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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
// (3-clause BSD License)
//
// Copyright (C) 2017, Intel Corporation, 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:
//
// * Redistributions of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistributions 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.
//
// * Neither the names of the copyright holders nor the names of the contributors
// may 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 copyright holders 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 <iostream>
#include <algorithm>
#include <vector>
#include <map>
#include "darknet_io.hpp"
namespace cv {
namespace dnn {
CV__DNN_EXPERIMENTAL_NS_BEGIN
namespace
{
class DarknetImporter : public Importer
{
darknet::NetParameter net;
public:
DarknetImporter() {}
DarknetImporter(const char *cfgFile, const char *darknetModel)
{
CV_TRACE_FUNCTION();
ReadNetParamsFromCfgFileOrDie(cfgFile, &net);
if (darknetModel && darknetModel[0])
ReadNetParamsFromBinaryFileOrDie(darknetModel, &net);
}
struct BlobNote
{
BlobNote(const std::string &_name, int _layerId, int _outNum) :
name(_name), layerId(_layerId), outNum(_outNum) {}
std::string name;
int layerId, outNum;
};
std::vector<BlobNote> addedBlobs;
std::map<String, int> layerCounter;
void populateNet(Net dstNet)
{
CV_TRACE_FUNCTION();
int layersSize = net.layer_size();
layerCounter.clear();
addedBlobs.clear();
addedBlobs.reserve(layersSize + 1);
//setup input layer names
{
std::vector<String> netInputs(net.input_size());
for (int inNum = 0; inNum < net.input_size(); inNum++)
{
addedBlobs.push_back(BlobNote(net.input(inNum), 0, inNum));
netInputs[inNum] = net.input(inNum);
}
dstNet.setInputsNames(netInputs);
}
for (int li = 0; li < layersSize; li++)
{
const darknet::LayerParameter &layer = net.layer(li);
String name = layer.name();
String type = layer.type();
LayerParams layerParams = layer.getLayerParams();
int repetitions = layerCounter[name]++;
if (repetitions)
name += cv::format("_%d", repetitions);
int id = dstNet.addLayer(name, type, layerParams);
// iterate many bottoms layers (for example for: route -1, -4)
for (int inNum = 0; inNum < layer.bottom_size(); inNum++)
addInput(layer.bottom(inNum), id, inNum, dstNet, layer.name());
for (int outNum = 0; outNum < layer.top_size(); outNum++)
addOutput(layer, id, outNum);
}
addedBlobs.clear();
}
void addOutput(const darknet::LayerParameter &layer, int layerId, int outNum)
{
const std::string &name = layer.top(outNum);
bool haveDups = false;
for (int idx = (int)addedBlobs.size() - 1; idx >= 0; idx--)
{
if (addedBlobs[idx].name == name)
{
haveDups = true;
break;
}
}
if (haveDups)
{
bool isInplace = layer.bottom_size() > outNum && layer.bottom(outNum) == name;
if (!isInplace)
CV_Error(Error::StsBadArg, "Duplicate blobs produced by multiple sources");
}
addedBlobs.push_back(BlobNote(name, layerId, outNum));
}
void addInput(const std::string &name, int layerId, int inNum, Net &dstNet, std::string nn)
{
int idx;
for (idx = (int)addedBlobs.size() - 1; idx >= 0; idx--)
{
if (addedBlobs[idx].name == name)
break;
}
if (idx < 0)
{
CV_Error(Error::StsObjectNotFound, "Can't find output blob \"" + name + "\"");
return;
}
dstNet.connect(addedBlobs[idx].layerId, addedBlobs[idx].outNum, layerId, inNum);
}
~DarknetImporter()
{
}
};
}
Net readNetFromDarknet(const String &cfgFile, const String &darknetModel /*= String()*/)
{
DarknetImporter darknetImporter(cfgFile.c_str(), darknetModel.c_str());
Net net;
darknetImporter.populateNet(net);
return net;
}
CV__DNN_EXPERIMENTAL_NS_END
}} // namespace