2022-01-13 14:05:24 +08:00
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/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
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2021-10-21 19:04:49 +08:00
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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* * Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above copyright
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* notice, this list of conditions and the following disclaimer in the
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* documentation and/or other materials provided with the distribution.
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* * Neither the name of NVIDIA CORPORATION nor the names of its
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* contributors may be used to endorse or promote products derived
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* from this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
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* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
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* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
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* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
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* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
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* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*/
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#include <assert.h>
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#include "convolutionFFT2D_common.h"
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////////////////////////////////////////////////////////////////////////////////
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// Reference straightforward CPU convolution
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////////////////////////////////////////////////////////////////////////////////
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extern "C" void convolutionClampToBorderCPU(float *h_Result, float *h_Data,
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float *h_Kernel, int dataH,
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int dataW, int kernelH, int kernelW,
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int kernelY, int kernelX) {
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for (int y = 0; y < dataH; y++)
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for (int x = 0; x < dataW; x++) {
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double sum = 0;
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for (int ky = -(kernelH - kernelY - 1); ky <= kernelY; ky++)
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for (int kx = -(kernelW - kernelX - 1); kx <= kernelX; kx++) {
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int dy = y + ky;
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int dx = x + kx;
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if (dy < 0) dy = 0;
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if (dx < 0) dx = 0;
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if (dy >= dataH) dy = dataH - 1;
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if (dx >= dataW) dx = dataW - 1;
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sum += h_Data[dy * dataW + dx] *
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h_Kernel[(kernelY - ky) * kernelW + (kernelX - kx)];
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}
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h_Result[y * dataW + x] = (float)sum;
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}
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}
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