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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 <opencv2/dnn/shape_utils.hpp>
#include <opencv2/dnn/all_layers.hpp>
#include <iostream>
#ifdef HAVE_OPENCL
#include "opencl_kernels_dnn.hpp"
#endif
namespace cv
{
namespace dnn
{
class ReorgLayerImpl CV_FINAL : public ReorgLayer
{
int reorgStride;
public:
ReorgLayerImpl(const LayerParams& params)
{
setParamsFrom(params);
reorgStride = params.get<int>("reorg_stride", 2);
CV_Assert(reorgStride > 0);
}
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);
outputs = std::vector<MatShape>(inputs.size(), shape(
inputs[0][0],
inputs[0][1] * reorgStride * reorgStride,
inputs[0][2] / reorgStride,
inputs[0][3] / reorgStride));
CV_Assert(outputs[0][0] > 0 && outputs[0][1] > 0 && outputs[0][2] > 0 && outputs[0][3] > 0);
CV_Assert(total(outputs[0]) == total(inputs[0]));
return false;
}
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_DEFAULT;
}
#ifdef HAVE_OPENCL
bool forward_ocl(InputArrayOfArrays inps, OutputArrayOfArrays outs, OutputArrayOfArrays internals)
{
std::vector<UMat> inputs;
std::vector<UMat> outputs;
bool use_half = (inps.depth() == CV_16S);
inps.getUMatVector(inputs);
outs.getUMatVector(outputs);
String buildopt= format("-DDtype=%s ", use_half ? "half" : "float");
for (size_t i = 0; i < inputs.size(); i++)
{
ocl::Kernel kernel("reorg", ocl::dnn::reorg_oclsrc, buildopt);
if (kernel.empty())
return false;
UMat& srcBlob = inputs[i];
UMat& dstBlob = outputs[0];
int channels = srcBlob.size[1];
int height = srcBlob.size[2];
int width = srcBlob.size[3];
size_t nthreads = channels * height * width;
kernel.set(0, (int)nthreads);
kernel.set(1, ocl::KernelArg::PtrReadOnly(srcBlob));
kernel.set(2, (int)channels);
kernel.set(3, (int)height);
kernel.set(4, (int)width);
kernel.set(5, (int)reorgStride);
kernel.set(6, ocl::KernelArg::PtrWriteOnly(dstBlob));
if (!kernel.run(1, &nthreads, NULL, false))
return false;
}
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) &&
OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
forward_ocl(inputs_arr, outputs_arr, internals_arr))
Layer::forward_fallback(inputs_arr, outputs_arr, internals_arr);
}
void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals) CV_OVERRIDE
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
for (size_t i = 0; i < inputs.size(); i++)
{
Mat srcBlob = *inputs[i];
MatShape inputShape = shape(srcBlob), outShape = shape(outputs[i]);
float *dstData = outputs[0].ptr<float>();
const float *srcData = srcBlob.ptr<float>();
int channels = inputShape[1], height = inputShape[2], width = inputShape[3];
int out_c = channels / (reorgStride*reorgStride);
for (int k = 0; k < channels; ++k) {
for (int j = 0; j < height; ++j) {
for (int i = 0; i < width; ++i) {
int out_index = i + width*(j + height*k);
int c2 = k % out_c;
int offset = k / out_c;
int w2 = i*reorgStride + offset % reorgStride;
int h2 = j*reorgStride + offset / reorgStride;
int in_index = w2 + width*reorgStride*(h2 + height*reorgStride*c2);
dstData[out_index] = srcData[in_index];
}
}
}
}
}
virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
const std::vector<MatShape> &outputs) const CV_OVERRIDE
{
(void)outputs; // suppress unused variable warning
int64 flops = 0;
for(int i = 0; i < inputs.size(); i++)
{
flops += 21*total(inputs[i]);
}
return flops;
}
};
Ptr<ReorgLayer> ReorgLayer::create(const LayerParams& params)
{
return Ptr<ReorgLayer>(new ReorgLayerImpl(params));
}
} // namespace dnn
} // namespace cv