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522 lines
19 KiB
C++
522 lines
19 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
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// Copyright (C) 2017, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "../precomp.hpp"
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#include "layers_common.hpp"
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#include "../op_cuda.hpp"
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#include "../op_inf_engine.hpp"
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#include "../ie_ngraph.hpp"
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#include "../op_webnn.hpp"
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#include "../op_timvx.hpp"
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#include "../op_cann.hpp"
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#include <opencv2/dnn/shape_utils.hpp>
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#ifdef HAVE_CUDA
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#include "../cuda4dnn/primitives/reshape.hpp"
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using namespace cv::dnn::cuda4dnn;
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#endif
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namespace cv
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{
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namespace dnn
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{
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static void computeShapeByReshapeMask(const MatShape &srcShape,
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const MatShape &maskShape,
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Range srcRange /*= Range::all()*/,
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MatShape& dstShape)
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{
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int srcShapeSize = (int)srcShape.size();
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int maskShapeSize = (int)maskShape.size();
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srcRange = normalize_axis_range(srcRange, srcShapeSize);
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bool explicitMask = !maskShape.empty(); // All mask values are positive.
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for (int i = 0, n = maskShape.size(); i < n && explicitMask; ++i)
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{
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explicitMask = maskShape[i] > 0;
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}
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// Working range of source shape is a range where area(src) == area(mask).
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if (explicitMask)
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{
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int maskTotal = total(maskShape);
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// Go from the end of mask until we collect required total.
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bool matched = false;
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for (int i = srcRange.end - 1; i >= srcRange.start; --i)
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{
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if (matched)
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{
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if (total(srcShape, i, srcRange.end) != maskTotal)
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{
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srcRange.start = i + 1;
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break;
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}
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else if (i == 0)
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{
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srcRange.start = 0;
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break;
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}
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}
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else
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{
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matched = total(srcShape, i, srcRange.end) == maskTotal;
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}
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}
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while (total(srcShape, srcRange.start, srcRange.end) != maskTotal && srcRange.start > 0)
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{
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srcRange.start -= 1;
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}
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CV_Assert(total(srcShape, srcRange.start, srcRange.end) == maskTotal);
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}
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CV_Assert(0 <= srcRange.start && srcRange.start <= srcRange.end && srcRange.end <= srcShapeSize);
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int dstShapeSize = srcShapeSize - srcRange.size() + maskShapeSize;
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dstShape.resize(dstShapeSize);
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std::copy(srcShape.begin(), srcShape.begin() + srcRange.start, dstShape.begin());
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std::copy(srcShape.begin() + srcRange.end, srcShape.begin() + srcShapeSize, dstShape.begin() + srcRange.start + maskShapeSize);
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int inferDim = -1;
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for (int i = 0; i < maskShapeSize; i++)
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{
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if (maskShape[i] > 0)
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{
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dstShape[srcRange.start + i] = maskShape[i];
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}
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else if (maskShape[i] == 0)
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{
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if (srcRange.start + i >= srcShapeSize)
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CV_Error(Error::StsBadArg, format("Copy dim[%d] (which has zero size) is out of the source shape bounds", srcRange.start + i));
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dstShape[srcRange.start + i] = srcShape[srcRange.start + i];
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}
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else if (maskShape[i] == -1)
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{
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if (inferDim != -1)
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CV_Error(Error::StsAssert, "Duplicate of inferred dim (which is denoted by -1)");
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inferDim = srcRange.start + i;
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dstShape[inferDim] = 1;
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}
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else
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CV_Error(Error::StsBadArg, "maskShape[i] >= -1");
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}
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size_t srcTotal = total(srcShape);
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size_t dstTotal = total(dstShape);
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CV_Assert(dstTotal != 0);
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if (inferDim != -1)
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{
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if (srcTotal % dstTotal != 0)
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CV_Error(Error::StsBackTrace, "Can't infer a dim denoted by -1");
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dstShape[inferDim] = (int)(srcTotal / dstTotal);
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}
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else
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{
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CV_Assert(srcTotal == dstTotal);
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}
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}
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class ReshapeLayerImpl CV_FINAL : public ReshapeLayer
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{
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public:
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ReshapeLayerImpl(const LayerParams& params)
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{
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setParamsFrom(params);
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axis = params.get<int>("axis", 0);
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numAxes = params.get<int>("num_axes", -1);
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hasDynamicShapes = params.get<bool>("has_dynamic_shapes", false);
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shapesInitialized = !hasDynamicShapes;
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zeropoint = params.get<int>("zeropoints", 0);
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scale = params.get<float>("scales", 1.0f);
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CV_Assert(numAxes >= -1);
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newShapeRange = (numAxes == -1) ? Range(axis, INT_MAX) : Range(axis, axis + numAxes);
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newShapeDesc.clear();
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if (params.has("dim"))
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{
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const DictValue ¶mShape = params.get("dim");
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int i, dims = paramShape.size();
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newShapeDesc.resize(dims);
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for (i = 0; i < dims; i++)
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newShapeDesc[i] = paramShape.get<int>(i);
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}
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if (hasDynamicShapes)
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{
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dynamicShapes.clear();
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inputIndices.clear();
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if (params.has("dynamic_axes")) {
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CV_Assert(params.has("input_indices"));
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const DictValue &dynamicAxes = params.get("dynamic_axes");
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const DictValue &dynamicInputShapes = params.get("input_indices");
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int i, dims = dynamicAxes.size();
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CV_Assert(dims == dynamicInputShapes.size());
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CV_Assert(dims > 0);
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dynamicShapes.resize(dims);
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inputIndices.resize(dims);
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for (i = 0; i < dims; i++) {
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dynamicShapes[i] = dynamicAxes.get<int>(i);
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inputIndices[i] = dynamicInputShapes.get<int>(i);
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}
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}
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}
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}
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virtual bool supportBackend(int backendId) CV_OVERRIDE
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{
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if (backendId == DNN_BACKEND_TIMVX && haveTimVX())
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{
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int len = this->type.length();
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if (len <= 4)
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return false;
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if (this->type.substr(len - 4) == "Int8")
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return true;
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else
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return false;
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}
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#ifdef HAVE_INF_ENGINE
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if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
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return true;
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#endif
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return backendId == DNN_BACKEND_OPENCV ||
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backendId == DNN_BACKEND_CUDA ||
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backendId == DNN_BACKEND_WEBNN ||
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backendId == DNN_BACKEND_CANN;
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}
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bool getMemoryShapes(const std::vector<MatShape> &inputs,
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const int requiredOutputs,
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std::vector<MatShape> &outputs,
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std::vector<MatShape> &internals) const CV_OVERRIDE
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{
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if (inputs.size() == 1 || inputs.size() == requiredOutputs)
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{
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outputs.clear();
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for (size_t i = 0; i < inputs.size(); i++)
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{
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if (hasDynamicShapes && !shapesInitialized)
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{
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outputs.push_back(newShapeDesc);
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}
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else
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{
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outputs.push_back(MatShape());
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computeShapeByReshapeMask(inputs[i], newShapeDesc, newShapeRange, outputs.back());
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}
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}
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}
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else
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{
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CV_Assert_N(inputs.size() == 2, total(inputs[0]) == total(inputs[1]));
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outputs.assign(1, inputs[1]);
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}
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return true;
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}
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bool updateMemoryShapes(const std::vector<MatShape> &inputs) CV_OVERRIDE
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{
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if (hasDynamicShapes)
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{
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for (int i = 0; i < dynamicShapes.size(); ++i)
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{
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newShapeDesc[dynamicShapes[i]] = inputs[0][inputIndices[i]];
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}
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}
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shapesInitialized = true;
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return true;
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}
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void finalize(InputArrayOfArrays, OutputArrayOfArrays outputs_arr) CV_OVERRIDE
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{
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std::vector<Mat> outputs;
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outputs_arr.getMatVector(outputs);
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CV_Assert(!outputs.empty());
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outShapes.resize(outputs.size());
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for (int i = 0; i < outputs.size(); ++i)
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outShapes[i] = shape(outputs[i]);
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}
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bool forward_ocl(InputArrayOfArrays inps, OutputArrayOfArrays outs, OutputArrayOfArrays internals)
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{
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std::vector<UMat> inputs;
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std::vector<UMat> outputs;
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inps.getUMatVector(inputs);
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outs.getUMatVector(outputs);
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for (size_t i = 0; i < outputs.size(); i++)
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{
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UMat srcBlob = inputs[i];
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void *src_handle = inputs[i].handle(ACCESS_READ);
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void *dst_handle = outputs[i].handle(ACCESS_WRITE);
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if (src_handle != dst_handle)
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{
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UMat umat = srcBlob.reshape(1, (int)outShapes[i].size(), &outShapes[i][0]);
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umat.copyTo(outputs[i]);
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}
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}
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outs.assign(outputs);
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return true;
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}
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void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE
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{
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CV_TRACE_FUNCTION();
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CV_TRACE_ARG_VALUE(name, "name", name.c_str());
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CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget),
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forward_ocl(inputs_arr, outputs_arr, internals_arr))
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std::vector<Mat> inputs, outputs;
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inputs_arr.getMatVector(inputs);
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outputs_arr.getMatVector(outputs);
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for (size_t i = 0; i < outputs.size(); i++)
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{
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Mat srcBlob = inputs[i];
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if (outputs[i].data != srcBlob.data)
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srcBlob.reshape(1, shape(outputs[i])).copyTo(outputs[i]);
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}
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}
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#ifdef HAVE_CANN
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virtual Ptr<BackendNode> initCann(const std::vector<Ptr<BackendWrapper> > &inputs,
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const std::vector<Ptr<BackendWrapper> > &outputs,
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const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
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{
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auto x = inputs[0].dynamicCast<CannBackendWrapper>();
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// create operator
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auto op = std::make_shared<ge::op::Reshape>(name);
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// set attributes
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op->set_attr_axis(axis);
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op->set_attr_num_axes(numAxes);
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// set inputs
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// set inputs : x
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auto op_x = nodes[0].dynamicCast<CannBackendNode>()->getOp();
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op->set_input_x_by_name(*op_x, x->name.c_str());
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auto x_desc = x->getTensorDesc();
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op->update_input_desc_x(*x_desc);
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// set inputs : shape
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std::vector<int> shape_of_shape{(int)newShapeDesc.size()};
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Mat shape_mat(shape_of_shape, CV_32S, newShapeDesc.data());
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auto op_const_shape = std::make_shared<CannConstOp>(shape_mat.data, shape_mat.type(), shape_of_shape, cv::format("%s_shape", name.c_str()));
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op->set_input_shape(*(op_const_shape->getOp()));
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op->update_input_desc_shape(*(op_const_shape->getTensorDesc()));
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// set outputs
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auto output_y_desc = std::make_shared<ge::TensorDesc>(ge::Shape(), ge::FORMAT_NCHW, ge::DT_FLOAT);
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op->update_output_desc_y(*output_y_desc);
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return Ptr<BackendNode>(new CannBackendNode(op));
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}
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#endif // HAVE_CANN
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#ifdef HAVE_DNN_NGRAPH
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virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
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const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
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{
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CV_Assert(outShapes.size() == 1);
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auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
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std::vector<int64_t> out(outShapes[0].begin(), outShapes[0].end());
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auto shape = std::make_shared<ov::op::v0::Constant>(ov::element::i64,
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ov::Shape{out.size()}, out.data());
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auto reshape = std::make_shared<ov::op::v1::Reshape>(ieInpNode, shape, true);
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return Ptr<BackendNode>(new InfEngineNgraphNode(reshape));
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}
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#endif // HAVE_DNN_NGRAPH
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#ifdef HAVE_WEBNN
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virtual Ptr<BackendNode> initWebnn(const std::vector<Ptr<BackendWrapper> >& inputs, const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
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{
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Ptr<WebnnBackendNode> node = nodes[0].dynamicCast<WebnnBackendNode>();
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auto& webnnInpOperand = node->operand;
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auto& webnnGraphBuilder = node->net->builder;
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const std::vector<int32_t> out(outShapes[0].begin(), outShapes[0].end());
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auto operand = webnnGraphBuilder.Reshape(webnnInpOperand, out.data(), out.size());
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return Ptr<BackendNode>(new WebnnBackendNode(operand));
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}
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#endif
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#ifdef HAVE_CUDA
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Ptr<BackendNode> initCUDA(
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void *context_,
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const std::vector<Ptr<BackendWrapper>>& inputs,
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const std::vector<Ptr<BackendWrapper>>& outputs
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) override
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{
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auto context = reinterpret_cast<csl::CSLContext*>(context_);
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return make_cuda_node<cuda4dnn::ReshapeOp>(preferableTarget, std::move(context->stream));
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}
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#endif
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virtual Ptr<BackendNode> initTimVX(void* timVXInfo_,
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const std::vector<Ptr<BackendWrapper> > &inputsWrapper,
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const std::vector<Ptr<BackendWrapper> > &outputsWrapper,
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bool isLast) CV_OVERRIDE
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{
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#ifdef HAVE_TIMVX
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// tvGraph Initialization.
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auto timVxInfo = reinterpret_cast<TimVXInfo *>(timVXInfo_);
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CV_Assert(timVxInfo);
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Ptr<TimVXGraph> tvGraph = timVxInfo->getGraph();
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CV_Assert(tvGraph);
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Ptr<tim::vx::Graph> graph = tvGraph->graph;
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std::vector<int> inputsIndex, outputsIndex;
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int input_index = -1, output_index = -1;
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int reshapeNum = 0;
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Ptr<TimVXBackendWrapper> tmpWrapper, inputWrapper, outputWrapper;
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for (size_t i = 0; i < outputsWrapper.size(); i++)
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{
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tmpWrapper = inputsWrapper[i].dynamicCast<TimVXBackendWrapper>();
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Mat srcBlob = tmpWrapper->getMat();
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tmpWrapper = outputsWrapper[i].dynamicCast<TimVXBackendWrapper>();
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Mat dstBlob = tmpWrapper->getMat();
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if (dstBlob.data != srcBlob.data)
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{
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reshapeNum++;
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inputWrapper = inputsWrapper[i].dynamicCast<TimVXBackendWrapper>();
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outputWrapper = outputsWrapper[i].dynamicCast<TimVXBackendWrapper>();
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}
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}
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// Only work for single reshape Mat
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if (reshapeNum != 1)
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{
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return Ptr<BackendNode>();
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}
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// Input
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if (inputWrapper->isTensor())
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{
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input_index = tvGraph->getTensorIndex(inputWrapper->getTensor());
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if (input_index == -1)
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{
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// Copy To New inputWrapper
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Mat tmp = inputWrapper->getMat();
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inputWrapper = Ptr<TimVXBackendWrapper>(new TimVXBackendWrapper(tmp));
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}
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}
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if (!inputWrapper->isTensor() || input_index == -1)
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{
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Ptr<tim::vx::Quantization> tvInputQuant = Ptr<tim::vx::Quantization>(
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new tim::vx::Quantization(tim::vx::QuantType::ASYMMETRIC, scale, zeropoint));
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inputWrapper->createTensor(graph,tim::vx::TensorAttribute::INPUT,tvInputQuant);
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input_index = tvGraph->addWrapper(inputWrapper);
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}
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inputsIndex.push_back(input_index);
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//Output
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// Output Tensor has the same quantized attrib as Input Tesor.
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Ptr<tim::vx::Quantization> outputQuant = inputWrapper->getTensorQuantization();
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if (isLast)
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{
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auto shapeType = getShapeTypeFromMat(outputWrapper->getMat());
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// For Graph Output tensor, we need to set tensor shape before createTensor().
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outputWrapper->setTensorShape(shapeType);
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outputWrapper->createTensor(graph, tim::vx::TensorAttribute::OUTPUT, outputQuant);
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}
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else
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{
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outputWrapper->createTensor(graph, tim::vx::TensorAttribute::TRANSIENT, outputQuant);
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}
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output_index = tvGraph->addWrapper(outputWrapper);
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outputsIndex.push_back(output_index);
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// generate output shape.
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MatShape outputShape = shape(outputWrapper->getMat());
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// reverse shape, from NCHW to WHCN
|
|
std::reverse(outputShape.begin(), outputShape.end());
|
|
std::vector<uint32_t> tvShape(outputShape.begin(), outputShape.end());
|
|
|
|
std::shared_ptr<tim::vx::Operation> tvReshape = graph->CreateOperation<tim::vx::ops::Reshape>(tvShape);
|
|
|
|
Ptr<TimVXBackendNode> tvBackendNode = new TimVXBackendNode(tvGraph, tvReshape, inputsIndex, outputsIndex);
|
|
|
|
return tvBackendNode;
|
|
#endif // HAVE_TIMVX
|
|
return Ptr<BackendNode>();
|
|
}
|
|
|
|
virtual bool tryQuantize(const std::vector<std::vector<float> > &scales,
|
|
const std::vector<std::vector<int> > &zeropoints, LayerParams& params) CV_OVERRIDE
|
|
{
|
|
return true;
|
|
}
|
|
|
|
private:
|
|
int axis;
|
|
int numAxes;
|
|
std::vector<MatShape> outShapes;
|
|
std::vector<int> dynamicShapes; // Which axes shapes are dynamic and require reinitialization with new input
|
|
std::vector<int> inputIndices; // Which axes from input are needed to compute correct output shape
|
|
bool hasDynamicShapes;
|
|
bool shapesInitialized;
|
|
float scale;
|
|
int zeropoint;
|
|
};
|
|
|
|
Ptr<ReshapeLayer> ReshapeLayer::create(const LayerParams& params)
|
|
{
|
|
return Ptr<ReshapeLayer>(new ReshapeLayerImpl(params));
|
|
}
|
|
|
|
|
|
}
|
|
}
|