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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.
// 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:
//
// * 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 "layers_common.hpp"
#include "../op_inf_engine.hpp"
#include "../ie_ngraph.hpp"
#include <float.h>
#include <algorithm>
#include <opencv2/dnn/shape_utils.hpp>
namespace cv
{
namespace dnn
{
class FlattenLayerImpl CV_FINAL : public FlattenLayer
{
public:
FlattenLayerImpl(const LayerParams ¶ms)
{
_startAxis = params.get<int>("axis", 1);
_endAxis = params.get<int>("end_axis", -1);
setParamsFrom(params);
}
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV ||
((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && haveInfEngine());
}
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() > 0);
for (size_t i = 1; i < inputs.size(); i++)
{
CV_Assert(inputs[i] == inputs[0]);
}
int numAxes = inputs[0].size();
int startAxis = clamp(_startAxis, numAxes);
int endAxis = clamp(_endAxis, numAxes);
CV_Assert(startAxis >= 0);
CV_Assert(endAxis >= startAxis && endAxis < (int)numAxes);
size_t flattenedDimensionSize = total(inputs[0], startAxis, endAxis + 1);
MatShape outputShapeVec;
for (int i = 0; i < startAxis; i++)
{
outputShapeVec.push_back(inputs[0][i]);
}
outputShapeVec.push_back(flattenedDimensionSize);
for (size_t i = endAxis + 1; i < numAxes; i++)
{
outputShapeVec.push_back(inputs[0][i]);
}
CV_Assert(outputShapeVec.size() <= 4);
outputs.resize(inputs.size(), outputShapeVec);
return true;
}
void finalize(InputArrayOfArrays inputs_arr, OutputArrayOfArrays) CV_OVERRIDE
{
std::vector<Mat> inputs;
inputs_arr.getMatVector(inputs);
int numAxes = inputs[0].dims;
_startAxis = clamp(_startAxis, numAxes);
_endAxis = clamp(_endAxis, numAxes);
}
#ifdef HAVE_OPENCL
bool forward_ocl(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr)
{
std::vector<UMat> inpvec;
std::vector<UMat> outputs;
inputs_arr.getUMatVector(inpvec);
outputs_arr.getUMatVector(outputs);
std::vector<UMat*> inputs(inpvec.size());
for (int i = 0; i < inpvec.size(); i++)
inputs[i] = &inpvec[i];
for (size_t i = 0; i < inputs.size(); i++)
{
MatShape outShape = shape(outputs[i]);
UMat& output = outputs_arr.getUMatRef(i);
output = inputs[i]->reshape(1, (int)outShape.size(), &outShape[0]);
}
return true;
}
#endif
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) &&
outputs_arr.isUMatVector(),
forward_ocl(inputs_arr, outputs_arr, internals_arr))
std::vector<Mat> inputs, outputs;
inputs_arr.getMatVector(inputs);
outputs_arr.getMatVector(outputs);
for (size_t i = 0; i < inputs.size(); i++)
{
MatShape outShape = shape(outputs[i]);
if (inputs[i].data != outputs[i].data)
{
inputs[i].reshape(1, (int)outShape.size(), &outShape[0]).copyTo(outputs[i]);
}
}
}
#ifdef HAVE_INF_ENGINE
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
{
InferenceEngine::Builder::Layer ieLayer(name);
ieLayer.setName(name);
ieLayer.setType("Flatten");
ieLayer.getParameters()["axis"] = (size_t)_startAxis;
ieLayer.getParameters()["end_axis"] = _endAxis; // Do not cast to size_t because it might be negative.
ieLayer.setInputPorts(std::vector<InferenceEngine::Port>(1));
ieLayer.setOutputPorts(std::vector<InferenceEngine::Port>(1));
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
}
#endif // HAVE_INF_ENGINE
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
std::vector<size_t> dims = ieInpNode->get_shape();
int numAxes = dims.size();
int startAxis = clamp(_startAxis, numAxes);
int endAxis = clamp(_endAxis, numAxes);
CV_Assert(startAxis >= 0);
CV_Assert(endAxis >= startAxis && endAxis < numAxes);
int64_t flattenedDimensionSize = std::accumulate(dims.begin() + startAxis,
dims.begin() + endAxis + 1, 1, std::multiplies<size_t>());
std::vector<int64_t> outputShapeVec(dims.begin(), dims.begin() + startAxis);
outputShapeVec.push_back(flattenedDimensionSize);
outputShapeVec.insert(outputShapeVec.end(), dims.begin() + endAxis + 1, dims.end());
auto shape = std::make_shared<ngraph::op::Constant>(ngraph::element::i64,
ngraph::Shape({outputShapeVec.size()}), outputShapeVec.data());
auto reshape = std::make_shared<ngraph::op::v1::Reshape>(ieInpNode, shape, true);
return Ptr<BackendNode>(new InfEngineNgraphNode(reshape));
}
#endif // HAVE_DNN_NGRAPH
// HAVE_INF_ENGINE
int _startAxis;
int _endAxis;
};
Ptr<FlattenLayer> FlattenLayer::create(const LayerParams& params)
{
return Ptr<FlattenLayer>(new FlattenLayerImpl(params));
}
}
}