shuffle_channel_layer.cpp
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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
// Copyright (C) 2018, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
#include "../precomp.hpp"
#include "../op_cuda.hpp"
#ifdef HAVE_CUDA
#include "../cuda4dnn/primitives/shuffle_channel.hpp"
using namespace cv::dnn::cuda4dnn;
#endif
namespace cv { namespace dnn {
class ShuffleChannelLayerImpl CV_FINAL : public ShuffleChannelLayer
{
public:
ShuffleChannelLayerImpl(const LayerParams& params)
{
group = params.get<int>("group", 1);
setParamsFrom(params);
}
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_CUDA;
}
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() == 1 && inputs[0].size() == 4);
CV_Assert(inputs[0][1] % group == 0);
Layer::getMemoryShapes(inputs, requiredOutputs, outputs, internals);
return group == 1;
}
virtual void finalize(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr) CV_OVERRIDE
{
if (group != 1)
{
std::vector<Mat> inputs, outputs;
inputs_arr.getMatVector(inputs);
outputs_arr.getMatVector(outputs);
LayerParams lp;
float order[] = {0, 2, 1, 3};
lp.set("order", DictValue::arrayInt(&order[0], 4));
permute = PermuteLayer::create(lp);
const Mat& inp = inputs[0];
const Mat& out = outputs[0];
permuteInpShape.resize(4);
permuteInpShape[0] = inp.size[0];
permuteInpShape[1] = group;
permuteInpShape[2] = inp.size[1] / group;
permuteInpShape[3] = inp.size[2]*inp.size[3];
permuteOutShape.resize(4);
permuteOutShape[0] = permuteInpShape[0];
permuteOutShape[1] = permuteInpShape[2];
permuteOutShape[2] = permuteInpShape[1];
permuteOutShape[3] = permuteInpShape[3];
std::vector<Mat> permuteInputs(1, inp.reshape(1, permuteInpShape));
std::vector<Mat> permuteOutputs(1, out.reshape(1, permuteOutShape));
permute->finalize(permuteInputs, permuteOutputs);
}
}
#ifdef HAVE_OPENCL
bool forward_ocl(InputArrayOfArrays inps, OutputArrayOfArrays outs, OutputArrayOfArrays internals)
{
std::vector<UMat> inputs;
std::vector<UMat> outputs;
inps.getUMatVector(inputs);
outs.getUMatVector(outputs);
if (inputs[0].u != outputs[0].u)
{
if (!permute.empty())
{
inputs[0] = inputs[0].reshape(1, permuteInpShape.size(), &permuteInpShape[0]);
outputs[0] = outputs[0].reshape(1, permuteOutShape.size(), &permuteOutShape[0]);
permute->preferableTarget = preferableTarget;
permute->forward(inputs, outputs, internals);
}
else
inputs[0].copyTo(outputs[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),
forward_ocl(inputs_arr, outputs_arr, internals_arr))
if (inputs_arr.depth() == CV_16S)
{
forward_fallback(inputs_arr, outputs_arr, internals_arr);
return;
}
std::vector<Mat> inputs, outputs, internals;
inputs_arr.getMatVector(inputs);
outputs_arr.getMatVector(outputs);
internals_arr.getMatVector(internals);
Mat inp = inputs[0];
Mat out = outputs[0];
if (inp.data != out.data)
{
if (!permute.empty())
{
inp = inp.reshape(1, permuteInpShape);
out = out.reshape(1, permuteOutShape);
std::vector<Mat> permuteInputs(1, inp);
std::vector<Mat> permuteOutputs(1, out);
permute->forward(permuteInputs, permuteOutputs, internals);
}
else
inp.copyTo(out);
}
}
#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::ShuffleChannelOp>(preferableTarget, std::move(context->stream), group);
}
#endif
virtual bool tryQuantize(const std::vector<std::vector<float> > &scales,
const std::vector<std::vector<int> > &zeropoints, LayerParams& params) CV_OVERRIDE
{
return true;
}
private:
Ptr<PermuteLayer> permute;
std::vector<int> permuteInpShape, permuteOutShape;
};
Ptr<Layer> ShuffleChannelLayer::create(const LayerParams& params)
{
return Ptr<Layer>(new ShuffleChannelLayerImpl(params));
}
} // namespace dnn
} // namespace cv