blank_layer.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.
// 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 "../op_cuda.hpp"
#include "../op_inf_engine.hpp"
#include "../ie_ngraph.hpp"
#ifdef HAVE_CUDA
#include "../cuda4dnn/primitives/reshape.hpp"
using namespace cv::dnn::cuda4dnn;
#endif
namespace cv
{
namespace dnn
{
class BlankLayerImpl CV_FINAL : public BlankLayer
{
public:
BlankLayerImpl(const LayerParams& params)
{
setParamsFrom(params);
}
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_CUDA ||
((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
{
Layer::getMemoryShapes(inputs, requiredOutputs, outputs, internals);
return true;
}
#ifdef HAVE_OPENCL
bool forward_ocl(InputArrayOfArrays inputs_, OutputArrayOfArrays outputs_, OutputArrayOfArrays internals_)
{
std::vector<UMat> inputs;
std::vector<UMat> outputs;
inputs_.getUMatVector(inputs);
outputs_.getUMatVector(outputs);
for (int i = 0, n = outputs.size(); i < n; ++i)
{
void *src_handle = inputs[i].handle(ACCESS_READ);
void *dst_handle = outputs[i].handle(ACCESS_WRITE);
if (src_handle != dst_handle)
inputs[i].copyTo(outputs[i]);
}
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),
forward_ocl(inputs_arr, outputs_arr, internals_arr))
std::vector<Mat> inputs, outputs;
inputs_arr.getMatVector(inputs);
outputs_arr.getMatVector(outputs);
for (int i = 0, n = outputs.size(); i < n; ++i)
if (outputs[i].data != inputs[i].data)
inputs[i].copyTo(outputs[i]);
}
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
{
InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
std::vector<size_t> dims = input->getDims();
CV_Assert(!dims.empty());
InferenceEngine::Builder::Layer ieLayer(name);
ieLayer.setName(name);
if (preferableTarget == DNN_TARGET_MYRIAD || preferableTarget == DNN_TARGET_HDDL)
{
ieLayer.setType("Copy");
}
else
{
ieLayer.setType("Split");
ieLayer.getParameters()["axis"] = dims.size() - 1;
ieLayer.getParameters()["out_sizes"] = dims[0];
}
ieLayer.setInputPorts({InferenceEngine::Port(dims)});
ieLayer.setOutputPorts(std::vector<InferenceEngine::Port>(1));
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#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;
ngraph::OutputVector inp{ieInpNode};
auto blank = std::make_shared<ngraph::op::Concat>(inp, 0);
return Ptr<BackendNode>(new InfEngineNgraphNode(blank));
}
#endif // HAVE_DNN_NGRAPH
#ifdef HAVE_CUDA
Ptr<BackendNode> initCUDA(
void *context_,
const std::vector<Ptr<BackendWrapper>>& inputs,
const std::vector<Ptr<BackendWrapper>>& outputs
) override
{
auto context = reinterpret_cast<csl::CSLContext*>(context_);
return make_cuda_node<cuda4dnn::ReshapeOp>(preferableTarget, std::move(context->stream));
}
#endif
virtual bool tryQuantize(const std::vector<std::vector<float> > &scales,
const std::vector<std::vector<int> > &zeropoints, LayerParams& params) CV_OVERRIDE
{
return true;
}
};
Ptr<Layer> BlankLayer::create(const LayerParams& params)
{
// In case of Caffe's Dropout layer from Faster-RCNN framework,
// https://github.com/rbgirshick/caffe-fast-rcnn/tree/faster-rcnn
// return Power layer.
if (!params.get<bool>("scale_train", true))
{
float scale = 1 - params.get<float>("dropout_ratio", 0.5f);
CV_Assert(scale > 0);
LayerParams powerParams;
powerParams.name = params.name;
powerParams.type = "Power";
powerParams.set("scale", scale);
return PowerLayer::create(powerParams);
}
else
return Ptr<BlankLayer>(new BlankLayerImpl(params));
}
}
}