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/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions 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.
* * Neither the name of NVIDIA CORPORATION nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``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 COPYRIGHT OWNER 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.
*/
#include <assert.h>
#include "convolutionFFT2D_common.h"
////////////////////////////////////////////////////////////////////////////////
// Reference straightforward CPU convolution
////////////////////////////////////////////////////////////////////////////////
extern "C" void convolutionClampToBorderCPU(float *h_Result, float *h_Data,
float *h_Kernel, int dataH,
int dataW, int kernelH, int kernelW,
int kernelY, int kernelX) {
for (int y = 0; y < dataH; y++)
for (int x = 0; x < dataW; x++) {
double sum = 0;
for (int ky = -(kernelH - kernelY - 1); ky <= kernelY; ky++)
for (int kx = -(kernelW - kernelX - 1); kx <= kernelX; kx++) {
int dy = y + ky;
int dx = x + kx;
if (dy < 0) dy = 0;
if (dx < 0) dx = 0;
if (dy >= dataH) dy = dataH - 1;
if (dx >= dataW) dx = dataW - 1;
sum += h_Data[dy * dataW + dx] *
h_Kernel[(kernelY - ky) * kernelW + (kernelX - kx)];
}
h_Result[y * dataW + x] = (float)sum;
}
}
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