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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 <opencv2/dnn/shape_utils.hpp>
#include "opencl_kernels_dnn.hpp"
namespace cv
{
namespace dnn
{
class SliceLayerImpl : public SliceLayer
{
public:
SliceLayerImpl(const LayerParams& params)
{
setParamsFrom(params);
axis = params.get<int>("axis", 1);
if (params.has("slice_point"))
{
CV_Assert(!params.has("begin") && !params.has("size") && !params.has("end"));
const DictValue &indicesValue = params.get("slice_point");
sliceRanges.resize(indicesValue.size() + 1,
std::vector<Range>(axis + 1, Range::all()));
int prevSlice = 0;
for (int i = 0; i < indicesValue.size(); ++i)
{
sliceRanges[i][axis].start = prevSlice;
sliceRanges[i][axis].end = indicesValue.get<int>(i);
prevSlice = sliceRanges[i][axis].end;
}
sliceRanges.back()[axis].start = prevSlice;
}
else if (params.has("begin"))
{
CV_Assert(params.has("size") ^ params.has("end"));
const DictValue &begins = params.get("begin");
const DictValue &sizesOrEnds = params.has("size") ? params.get("size") : params.get("end");
CV_Assert(begins.size() == sizesOrEnds.size());
sliceRanges.resize(1);
sliceRanges[0].resize(begins.size(), Range::all());
for (int i = 0; i < begins.size(); ++i)
{
int start = begins.get<int>(i);
int sizeOrEnd = sizesOrEnds.get<int>(i); // It may be negative to reverse indexation.
CV_Assert(start >= 0);
sliceRanges[0][i].start = start;
if (params.has("size"))
{
int size = sizeOrEnd;
CV_Assert(size == -1 || size > 0); // -1 value means range [start, axis_size).
sliceRanges[0][i].end = size > 0 ? (start + size) : -1; // We'll finalize a negative value later.
}
else
{
int end = sizeOrEnd;
CV_Assert(end < 0 || end > start); // End index is excluded.
sliceRanges[0][i].end = end; // We'll finalize a negative value later.
}
}
}
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
const int requiredOutputs,
std::vector<MatShape> &outputs,
std::vector<MatShape> &internals) const
{
CV_Assert(inputs.size() == 1);
MatShape inpShape = inputs[0];
if (!sliceRanges.empty())
{
outputs.resize(sliceRanges.size(), inpShape);
for (int i = 0; i < outputs.size(); ++i)
{
CV_Assert(sliceRanges[i].size() <= inpShape.size());
for (int j = 0; j < sliceRanges[i].size(); ++j)
{
outputs[i][j] = clamp(sliceRanges[i][j], inpShape[j]).size();
}
}
}
else // Divide input blob on equal parts by axis.
{
CV_Assert(0 <= axis && axis < inpShape.size());
CV_Assert(requiredOutputs > 0 && inpShape[axis] % requiredOutputs == 0);
inpShape[axis] /= requiredOutputs;
outputs.resize(requiredOutputs, inpShape);
}
return false;
}
void finalize(const std::vector<Mat*> &inputs, std::vector<Mat> &outputs)
{
CV_Assert(inputs.size() == 1);
const MatSize& inpShape = inputs[0]->size;
if (sliceRanges.empty())
{
// Divide input blob on equal parts by axis.
int outAxisSize = inpShape[axis] / outputs.size();
sliceRanges.resize(outputs.size(),
std::vector<Range>(axis + 1, Range::all()));
int prevSlice = 0;
for (int i = 0; i < outputs.size(); ++i)
{
sliceRanges[i][axis].start = prevSlice;
sliceRanges[i][axis].end = sliceRanges[i][axis].start + outAxisSize;
prevSlice = sliceRanges[i][axis].end;
}
}
else
CV_Assert(outputs.size() == sliceRanges.size());
for (int i = 0; i < outputs.size(); ++i)
{
CV_Assert(sliceRanges[i].size() <= inpShape[-1]);
// Clamp.
for (int j = 0; j < sliceRanges[i].size(); ++j)
{
sliceRanges[i][j] = clamp(sliceRanges[i][j], inpShape[j]);
}
// Fill the rest of ranges.
for (int j = sliceRanges[i].size(); j < inpShape[-1]; ++j)
{
sliceRanges[i].push_back(Range::all());
}
}
}
#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);
if (inputs[0].dims < 4)
return false;
const UMat& inpMat = inputs[0];
for (size_t i = 0; i < outputs.size(); i++)
{
int groups = outputs[i].size[0];
int channels = outputs[i].size[1];
int rows = outputs[i].size[2];
int cols = outputs[i].size[3];
int number = (cols % 8 == 0) ? 8 : ((cols % 4 == 0) ? 4 : 1);
String buildopt = format("-DNUM=%d ", number);
String kname = format("slice%d", number);
ocl::Kernel kernel(kname.c_str(), ocl::dnn::slice_oclsrc, buildopt);
size_t global[] = { (size_t)groups * channels, (size_t)rows * cols / number };
int idx = 0;
kernel.set(idx++, ocl::KernelArg::PtrReadOnly(inpMat));
kernel.set(idx++, (int)(inpMat.size[2] * inpMat.size[3]));
kernel.set(idx++, (int)inpMat.size[3]);
kernel.set(idx++, (int)global[0]);
kernel.set(idx++, (int)(rows * cols));
kernel.set(idx++, (int)cols);
kernel.set(idx++, (int)sliceRanges[i][2].start);
kernel.set(idx++, (int)sliceRanges[i][3].start);
kernel.set(idx++, ocl::KernelArg::PtrWriteOnly(outputs[i]));
bool ret = kernel.run(2, global, NULL, false);
if (!ret)
return false;
}
return true;
}
#endif
void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
CV_OCL_RUN((preferableTarget == DNN_TARGET_OPENCL) &&
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_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
const Mat& inpMat = *inputs[0];
CV_Assert(outputs.size() == sliceRanges.size());
for (size_t i = 0; i < outputs.size(); i++)
{
inpMat(sliceRanges[i]).copyTo(outputs[i]);
}
}
};
Ptr<SliceLayer> SliceLayer::create(const LayerParams& params)
{
return Ptr<SliceLayer>(new SliceLayerImpl(params));
}
}
}