData &inpLd = layers[ld.inputBlobsId[i].lid];
Ptr<BackendNode> inpNode = inpLd.backendNodes[preferableBackend];
if (!inpNode.empty())
{
Ptr<InfEngineBackendNode> ieInpNode = inpNode.dynamicCast<InfEngineBackendNode>();
CV_Assert(!ieInpNode.empty()); CV_Assert(!ieInpNode->net.empty());
if (ieInpNode->net != net)
{
net = Ptr<InfEngineBackendNet>();
netBlobsWrappers.clear();
break;
}
}
}
// The same blobs wrappers cannot be shared between two Inference Engine
// networks because of explicit references between layers and blobs.
// So we need to rewrap all the external blobs.
for (int i = 0; i < ld.inputBlobsId.size(); ++i)
{
LayerPin inPin = ld.inputBlobsId[i];
auto it = netBlobsWrappers.find(inPin);
if (it == netBlobsWrappers.end())
{
ld.inputBlobsWrappers[i] = InfEngineBackendWrapper::create(ld.inputBlobsWrappers[i]);
netBlobsWrappers[inPin] = ld.inputBlobsWrappers[i];
}
else
ld.inputBlobsWrappers[i] = it->second;
}
netBlobsWrappers[LayerPin(ld.id, 0)] = ld.outputBlobsWrappers[0];
Ptr<BackendNode> node;
if (!net.empty())
{
if (fused)
{
bool inPlace = ld.inputBlobsId.size() == 1 && ld.outputBlobs.size() == 1 &&
ld.inputBlobs[0]->data == ld.outputBlobs[0].data;
CV_Assert(inPlace);
node = layers[ld.inputBlobsId[0].lid].backendNodes[preferableBackend];
ld.inputBlobsWrappers = layers[ld.inputBlobsId[0].lid].inputBlobsWrappers;
}
}
else
net = Ptr<InfEngineBackendNet>(new InfEngineBackendNet());
if (!fused)
{
node = layer->initInfEngine(ld.inputBlobsWrappers);
}
else if (node.empty())
continue;
CV_Assert(!node.empty());
ld.backendNodes[preferableBackend] = node;
Ptr<InfEngineBackendNode> ieNode = node.dynamicCast<InfEngineBackendNode>();
CV_Assert(!ieNode.empty());
ieNode->net = net;
if ((preferableTarget == DNN_TARGET_OPENCL_FP16 || preferableTarget == DNN_TARGET_MYRIAD) && !fused)
{
ieNode->layer->precision = InferenceEngine::Precision::FP16;
auto weightableLayer = std::dynamic_pointer_cast<InferenceEngine::WeightableLayer>(ieNode->layer);
if (weightableLayer)
{
if (weightableLayer->_weights)
weightableLayer->_weights = convertFp16(weightableLayer->_weights);
if (weightableLayer->_biases)
weightableLayer->_biases = convertFp16(weightableLayer->_biases);
}
else
{
for (const auto& weights : {"weights", "biases"})
{
auto it = ieNode->layer->blobs.find(weights);
if (it != ieNode->layer->blobs.end())
it->second = convertFp16(it->second);
}
}
}
ieNode->connect(ld.inputBlobsWrappers, ld.outputBlobsWrappers);
net->addBlobs(ld.inputBlobsWrappers);
net->addBlobs(ld.outputBlobsWrappers);
if (!fused)
net->addLayer(ieNode->layer);
addInfEngineNetOutputs(ld);
}
// Initialize all networks.
std::set<InfEngineBackendNet> initializedNets;
for (MapIdToLayerData::reverse_iterator it = layers.rbegin(); it != layers.rend(); ++it)
{
LayerData &ld = it->second;
if (ld.backendNodes.find(preferableBackend) == ld.backendNodes.end())
continue;
Ptr<BackendNode> node = ld.backendNodes[preferableBackend];
if (node.empty())
continue;
Ptr<InfEngineBackendNode> ieNode = node.dynamicCast<InfEngineBackendNode>();
if (ieNode.empty())
continue;
CV_Assert(!ieNode->net.empty());
if (!ieNode->net->isInitialized())
{
ieNode->net->init(preferableTarget);
ld.skip = false;
}
}
#endif // HAVE_INF_ENGINE
}
void allocateLayer(int lid, const LayersShapesMap& layersShapes)
{
CV_TRACE_FUNCTION();
LayerData &ld = layers[lid];
//already allocated
if (ld.flag)
return;
size_t ninputs = ld.inputBlobsId.size();
#if 0
printf("layer %s:", ld.name.c_str());
for (size_t i = 0; i < ninputs; i++)
{
int inp_lid = ld.inputBlobsId[i].lid;
LayerData &inp_ld = layers[inp_lid];
int inp_outputs = (int)inp_ld.outputBlobs.size();
std::cout << " " << inp_ld.name << "(" << inp_outputs;
for( int j = 0; j < inp_outputs; j++ )
{
std::cout << (j == 0 ? ": " : ", ") << inp_ld.outputBlobs[j].size;
}
std::cout << ")";
}
printf("\n");
#endif
//determine parent layers
for (size_t i = 0; i < ninputs; i++)
ld.inputLayersId.insert(ld.inputBlobsId[i].lid);
//allocate parents
for (set<int>::iterator i = ld.inputLayersId.begin(); i != ld.inputLayersId.end(); i++)
allocateLayer(*i, layersShapes);
//bind inputs
if (ld.id == 0) // DataLayer
{
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);
CV_Assert(layerShapesIt != layersShapes.end());
std::vector<LayerPin> pinsForInternalBlobs;
blobManager.allocateBlobsForLayer(ld, layerShapesIt->second, pinsForInternalBlobs,
preferableBackend == DNN_BACKEND_OPENCV &&
preferableTarget == DNN_TARGET_OPENCL_FP16);
ld.outputBlobsWrappers.resize(ld.outputBlobs.size());
for (int i = 0; i < ld.outputBlobs.size(); ++i)
{
ld.outputBlobsWrappers[i] = wrap(ld.outputBlobs[i]);
}
ld.internalBlobsWrappers.resize(ld.internals.size());
for (int i = 0; i < ld.internals.size(); ++i)
{
ld.internalBlobsWrappers[i] = wrap(ld.internals[i]);
}
Ptr<Layer> layerPtr = ld.getLayerInstance();
{
std::vector<Mat> inps(ld.inputBlobs.size());
for (int i = 0; i < ld.inputBlobs.size(); ++i)
{
inps[i] = *ld.inputBlobs[i];
}
layerPtr->finalize(inps, ld.outputBlobs);
layerPtr->preferableTarget = preferableTarget;
#if 0
std::cout << "\toutputs:";
size_t noutputs = ld.outputBlobs.size();
for (size_t j = 0; j < noutputs; j++)
{
std::cout << (j == 0 ? " " : ", ") << ld.outputBlobs[j].size;
}
std::cout << "\n";
#endif
}
// After allocation of layer, we decrease counters to it's input blobs.
blobManager.releaseReferences(ld.inputBlobsId);
blobManager.releaseReferences(pinsForInternalBlobs);
ld.flag = 1;
}
#if 0
#define printf_(args) printf args
#else
#define printf_(args)
#endif
void fuseLayers(const std::vector<LayerPin>& blobsToKeep_)
{
if( !fusion || preferableBackend != DNN_BACKEND_OPENCV &&
preferableBackend != DNN_BACKEND_INFERENCE_ENGINE)
return;
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::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 )
{
printf_(("skipped %s: %s\n", ld.layerInstance->name.c_str(), ld.layerInstance->type.c_str()));
continue;
}
printf_(("analyzing %s: %s\n", ld.layerInstance->name.c_str(), ld.layerInstance->type.c_str()));
// the optimization #1. try to fuse batch norm, scaling and/or activation layers
// with the current layer if they follow it. Normally, the are fused with the convolution layer,
// but some of them (like activation) may be fused with fully-connected, elemwise (+) and
// some other layers.
Ptr<Layer>& currLayer = ld.layerInstance;
if( ld.consumers.size() == 1 && pinsToKeep.count(LayerPin(lid, 0)) == 0 )
{
LayerData* nextData = &layers[ld.consumers[0].lid];
LayerPin lpNext(ld.consumers[0].lid, 0);
while (nextData)
{
Ptr<Layer> nextLayer = nextData->layerInstance;
if (currLayer->tryFuse(nextLayer))
{
printf_(("\tfused with %s\n", nextLayer->name.c_str()));
nextData->skip = true;
ld.outputBlobs = layers[lpNext.lid].outputBlobs;
ld.outputBlobsWrappers = layers[lpNext.lid].outputBlobsWrappers;
if (nextData->consumers.size() == 1)
{
int nextLayerId = nextData->consumers[0].lid;
nextData = &layers[nextLayerId];
lpNext = LayerPin(nextLayerId, 0);
}
else
{
nextData = 0;
break;
}
}
else
break;
}
if (preferableBackend != DNN_BACKEND_OPENCV)
continue; // Go to the next layer.
// TODO: OpenCL target support more fusion styles.
if ( preferableBackend == DNN_BACKEND_OPENCV && IS_DNN_OPENCL_TARGET(preferableTarget) &&
(!cv::ocl::useOpenCL() || (ld.layerInstance->type != "Convolution" &&
ld.layerInstance->type != "MVN" && ld.layerInstance->type != "Pooling" &&
ld.layerInstance->type != "Concat")) )
continue;
while (nextData)
{
// For now, OpenCL target support fusion with activation of ReLU/ChannelsPReLU/Power/Tanh
if (IS_DNN_OPENCL_TARGET(preferableTarget) &&
nextData->type != "ReLU" &&
nextData->type != "ChannelsPReLU" &&
nextData->type != "ReLU6" &&
nextData->type != "TanH" &&
nextData->type != "Power")
break;
Ptr<ActivationLayer> nextActivLayer = nextData->layerInstance.dynamicCast<ActivationLayer>();
if (nextActivLayer.empty())
break;
if (currLayer->setActivation(nextActivLayer))
{
printf_(("\tfused with %s\n", nextActivLayer->name.c_str()));
nextData->skip = true;
ld.outputBlobs = layers[lpNext.lid].outputBlobs;
ld.outputBlobsWrappers = layers[lpNext.lid].outputBlobsWrappers;
if (nextData->consumers.size() == 1)
{
int nextLayerId = nextData->consumers[0].lid;
nextData = &layers[nextLayerId];
lpNext = LayerPin(nextLayerId, 0);
}
else
{
nextData = 0;
break;
}
}
else
break;
}
// fuse convolution layer followed by eltwise + relu
if ( IS_DNN_OPENCL_TARGET(preferableTarget) )
{
Ptr<EltwiseLayer> nextEltwiseLayer;
if( nextData )
nextEltwiseLayer = nextData->layerInstance.dynamicCast<EltwiseLayer>();
if( !nextEltwiseLayer.empty() && pinsToKeep.count(lpNext) == 0 )
{
LayerData *eltwiseData = nextData;
// go down from the second input and find the first non-skipped layer.
LayerData *downLayerData = &layers[eltwiseData->inputBlobsId[1].lid];
CV_Assert(downLayerData);
while (downLayerData->skip)
{
downLayerData = &layers[downLayerData->inputBlobsId[0].lid];
}
CV_Assert(downLayerData);
// second input layer is current layer.
if ( ld.id == downLayerData->id )
{
// go down from the first input and find the first non-skipped layer
downLayerData = &layers[eltwiseData->inputBlobsId[0].lid];
while (downLayerData->skip)
{
if ( !downLayerData->type.compare("Eltwise") )
downLayerData = &layers[downLayerData->inputBlobsId[1].lid];
else
downLayerData = &layers[downLayerData->inputBlobsId[0].lid];
}
Ptr<ConvolutionLayer> convLayer = downLayerData->layerInstance.dynamicCast<ConvolutionLayer>();
// first input layer is convolution layer
if( !convLayer.empty() && eltwiseData->consumers.size() == 1 )
{
// fuse eltwise + activation layer
LayerData *firstConvLayerData = downLayerData;
{
nextData = &layers[eltwiseData->consumers[0].lid];
lpNext = LayerPin(eltwiseData->consumers[0].lid, 0);
Ptr<ActivationLayer> nextActivLayer;
if( nextData )
nextActivLayer = nextData->layerInstance.dynamicCast<ActivationLayer>();
if( !nextActivLayer.empty() && pinsToKeep.count(lpNext) == 0 &&
(!nextData->type.compare("ReLU") ||
!nextData->type.compare("ChannelsPReLU") ||
!nextData->type.compare("Power")) &&
currLayer->setActivation(nextActivLayer) )
{
CV_Assert(firstConvLayerData->outputBlobsWrappers.size() == 1 && ld.inputBlobsWrappers.size() == 1);
ld.inputBlobsWrappers.push_back(firstConvLayerData->outputBlobsWrappers[0]);
printf_(("\tfused with %s\n", nextEltwiseLayer->name.c_str()));
printf_(("\tfused with %s\n", nextActivLayer->name.c_str()));
eltwiseData->skip = true;
nextData->skip = true;
// This optimization for cases like
// some_layer conv
// | |
// +-- eltwise --+
// |
// activ
// This way all the element-wise computations
// (i.e. some_layer+conv or some_layer*conv)
// would be done at [conv] layer. So we need to
// replace [conv]'s output blob to [eltwise]'s one
// considering that [activ] is an in-place layer.
// Also we need to move all the consumers' references.
// To prevent memory collisions (i.e. when input of
// [conv] and output of [eltwise] is the same blob)
// we allocate a new blob.
CV_Assert_N(ld.outputBlobs.size() == 1, ld.outputBlobsWrappers.size() == 1);
ld.outputBlobs[0] = ld.outputBlobs[0].clone();
ld.outputBlobsWrappers[0] = wrap(ld.outputBlobs[0]);
eltwiseData->outputBlobs = ld.outputBlobs;
nextData->outputBlobs = ld.outputBlobs;
eltwiseData->outputBlobsWrappers = ld.outputBlobsWrappers;
nextData->outputBlobsWrappers = ld.outputBlobsWrappers;
// Move references of [activ] layer consumers to the newly allocated blob.
for (int i = 0; i < nextData->consumers.size(); ++i)
{
LayerData& consumer = layers[nextData->consumers[i].lid];
for (int j = 0; j < consumer.inputBlobsId.size(); ++j)
{
if (consumer.inputBlobsId[j].lid == lpNext.lid)
{
consumer.inputBlobs[j] = &ld.outputBlobs[0];
consumer.inputBlobsWrappers[j] = ld.outputBlobsWrappers[0];
break;
}
}
}
}
}
}
}
}
}
}
if (preferableBackend != DNN_BACKEND_OPENCV)
continue; // Go to the next layer.
// the optimization #2. if there is no layer that takes max pooling layer's computed
// max indices (and only some semantical segmentation networks might need this;
// many others only take the maximum values), then we switch the max pooling
// layer to the faster operating mode.
Ptr<PoolingLayer> poolingLayer = ld.layerInstance.dynamicCast<PoolingLayer>();
if( !poolingLayer.empty() && !ld.consumers.empty() )
{
size_t i = 0, nconsumers = ld.consumers.size();
for( ; i < nconsumers; i++ )
if( ld.consumers[i].oid > 0 )
break;
// if there is no layer that takes the second output pin of the pooling layer
// on input then we don't need to compute the indices
if( i >= nconsumers )
{
poolingLayer->computeMaxIdx = false;
printf_(("\tsimplified pooling layer %s\n", poolingLayer->name.c_str()));
}
}
// the optimization #3. if there is concat layer that concatenates channels
// from the inputs together (i.e. axis == 1) then we make the inputs of
// the concat layer to write to the concatenation output buffer
// (and so we eliminate the concatenation layer, because the channels
// are concatenated implicitly).
Ptr<ConcatLayer> concatLayer = ld.layerInstance.dynamicCast<ConcatLayer>();
if( !concatLayer.empty() && concatLayer->axis == 1 && !concatLayer->padding &&
ld.outputBlobs.size() == 1 )
{
Mat& output = ld.outputBlobs[0];
UMat umat_output;
if (!ld.outputBlobsWrappers.empty() &&
(preferableBackend == DNN_BACKEND_OPENCV && IS_DNN_OPENCL_TARGET(preferableTarget)))
{
size_t i, ninputs = ld.inputBlobsId.size();
bool conv_layer = true;
for( i = 0; i < ninputs; i++ )
{
LayerPin pin = ld.inputBlobsId[i];
LayerData* inp_i_data = &layers[pin.lid];
while(inp_i_data->skip &&
inp_i_data->inputBlobsId.size() == 1 &&
inp_i_data->consumers.size() == 1)
{
pin = inp_i_data->inputBlobsId[0];
inp_i_data = &layers[pin.lid];
}
conv_layer = conv_layer && (inp_i_data->getLayerInstance()->type == "Convolution");
}
if (!conv_layer)
continue;
std::vector<UMat> umat_outputBlobs;
umat_outputBlobs = OpenCLBackendWrapper::getUMatVector(ld.outputBlobsWrappers);
umat_output = umat_outputBlobs[0];
}
// TODO: in general, this optimization can always be done, but
// many layers currently check that the input/output blobs are
// continuous arrays. Unfortunately, this is not true when
// the concatenation optimization is applied with batch_size > 1.
// so, for now, we only apply this optimization in the most popular
// case batch_size == 1.
if( output.dims == 4 && output.size[0] == 1 )
{
size_t i, ninputs = ld.inputBlobsId.size();
std::vector<LayerPin> realinputs(ninputs);
for( i = 0; i < ninputs; i++ )
{
LayerPin pin = ld.inputBlobsId[i];
LayerData* inp_i_data = &layers[pin.lid];
while(inp_i_data->skip &&
inp_i_data->inputBlobsId.size() == 1 &&
inp_i_data->consumers.size() == 1)
{
pin = inp_i_data->inputBlobsId[0];
inp_i_data = &layers[pin.lid];
}
printf_(("\treal input for %s is %s\n",
layers[ld.inputBlobsId[i].lid].getLayerInstance()->name.c_str(),
inp_i_data->getLayerInstance()->name.c_str()));
if(inp_i_data->skip || inp_i_data->consumers.size() != 1)
break;
realinputs[i] = pin;
}
if( i >= ninputs )
{
// Allocate new memory to prevent collisions during memory
// reusing (see https://github.com/opencv/opencv/pull/10456).
output = output.clone();
if (preferableBackend == DNN_BACKEND_OPENCV &&
IS_DNN_OPENCL_TARGET(preferableTarget))
{
std::vector<UMat> umats(1);
umat_output = umat_output.clone();
umats[0] = umat_output;
OpenCLBackendWrapper::update(ld.outputBlobsWrappers, umats);
}
Range chrange[] = { Range::all(), Range::all(), Range::all(), Range::all() };
int ofs = 0;
for( i = 0; i < ninputs; i++ )
{
LayerPin pin = realinputs[i];
LayerData* inp_i_data = &layers[pin.lid];
int channels_i = ld.inputBlobs[i]->size[1];
chrange[1] = Range(ofs, ofs + channels_i);
printf_(("\toutput %s(%d) to channels (%d, %d)\n", inp_i_data->layerInstance->name.c_str(),
pin.oid, ofs, ofs + channels_i));
ofs += channels_i;
Mat output_slice = output(chrange);
Mat& curr_output = inp_i_data->outputBlobs[pin.oid];
CV_Assert(output_slice.isContinuous() && output_slice.size == curr_output.size);
Mat* oldPtr = &curr_output;
curr_output = output_slice;
if (preferableBackend == DNN_BACKEND_OPENCV && IS_DNN_OPENCL_TARGET(preferableTarget))
{
std::vector<UMat> umats(inp_i_data->outputBlobsWrappers.size());
umats[pin.oid] = umat_output(chrange);
OpenCLBackendWrapper::update(inp_i_data->outputBlobsWrappers, umats);
}
// Layers that refer old input Mat will refer to the
// new data but the same Mat object.
CV_Assert_N(curr_output.data == output_slice.data, oldPtr == &curr_output);
}
ld.skip = true;
printf_(("\toptimized out Concat layer %s\n", concatLayer->name.c_str()));
}
}
}
}
}
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;
CV_Assert(!layers[0].outputBlobs.empty());
ShapesVec inputShapes;
for(int i = 0; i < layers[0].outputBlobs.size(); i++)
{
Mat& inp = layers[0].outputBlobs[i];
CV_Assert(inp.total());
if (preferableBackend == DNN_BACKEND_OPENCV &&
preferableTarget == DNN_TARGET_OPENCL_FP16)
{
layers[0].outputBlobs[i].create(inp.dims, inp.size, CV_16S);
}
inputShapes.push_back(shape(inp));
}
LayersShapesMap layersShapes;
getLayersShapes(inputShapes, layersShapes);
blobManager.reset();
backendWrappers.clear();
// Fake references to input blobs.
for (int i = 0; i < layers[0].outputBlobs.size(); ++i)
blobManager.addReference(LayerPin(0, i));
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);
}
layersTimings.resize(lastLayerId + 1, 0);
fuseLayers(blobsToKeep_);
}
void forwardLayer(LayerData &ld)
{
CV_TRACE_FUNCTION();
Ptr<Layer> layer = ld.layerInstance;
TickMeter tm;
tm.start();
if( !ld.skip )
{
std::map<int, Ptr<BackendNode> >::iterator it = ld.backendNodes.find(preferableBackend);
if (preferableBackend == DNN_BACKEND_OPENCV || it == ld.backendNodes.end() || it->second.empty())
{
if (preferableBackend == DNN_BACKEND_OPENCV && IS_DNN_OPENCL_TARGET(preferableTarget))
{
std::vector<UMat> umat_inputBlobs = OpenCLBackendWrapper::getUMatVector(ld.inputBlobsWrappers);
std::vector<UMat> umat_outputBlobs = OpenCLBackendWrapper::getUMatVector(ld.outputBlobsWrappers);
std::vector<UMat> umat_internalBlobs = OpenCLBackendWrapper::getUMatVector(ld.internalBlobsWrappers);
layer->forward(umat_inputBlobs,
umat_outputBlobs,
umat_internalBlobs);
if (DNN_CHECK_NAN_INF)
{
bool fail = false;
for (size_t i = 0; i < umat_outputBlobs.size(); ++i)
{
UMat& u = umat_outputBlobs[i];
Mat m;
if (u.depth() == CV_16S) // FP16
convertFp16(u, m);
else
m = u.getMat(ACCESS_READ);
if (!checkRange(m))
{
std::cerr << "WARNING: NaN detected in layer output: id=" << ld.id << " name=" << layer->name << std::endl;
std::cerr << "output id=" << i << " output shape=" << shape(m) << std::endl;
fail = true;
}
else if (!checkRange(m, true, NULL, -1e6, 1e6))
{
std::cerr << "WARNING: Inf detected in layer output: id=" << ld.id << " name=" << layer->name << std::endl;
std::cerr << "output id=" << i << " output shape=" << shape(m) << std::endl;
fail = true;
}
}
if (fail)
{
for (size_t i = 0; i < umat_inputBlobs.size(); ++i)
{
UMat& u = umat_inputBlobs[i];
Mat m;
if (u.depth() == CV_16S) // FP16
convertFp16(u, m);
else
m = u.getMat(ACCESS_READ);
std::cout << "INPUT " << i << " " << cv::typeToString(u.type()) << " " << shape(m) << std::endl;
if (DNN_CHECK_NAN_INF_DUMP) std::cout << m.reshape(1, 1) << std::endl;
}
for (size_t i = 0; i < umat_outputBlobs.size(); ++i)
{
UMat& u = umat_outputBlobs[i];
Mat m;
if (u.depth() == CV_16S) // FP16
convertFp16(u, m);
else
m = u.getMat(ACCESS_READ);
std::cout << "OUTPUT " << i << " " << cv::typeToString(u.type()) << " " << shape(m) << std::endl;
if (DNN_CHECK_NAN_INF_DUMP) std::cout << m.reshape(1, 1) << std::endl;
}
for (size_t i = 0; i < umat_internalBlobs.size(); ++i)
{
UMat& u = umat_internalBlobs[i];
Mat m;
if (u.depth() == CV_16S) // FP16
convertFp16(u, m);
else
m = u.getMat(ACCESS_READ);
std::cout << "INTERNAL " << i << " " << shape(m) << std::endl;
if (DNN_CHECK_NAN_INF_DUMP) std::cout << cv::typeToString(u.type()) << " " << m.reshape(1, 1) << std::endl;
}
if (DNN_CHECK_NAN_INF_RAISE_ERROR)
CV_Assert(!fail);
}
}
OpenCLBackendWrapper::update(ld.outputBlobsWrappers, umat_outputBlobs);
}
else
{
for (int i = 0, n = ld.inputBlobsWrappers.size(); i < n; ++i)
{
if (!ld.inputBlobsWrappers[i].empty())
ld.inputBlobsWrappers[i]->copyToHost();
}
std::vector<Mat> inps(ld.inputBlobs.size());
for (int i = 0; i < ld.inputBlobs.size(); ++i)
{
inps[i] = *ld.inputBlobs[i];
}
layer->forward(inps, ld.outputBlobs, ld.internals);
if (DNN_CHECK_NAN_INF)
{
bool fail = false;
for (size_t i = 0; i < ld.outputBlobs.size(); ++i)
{
const Mat& m = ld.outputBlobs[i];
if (!checkRange(m))
{
std::cerr << "WARNING: NaN detected in layer output: id=" << ld.id << " name=" << layer->name << std::endl;
std::cerr << "output id=" << i << " output shape=" << shape(m) << std::endl;
fail = true;
}
else if (!checkRange(m, true, NULL, -1e6, 1e6))
{
std::cerr << "WARNING: Inf detected in layer output: id=" << ld.id << " name=" << layer->name << std::endl;
std::cerr << "output id=" << i << " output shape=" << shape(m) << std::endl;
fail = true;
}
}
if (fail)
{
for (size_t i = 0; i < ld.inputBlobs.size(); ++i)
{
const Mat* pM = ld.inputBlobs[i];
if (!pM)
{
std::cout << "INPUT " << i << " is NULL" << std::endl;
continue;
}
const Mat& m = *pM;
std::cout << "INPUT " << i << " " << cv::typeToString(m.type()) << " " << shape(m) << std::endl;
if (DNN_CHECK_NAN_INF_DUMP) std::cout << m.reshape(1, 1) << std::endl;
}
for (size_t i = 0; i < ld.outputBlobs.size(); ++i)
{
const Mat& m = ld.outputBlobs[i];
std::cout << "OUTPUT " << i << " " << cv::typeToString(m.type()) << " " << shape(m) << std::endl;
if (DNN_CHECK_NAN_INF_DUMP) std::cout << m.reshape(1, 1) << std::endl;
}
for (size_t i = 0; i < ld.internals.size(); ++i)
{
const Mat& m = ld.internals[i];
std::cout << "INTERNAL " << i << " " << cv::typeToString(m.type()) << " " << shape(m) << std::endl;
if (DNN_CHECK_NAN_INF_DUMP) std::cout << m.reshape(1, 1) << std::endl;
}
if (DNN_CHECK_NAN_INF_RAISE_ERROR)
CV_Assert(!fail);
}
}
for (int i = 0, n = ld.outputBlobsWrappers.size(); i < n; ++i)
{
if (!ld.outputBlobsWrappers[i].empty())
ld.outputBlobsWrappers[i]->setHostDirty();
}
}
}
else
{
Ptr<BackendNode> node = it->second;
CV_Assert(!node.empty());
if (preferableBackend == DNN_BACKEND_HALIDE)
{
forwardHalide(ld.outputBlobsWrappers, node);
}
else if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE)
{
forwardInfEngine(node);
}
else
{
CV_Error(Error::StsNotImplemented, "Unknown backend identifier");
}
}
}
else
tm.reset();
tm.stop();
layersTimings[ld.id] = tm.getTimeTicks();
ld.flag = 1;
}
void forwardToLayer(LayerData &ld, bool clearFlags = true)
{
CV_TRACE_FUNCTION();
if (clearFlags)
{
MapIdToLayerData::iterator it;
for (it = layers.begin(); it != layers.end(); it++)
it->second.flag = 0;
}
//already was forwarded
if (ld.flag)
return;
//forward parents
MapIdToLayerData::iterator it;
for (it = layers.begin(); it != layers.end() && (it->second.id < ld.id); ++it)
{
LayerData &ld = it->second;
if (ld.flag)
continue;
forwardLayer(ld);
}
//forward itself
forwardLayer(ld);
}
void forwardAll()
{
CV_TRACE_FUNCTION();
MapIdToLayerData::reverse_iterator last_layer = layers.rbegin();
CV_Assert(last_layer != layers.rend());
forwardToLayer(last_layer->second, true);
}
void getLayerShapesRecursively(int id, LayersShapesMap& inOutShapes)
{
std::vector<LayerPin>& inputLayerIds = layers[id].inputBlobsId;
if (inOutShapes[id].in.empty())
{
for(int i = 0; i < inputLayerIds.size(); i++)
{
int layerId = inputLayerIds[i].lid;
LayersShapesMap::iterator it =
inOutShapes.find(layerId);
if(it == inOutShapes.end() ||
it->second.out.empty())
{
getLayerShapesRecursively(layerId, inOutShapes);
}
const MatShape& shape = inOutShapes[layerId].out[inputLayerIds[i].oid];
inOutShapes[id].in.push_back(shape);
}
}
const ShapesVec& is = inOutShapes[id].in;
ShapesVec& os = inOutShapes[id].out;
ShapesVec& ints = inOutShapes[id].internal;
int requiredOutputs = layers[id].requiredOutputs.size();
inOutShapes[id].supportInPlace =
layers[id].getLayerInstance()->getMemoryShapes(is, requiredOutputs, os, ints);
}
void getLayersShapes(const ShapesVec& netInputShapes,
LayersShapesMap& inOutShapes)
{
inOutShapes.clear();
inOutShapes[0].in = netInputShapes; //insert shape for first input layer
for (MapIdToLayerData::iterator it = layers.begin();
it != layers.end(); it++)
{
getLayerShapesRecursively(it->first, inOutShapes);
}
}
void getLayerShapes(const ShapesVec& netInputShapes,
const int layerId,
LayerShapes& shapes)
{
LayersShapesMap inOutShapes;
inOutShapes[0].in = netInputShapes; //insert shape for first input layer
getLayerShapesRecursively(layerId, inOutShapes);
shapes = inOutShapes[layerId];
}
LayerPin getLatestLayerPin(const std::vector<LayerPin>& pins)
{
return *std::max_element(pins.begin(), pins.end());
}
Mat getBlob(const LayerPin& pin)
{
CV_TRACE_FUNCTION();
if (!pin.valid())
CV_Error(Error::StsObjectNotFound, "Requested blob not found");
LayerData &ld = layers[pin.lid];
if ((size_t)pin.oid >= ld.outputBlobs.size())
{
CV_Error(Error::StsOutOfRange, format("Layer \"%s\" produce only %zu outputs, "
"the #%d was requested", ld.name.c_str(),
ld.outputBlobs.size(), pin.oid));
}
if (preferableTarget != DNN_TARGET_CPU)
{
CV_Assert(!ld.outputBlobsWrappers.empty() && !ld.outputBlobsWrappers[pin.oid].empty());
// Transfer data to CPU if it's require.
ld.outputBlobsWrappers[pin.oid]->copyToHost();
}
if (ld.outputBlobs[pin.oid].depth() == CV_16S)
{
convertFp16(ld.outputBlobs[pin.oid], output_blob);
return output_blob;
}
else
return ld.outputBlobs[pin.oid];
}
Mat getBlob(String outputName)
{
return getBlob(getPinByAlias(outputName));
}
};
Net::Net() : impl(new Net::Impl)
{
}
Net Net::readFromModelOptimizer(const String& xml, const String& bin)
{
#ifndef HAVE_INF_ENGINE
CV_Error(Error::StsError, "Build OpenCV with Inference Engine to enable loading models from Model Optimizer.");
#else
InferenceEngine::CNNNetReader reader;
reader.ReadNetwork(xml);
reader.ReadWeights(bin);
InferenceEngine::CNNNetwork ieNet = reader.getNetwork();
std::vector<String> inputsNames;
for (auto& it : ieNet.getInputsInfo())
{
inputsNames.push_back(it.first);
}
Net cvNet;
cvNet.setInputsNames(inputsNames);
Ptr<InfEngineBackendNode> backendNode(new InfEngineBackendNode(0));
backendNode->net = Ptr<InfEngineBackendNet>(new InfEngineBackendNet(ieNet));
for (auto& it : ieNet.getOutputsInfo())
{
Ptr<Layer> cvLayer(new InfEngineBackendLayer(it.second));
InferenceEngine::CNNLayerPtr ieLayer = ieNet.getLayerByName(it.first.c_str());
CV_Assert(ieLayer);
LayerParams lp;
int lid = cvNet.addLayer(it.first, "", lp);
LayerData& ld = cvNet.impl->layers[lid];
cvLayer->name = it.first;
cvLayer->type = ieLayer->type;
ld.layerInstance = cvLayer;
ld.backendNodes[DNN_BACKEND_INFERENCE_ENGINE] = backendNode;
for (int i = 0; i < inputsNames.size(); ++i)
cvNet.connect(0, i, lid, i);
}
cvNet.setPreferableBackend(DNN_BACKEND_INFERENCE_ENGINE);
cvNet.impl->skipInfEngineInit = true;
return cvNet;
#endif // HAVE_INF_ENGINE
}
Net::~Net()
{
}
int Net::addLayer(const String &name, const String &type, LayerParams &params)
{
CV_TRACE_FUNCTION();
if (impl->getLayerId(name) >= 0)
{
CV_Error(Error::StsBadArg, "Layer \"" + name + "\" already into net");
return -1;
}
int id = ++impl->lastLayerId;
impl->layerNameToId.insert(std::make_pair(name, id));
impl->layers.insert(std::make_pair(id, LayerData(id, name, type, params)));
return id;
}
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);
return newLid;
}
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);
CV_Assert(outPin.valid() && inpPin.valid());
impl->connect(outPin.lid, outPin.oid, inpPin.lid, inpPin.oid);
}
Mat Net::forward(const String& outputName)
{
CV_TRACE_FUNCTION();
String layerName = outputName;
if (layerName.empty())
layerName = getLayerNames().back();
std::vector<LayerPin> pins(1, impl->getPinByAlias(layerName));
impl->setUpNet(pins);
impl->forwardToLayer(impl->getLayerData(layerName));
return impl->getBlob(layerName);
}
void Net::forward(OutputArrayOfArrays outputBlobs, const String& outputName)
{
CV_TRACE_FUNCTION();
String layerName = outputName;
if (layerName.empty())
layerName = getLayerNames().back();
std::vector<LayerPin> pins(1, impl->getPinByAlias(layerName));
impl->setUpNet(pins);
impl->forwardToLayer(impl->getLayerData(layerName));
LayerPin pin = impl->getPinByAlias(layerName);
LayerData &ld = impl->layers[pin.lid];
if (outputBlobs.isUMat())
{
impl->getBlob(layerName).copyTo(outputBlobs);
}
else if (outputBlobs.isMat())
{
outputBlobs.assign(impl->getBlob(layerName));
}
else if (outputBlobs.isMatVector())
{
if (impl->preferableTarget != DNN_TARGET_CPU)
{
for (int i = 0; i < ld.outputBlobsWrappers.size(); ++i)
{
CV_Assert(!ld.outputBlobsWrappers[i].empty());
ld.outputBlobsWrappers[i]->copyToHost();
}
}
if (ld.outputBlobs[0].depth() == CV_32F)
{
std::vector<Mat> & outputvec = *(std::vector<Mat> *)outputBlobs.getObj();
outputvec = ld.outputBlobs;
} else {
std::vector<Mat> & outputvec = *(std::vector<Mat> *)outputBlobs.getObj();
outputvec.resize(ld.outputBlobs.size());
for (int i = 0; i < outputvec.size(); i++)
convertFp16(ld.outputBlobs[i], outputvec[i]);
}
}
else if (outputBlobs.isUMatVector())
{
std::vector<UMat> & outputvec = *(std::vector<UMat> *)outputBlobs.getObj();
if (impl->preferableBackend == DNN_BACKEND_OPENCV &&
IS_DNN_OPENCL_TARGET(impl->preferableTarget))
{
if (impl->preferableTarget == DNN_TARGET_OPENCL)
outputvec = OpenCLBackendWrapper::getUMatVector(ld.outputBlobsWrappers);
else if (impl->preferableTarget == DNN_TARGET_OPENCL_FP16)
{
std::vector<UMat> out_vec = OpenCLBackendWrapper::getUMatVector(ld.outputBlobsWrappers);
outputvec.resize(out_vec.size());
for (int i = 0; i < out_vec.size(); i++)
convertFp16(out_vec[i], outputvec[i]);
}
}
else
{
outputvec.resize(ld.outputBlobs.size());
for (int i = 0; i < outputvec.size(); ++i)
ld.outputBlobs[i].copyTo(outputvec[i]);
}
}
}
void Net::forward(OutputArrayOfArrays outputBlobs,
const std::vector<String>& outBlobNames)
{
CV_TRACE_FUNCTION();
std::vector<LayerPin> pins;
for (int i = 0; i < outBlobNames.size(); i++)
{
pins.push_back(impl->getPinByAlias(outBlobNames[i]));
}
impl->setUpNet(pins);
LayerPin out = impl->getLatestLayerPin(pins);
impl->forwardToLayer(impl->getLayerData(out.lid));
std::vector<Mat> matvec;
for (int i = 0; i < pins.size(); i++)
{
matvec.push_back(impl->getBlob(pins[i]));
}
std::vector<Mat> & outputvec = *(std::vector<Mat> *)outputBlobs.getObj();
outputvec = matvec;
}
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++)
{
std::vector<LayerPin> lp = impl->getLayerOutPins(outBlobNames[i]);
pins.insert(pins.end(), lp.begin(), lp.end());
}
impl->setUpNet(pins);
LayerPin out = impl->getLatestLayerPin(pins);
impl->forwardToLayer(impl->getLayerData(out.lid));
outputBlobs.resize(outBlobNames.size());
for (int i = 0; i < outBlobNames.size(); i++)
{
std::vector<LayerPin> lp = impl->getLayerOutPins(outBlobNames[i]);
for (int i = 0; i < lp.size(); i++)
{
outputBlobs[i].push_back(impl->getBlob(lp[i]));
}
}
}
void Net::setPreferableBackend(int backendId)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG(backendId);
if( impl->preferableBackend != backendId )
{
impl->preferableBackend = backendId;
impl->netWasAllocated = false;
impl->clear();
}
}
void Net::setPreferableTarget(int targetId)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG(targetId);
if( impl->preferableTarget != targetId )
{
impl->preferableTarget = targetId;
if (IS_DNN_OPENCL_TARGET(targetId))
{
#ifndef HAVE_OPENCL
#ifdef HAVE_INF_ENGINE
if (impl->preferableBackend == DNN_BACKEND_OPENCV)
#else
if (impl->preferableBackend == DNN_BACKEND_DEFAULT ||
impl->preferableBackend == DNN_BACKEND_OPENCV)
#endif // HAVE_INF_ENGINE
impl->preferableTarget = DNN_TARGET_CPU;
#else
bool fp16 = ocl::Device::getDefault().isExtensionSupported("cl_khr_fp16");
if (!fp16 && targetId == DNN_TARGET_OPENCL_FP16)
impl->preferableTarget = DNN_TARGET_OPENCL;
#endif
}
impl->netWasAllocated = false;
impl->clear();
}
}
void Net::setInputsNames(const std::vector<String> &inputBlobNames)
{
CV_TRACE_FUNCTION();
impl->netInputLayer->setNames(inputBlobNames);
}
void Net::setInput(InputArray blob, const String& name, double scalefactor, const Scalar& mean)
{
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);
if (!pin.valid())
CV_Error(Error::StsObjectNotFound, "Requested blob \"" + name + "\" not found");
LayerData &ld = impl->layers[pin.lid];
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);
impl->netInputLayer->scaleFactors.resize(numInputs);
impl->netInputLayer->means.resize(numInputs);
MatShape prevShape = shape(impl->netInputLayer->inputsData[pin.oid]);
Mat blob_ = blob.getMat();
bool oldShape = prevShape == shape(blob_);
if (oldShape)
{
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())
{
ld.outputBlobsWrappers[pin.oid]->setHostDirty();
}
impl->netInputLayer->scaleFactors[pin.oid] = scalefactor;
impl->netInputLayer->means[pin.oid] = mean;
impl->netWasAllocated = impl->netWasAllocated && oldShape;
}
Mat Net::getParam(LayerId layer, int numParam)
{
LayerData &ld = impl->getLayerData(layer);
std::vector<Mat> &layerBlobs = ld.getLayerInstance()->blobs;
CV_Assert(numParam < (int)layerBlobs.size());
return layerBlobs[numParam];
}
void Net::setParam(LayerId layer, int numParam, const Mat &blob)
{
LayerData &ld = impl->getLayerData(layer);
std::vector<Mat> &layerBlobs = ld.getLayerInstance()->blobs;
CV_Assert(numParam < (int)layerBlobs.size());
//we don't make strong checks, use this function carefully
layerBlobs[numParam] = blob;
}
int Net::getLayerId(const String &layer)
{
return impl->getLayerId(layer);
}
Ptr<Layer> Net::getLayer(LayerId layerId)
{
LayerData &ld = impl->getLayerData(layerId);
return ld.getLayerInstance();
}
std::vector<Ptr<Layer> > Net::getLayerInputs(LayerId layerId)
{
LayerData &ld = impl->getLayerData(layerId);
if (!ld.layerInstance)
CV_Error(Error::StsNullPtr, format("Requested layer \"%s\" was not initialized", ld.name.c_str()));
std::vector<Ptr<Layer> > inputLayers;
inputLayers.reserve(ld.inputLayersId.size());
std::set<int>::iterator it;
for (it = ld.inputLayersId.begin(); it != ld.inputLayersId.end(); ++it) {
inputLayers.push_back(getLayer(*it));
}
return inputLayers;
}
std::vector<String> Net::getLayerNames() const
{
std::vector<String> res;
res.reserve(impl->layers.size());
Impl::MapIdToLayerData::iterator it;
for (it = impl->layers.begin(); it != impl->layers.end(); it++)
{
if (it->second.id) //skip Data layer
res.push_back(it->second.name);
}
return res;
}
bool Net::empty() const
{
return impl->layers.size() <= 1; //first layer is default Data layer
}
std::vector<int> Net::getUnconnectedOutLayers() const
{
std::vector<int> layersIds;
Impl::MapIdToLayerData::iterator it;
for (it = impl->layers.begin(); it != impl->layers.end(); it++)
{
int lid = it->first;
LayerData &ld = it->second;
if (ld.requiredOutputs.size() == 0)
layersIds.push_back(lid);
}
return layersIds;
}
std::vector<String> Net::getUnconnectedOutLayersNames() const
{
std::vector<int> ids = getUnconnectedOutLayers();
const size_t n = ids.size();
std::vector<String> names(n);
for (size_t i = 0; i < n; ++i)
{
names[i] = impl->layers[ids[i]].name;
}
return names;
}
void Net::getLayersShapes(const ShapesVec& netInputShapes,
std::vector<int>& layersIds,
std::vector<ShapesVec>& inLayersShapes,
std::vector<ShapesVec>& outLayersShapes) const
{
layersIds.clear();
inLayersShapes.clear();
outLayersShapes.clear();
Impl::LayersShapesMap inOutShapes;
impl->getLayersShapes(netInputShapes, inOutShapes);
for(Impl::LayersShapesMap::const_iterator it = inOutShapes.begin();
it != inOutShapes.end(); it++)
{
layersIds.push_back(it->first);
inLayersShapes.push_back(it->second.in);
outLayersShapes.push_back(it->second.out);
}
}
void Net::getLayersShapes(const MatShape& netInputShape,
std::vector<int>& layerIds,
std::vector<ShapesVec>& inLayersShapes,
std::vector<ShapesVec>& outLayersShapes) const
{
getLayersShapes(ShapesVec(1, netInputShape),
layerIds, inLayersShapes, outLayersShapes);
}
void Net::getLayerShapes(const MatShape& netInputShape,
const int layerId,
ShapesVec& inLayerShapes,
ShapesVec& outLayerShapes) const
{
getLayerShapes(ShapesVec(1, netInputShape),
layerId, inLayerShapes, outLayerShapes);
}
void Net::getLayerShapes(const ShapesVec& netInputShapes,
const int layerId,
ShapesVec& inLayerShapes,
ShapesVec& outLayerShapes) const
{
LayerShapes shapes;
impl->getLayerShapes(netInputShapes, layerId, shapes);
inLayerShapes = shapes.in;
outLayerShapes = shapes.out;
}
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;
getLayersShapes(netInputShapes, ids, inShapes, outShapes);
CV_Assert(inShapes.size() == outShapes.size());
CV_Assert(inShapes.size() == ids.size());
for(int i = 0; i < ids.size(); i++)
{
flops += impl->layers[ids[i]].getLayerInstance()->getFLOPS(inShapes[i],
outShapes[i]);
}
return flops;
}
int64 Net::getFLOPS(const MatShape& netInputShape) const
{
return getFLOPS(std::vector<MatShape>(1, netInputShape));
}
int64 Net::getFLOPS(const int layerId,
const std::vector<MatShape>& netInputShapes) const
{
Impl::MapIdToLayerData::iterator layer = impl->layers.find(layerId);
CV_Assert(layer != impl->layers.end());
LayerShapes shapes;
impl->getLayerShapes(netInputShapes, layerId, shapes);
return layer->second.getLayerInstance()->getFLOPS(shapes.in, shapes.out);
}
int64 Net::getFLOPS(const int layerId,
const MatShape& netInputShape) const
{
return getFLOPS(layerId, std::vector<MatShape>(1, netInputShape));
}
void Net::getLayerTypes(std::vector<String>& layersTypes) const
{
layersTypes.clear();
std::map<String, int> layers;
for (Impl::MapIdToLayerData::iterator it = impl->layers.begin();
it != impl->layers.end(); it++)
{
if (layers.find(it->second.type) == layers.end())
layers[it->second.type] = 0;
layers[it->second.type]++;
}
for (std::map<String, int>::iterator it = layers.begin();
it != layers.end(); it++)
{
layersTypes.push_back(it->first);
}
}
int Net::getLayersCount(const String& layerType) const
{
int count = 0;
for (Impl::MapIdToLayerData::iterator it = impl->layers.begin();
it != impl->layers.end(); it++)
{
if (it->second.type == layerType)
count++;
}
return count;
}
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());
weights = blobs = 0;
for(int i = 0; i < layer->second.params.blobs.size(); i++)
{
const Mat& weightsBlob = layer->second.params.blobs[i];
weights += weightsBlob.total()*weightsBlob.elemSize();
}
ShapesVec inLayerShapes, outLayerShapes;
getLayerShapes(netInputShapes, layerId, inLayerShapes, outLayerShapes);
for(int i = 0; i < outLayerShapes.size(); i++)
{
blobs += total(outLayerShapes[i]) * sizeof(float);
}
}
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);
weights = blobs = 0;
for(int i = 0; i < layerIds.size(); i++)
{
weights += w[i];
blobs += b[i];
}
}
void Net::getMemoryConsumption(const int layerId,
const MatShape& netInputShape,
size_t& weights, size_t& blobs) const
{
getMemoryConsumption(layerId, std::vector<MatShape>(1, netInputShape),
weights, blobs);
}
void Net::getMemoryConsumption(const MatShape& netInputShape,
size_t& weights, size_t& blobs) const
{
getMemoryConsumption(std::vector<MatShape>(1, netInputShape),
weights, blobs);
}
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();
std::vector<std::vector<MatShape> > inLayerShapes, outLayerShapes;
getLayersShapes(netInputShapes, layerIds, inLayerShapes, outLayerShapes);
for(int i = 0; i < layerIds.size(); i++)
{
int w = 0, b = 0;
Impl::MapIdToLayerData::iterator layer = impl->layers.find(layerIds[i]);
CV_Assert(layer != impl->layers.end());
for(int j = 0; j < layer->second.params.blobs.size(); j++)
{
const Mat& weightsBlob = layer->second.params.blobs[j];
w += weightsBlob.total()*weightsBlob.elemSize();
}
for(int j = 0; j < outLayerShapes[i].size(); j++)
{
b += total(outLayerShapes[i][j]) * sizeof(float);
}
weights.push_back(w);
blobs.push_back(b);
}
}
void Net::getMemoryConsumption(const MatShape& netInputShape, std::vector<int>& layerIds,
std::vector<size_t>& weights, std::vector<size_t>& blobs) const
{
getMemoryConsumption(std::vector<MatShape>(1, netInputShape), layerIds,
weights, blobs);
}
void Net::enableFusion(bool fusion)
{
if( impl->fusion != fusion )
{
impl->fusion = fusion;
impl->netWasAllocated = false;
impl->clear();
}
}
void Net::setHalideScheduler(const String& scheduler)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(scheduler, "scheduler", scheduler.c_str());
impl->halideConfigFile = scheduler;
}
int64 Net::getPerfProfile(std::vector<double>& timings)
{
timings = std::vector<double>(impl->layersTimings.begin() + 1, impl->layersTimings.end());
int64 total = std::accumulate(timings.begin(), timings.end(), 0);
return total;
}
//////////////////////////////////////////////////////////////////////////
Layer::Layer() { preferableTarget = DNN_TARGET_CPU; }
Layer::Layer(const LayerParams &params)
: blobs(params.blobs), name(params.name), type(params.type)
{
preferableTarget = DNN_TARGET_CPU;
}
void Layer::setParamsFrom(const LayerParams &params)
{
blobs = params.blobs;
name = params.name;
type = params.type;
}
int Layer::inputNameToIndex(String)
{
return -1;
}
int Layer::outputNameToIndex(const String&)
{
return 0;
}
bool Layer::supportBackend(int backendId)
{
return backendId == DNN_BACKEND_OPENCV;
}
Ptr<BackendNode> Layer::initHalide(const std::vector<Ptr<BackendWrapper> > &)
{
CV_Error(Error::StsNotImplemented, "Halide pipeline of " + type +
" layers is not defined.");
return Ptr<BackendNode>();
}
Ptr<BackendNode> Layer::initInfEngine(const std::vector<Ptr<BackendWrapper> > &)
{
CV_Error(Error::StsNotImplemented, "Inference Engine pipeline of " + type +
" layers is not defined.");
return Ptr<BackendNode>();
}
void Layer::applyHalideScheduler(Ptr<BackendNode>& node, const std::vector<Mat*> &inputs,
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();
int outW, outH, outC, outN;
getCanonicalSize(outputs[0].size, &outW, &outH, &outC, &outN);
if (targetId == DNN_TARGET_CPU)
{
if (outW == 1 && outH == 1)
{
if (outC + outN == 1)
return;
if (outC > 8)
top.split(c, co, ci, 8)
.fuse(x, y, tile).fuse(co, tile, tile).fuse(n, tile, tile)
.parallel(tile)
.vectorize(ci, 8);
else
top.fuse(x, y, tile).fuse(c, tile, tile).fuse(n, tile, tile)
.parallel(tile);
}
else
{
if (outH > 2)
{
top.reorder(x, c, y)
.split(y, yo, yi, 2)
.fuse(yo, n, tile)
.parallel(tile)
.unroll(yi)
.vectorize(x, outW >= 16 ? 16 : outW);
}
}
}
else if (targetId == DNN_TARGET_OPENCL)
{
if (outW == 1 && outH == 1)
{
int c_split = outC > 8 ? (outC > 16 ? 8 : 4) : outC;
top.split(c, co, ci, c_split)
.fuse(x, y, tile).fuse(co, tile, tile).fuse(n, tile, tile)
.gpu_blocks(tile)
.gpu_threads(ci);
}
else
{
int x_split = outW > 8 ? (outW >= 32 ? 16 : 8) : outW;
int y_split = outH > 8 ? (outH >= 32 ? 16 : 8) : outH;
// Supported vectorization widths: 2, 3, 4, 8, 16
int c_split = outC > 8 ? (outC > 16 ? 8 : 4) : std::min(4, outC);
top.split(x, xo, xi, x_split).split(y, yo, yi, y_split)
.split(c, co, ci, c_split)
.gpu_blocks(xo, yo, co)
.gpu_threads(xi, yi)
.reorder(xi, yi, ci, xo, yo, co)
.vectorize(ci);
}
}
else
CV_Error(Error::StsNotImplemented, "Unknown target identifier");
#endif // HAVE_HALIDE
}
Ptr<BackendNode> Layer::tryAttach(const Ptr<BackendNode>& node)
{
return Ptr<BackendNode>();
}
bool Layer::setActivation(const Ptr<ActivationLayer>&) { return false; }
bool Layer::tryFuse(Ptr<Layer>&) { return false; }
void Layer::getScaleShift(Mat& scale, Mat& shift) const
{
scale = Mat();
shift = Mat();
}
void Layer::unsetAttached()
{
setActivation(Ptr<ActivationLayer>());
}
template <typename T>
static void vecToPVec(const std::vector<T> &v, std::vector<T*> &pv)
{
pv.resize(v.size());
for (size_t i = 0; i < v.size(); i++)
pv[i] = const_cast<T*>(&v[i]);
}
void Layer::finalize(const std::vector<Mat> &inputs, std::vector<Mat> &outputs)
{
CV_TRACE_FUNCTION();
this->finalize((InputArrayOfArrays)inputs, (OutputArrayOfArrays)outputs);
}
void Layer::finalize(const std::vector<Mat*> &input, std::vector<Mat> &output)
{
CV_UNUSED(input);CV_UNUSED(output);
}
void Layer::finalize(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr)
{
CV_TRACE_FUNCTION();
std::vector<Mat> inputs, outputs;
inputs_arr.getMatVector(inputs);
outputs_arr.getMatVector(outputs);
std::vector<Mat*> inputsp;
vecToPVec(inputs, inputsp);
this->finalize(inputsp, outputs);
}
std::vector<Mat> Layer::finalize(const std::vector<Mat> &inputs)
{
CV_TRACE_FUNCTION();
std::vector<Mat> outputs;
this->finalize(inputs, outputs);
return outputs;
}
void Layer::forward(std::vector<Mat*> &input, std::vector<Mat> &output, std::vector<Mat> &internals)
{
// We kept this method for compatibility. DNN calls it now only to support users' implementations.
}
void Layer::forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
Layer::forward_fallback(inputs_arr, outputs_arr, internals_arr);
}
void Layer::forward_fallback(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
if (preferableTarget == DNN_TARGET_OPENCL_FP16 && inputs_arr.depth() == CV_16S)
{
std::vector<UMat> inputs;
std::vector<UMat> outputs;
std::vector<UMat> internals;
std::vector<UMat> orig_inputs;
std::vector<UMat> orig_outputs;
std::vector<UMat> orig_internals;
inputs_arr.getUMatVector(orig_inputs);
outputs_arr.getUMatVector(orig_outputs);
internals_arr.getUMatVector(orig_internals);
inputs.resize(orig_inputs.size());
for (size_t i = 0; i < orig_inputs.size(); i++)
convertFp16(orig_inputs[i], inputs[i]);
outputs.resize(orig_outputs.size());
for (size_t i = 0; i < orig_outputs.size(); i++)
outputs[i].create(shape(orig_outputs[i]), CV_32F);
internals.resize(orig_internals.size());
for (size_t i = 0; i < orig_internals.size(); i++)
internals[i].create(shape(orig_internals[i]), CV_32F);
forward(inputs, outputs, internals);
for (size_t i = 0; i < outputs.size(); i++)
convertFp16(outputs[i], orig_outputs[i]);
// sync results back
outputs_arr.assign(orig_outputs);
internals_arr.assign(orig_internals);
return;
}
std::vector<Mat> inpvec;
std::vector<Mat> outputs;
std::vector<Mat> internals;
inputs_arr.getMatVector(inpvec);
outputs_arr.getMatVector(outputs);
internals_arr.getMatVector(internals);
std::vector<Mat*> inputs(inpvec.size());
for (int i = 0; i < inpvec.size(); i++)
inputs[i] = &inpvec[i];
this->forward(inputs, outputs, internals);
// sync results back
outputs_arr.assign(outputs);
internals_arr.assign(internals);
}
void Layer::run(const std::vector<Mat> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals)
{
CV_TRACE_FUNCTION();
this->finalize(inputs, outputs);
this->forward(inputs, outputs, internals);
}
Layer::~Layer() {}
bool Layer::getMemoryShapes(const std::vector<MatShape> &inputs,
const int requiredOutputs,
std::vector<MatShape> &outputs,
std::vector<MatShape> &internals) const
{
CV_Assert(inputs.size());
outputs.assign(std::max(requiredOutputs, (int)inputs.size()), inputs[0]);
return false;
}
//////////////////////////////////////////////////////////////////////////
static Mutex& getLayerFactoryMutex()
{
static Mutex* volatile instance = NULL;
if (instance == NULL)
{
cv::AutoLock lock(getInitializationMutex());
if (instance == NULL)
instance = new Mutex();
}
return *instance;
}
typedef std::map<String, std::vector<LayerFactory::Constructor> > LayerFactory_Impl;
static LayerFactory_Impl& getLayerFactoryImpl_()
{
static LayerFactory_Impl impl;
return impl;
}
static LayerFactory_Impl& getLayerFactoryImpl()
{
static LayerFactory_Impl* volatile instance = NULL;
if (instance == NULL)
{
cv::AutoLock lock(getLayerFactoryMutex());
if (instance == NULL)
{
instance = &getLayerFactoryImpl_();
initializeLayerFactory();
}
}
return *instance;
}
void LayerFactory::registerLayer(const String &type, Constructor constructor)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(type, "type", type.c_str());
cv::AutoLock lock(getLayerFactoryMutex());
String type_ = toLowerCase(type);
LayerFactory_Impl::iterator it = getLayerFactoryImpl().find(type_);
if (it != getLayerFactoryImpl().end())
{
if (it->second.back() == constructor)
CV_Error(cv::Error::StsBadArg, "Layer \"" + type_ + "\" already was registered");
it->second.push_back(constructor);
}
getLayerFactoryImpl().insert(std::make_pair(type_, std::vector<Constructor>(1, constructor)));
}
void LayerFactory::unregisterLayer(const String &type)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(type, "type", type.c_str());
cv::AutoLock lock(getLayerFactoryMutex());
String type_ = toLowerCase(type);
LayerFactory_Impl::iterator it = getLayerFactoryImpl().find(type_);
if (it != getLayerFactoryImpl().end())
{
if (it->second.size() > 1)
it->second.pop_back();
else
getLayerFactoryImpl().erase(it);
}
}
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_ = toLowerCase(type);
LayerFactory_Impl::const_iterator it = getLayerFactoryImpl().find(type_);
if (it != getLayerFactoryImpl().end())
{
CV_Assert(!it->second.empty());
return it->second.back()(params);
}
else
{
return Ptr<Layer>(); //NULL
}
}
BackendNode::BackendNode(int backendId) : backendId(backendId) {}
BackendNode::~BackendNode() {};
BackendWrapper::BackendWrapper(int backendId, int targetId)
: backendId(backendId), targetId(targetId) {}
BackendWrapper::BackendWrapper(int targetId, const cv::Mat& m)
{
CV_Error(Error::StsNotImplemented,
"Constructor of backend wrapper must be implemented");
}
BackendWrapper::BackendWrapper(const Ptr<BackendWrapper>& base, const MatShape& shape)
{
CV_Error(Error::StsNotImplemented,
"Constructor of backend wrapper must be implemented");
}
BackendWrapper::~BackendWrapper() {}
Net readNet(const String& _model, const String& _config, const String& _framework)
{
String framework = toLowerCase(_framework);
String model = _model;
String config = _config;
const std::string modelExt = model.substr(model.rfind('.') + 1);
const std::string configExt = config.substr(config.rfind('.') + 1);
if (framework == "caffe" || modelExt == "caffemodel" || configExt == "caffemodel" ||
modelExt == "prototxt" || configExt == "prototxt")
{
if (modelExt == "prototxt" || configExt == "caffemodel")
std::swap(model, config);
return readNetFromCaffe(config, model);
}
if (framework == "tensorflow" || modelExt == "pb" || configExt == "pb" ||
modelExt == "pbtxt" || configExt == "pbtxt")
{
if (modelExt == "pbtxt" || configExt == "pb")
std::swap(model, config);
return readNetFromTensorflow(model, config);
}
if (framework == "torch" || modelExt == "t7" || modelExt == "net" ||
configExt == "t7" || configExt == "net")
{
return readNetFromTorch(model.empty() ? config : model);
}
if (framework == "darknet" || modelExt == "weights" || configExt == "weights" ||
modelExt == "cfg" || configExt == "cfg")
{
if (modelExt == "cfg" || configExt == "weights")
std::swap(model, config);
return readNetFromDarknet(config, model);
}
if (framework == "dldt" || modelExt == "bin" || configExt == "bin" ||
modelExt == "xml" || configExt == "xml")
{
if (modelExt == "xml" || configExt == "bin")
std::swap(model, config);
return readNetFromModelOptimizer(config, model);
}
if (framework == "onnx" || modelExt == "onnx")
{
return readNetFromONNX(model);
}
CV_Error(Error::StsError, "Cannot determine an origin framework of files: " +
model + (config.empty() ? "" : ", " + config));
}
Net readNet(const String& _framework, const std::vector<uchar>& bufferModel,
const std::vector<uchar>& bufferConfig)
{
String framework = toLowerCase(_framework);
if (framework == "caffe")
return readNetFromCaffe(bufferConfig, bufferModel);
else if (framework == "tensorflow")
return readNetFromTensorflow(bufferModel, bufferConfig);
else if (framework == "darknet")
return readNetFromDarknet(bufferConfig, bufferModel);
else if (framework == "torch")
CV_Error(Error::StsNotImplemented, "Reading Torch models from buffers");
else if (framework == "dldt")
CV_Error(Error::StsNotImplemented, "Reading Intel's Model Optimizer models from buffers");
CV_Error(Error::StsError, "Cannot determine an origin framework with a name " + framework);
}
Net readNetFromModelOptimizer(const String &xml, const String &bin)
{
return Net::readFromModelOptimizer(xml, bin);
}
CV__DNN_INLINE_NS_END
}} // namespace
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