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upsample.hpp
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upsample.hpp
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/******************************************************************************
* Copyright (c) 2019, Xilinx, Inc.
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
*
* 1. Redistributions of source code must retain the above copyright notice,
* this list of conditions and the following disclaimer.
*
* 2. 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.
*
* 3. Neither the name of the copyright holder 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 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 COPYRIGHT HOLDER 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.
*
******************************************************************************/
/******************************************************************************
*
* Authors: Giulio Gambardella <[email protected]>
* erling on 5/10/21.
*
*
* Library of templated HLS functions for QNN deployment.
* Targeting upsampling layers
*
******************************************************************************/
#ifndef UPSAMPLE_HPP
#define UPSAMPLE_HPP
/**
* \brief Upsampling with the Nearest Neighbour algorithm. Works with square feature maps
*
* \tparam OFMDim Size of the output feature map
* \tparam IFMDim Size of the input feature map
* \tparam NumChannels Amount of channels of the input feature map
* \tparam In_t Input datatype
*
* \param in Input stream
* \param out Output stream
*/
template<unsigned int OFMDim,
unsigned int IFMDim,
unsigned int NumChannels,
typename In_t>
void UpsampleNearestNeighbour(
stream<ap_uint<NumChannels * In_t::width>> & in,
stream<ap_uint<NumChannels * In_t::width>> & out
) {
CASSERT_DATAFLOW(OFMDim > IFMDim);
constexpr unsigned int scale_factor = OFMDim/IFMDim;
constexpr unsigned int Padding = OFMDim % IFMDim;
// Padding might be asymmetrical
constexpr unsigned int PaddingDown = Padding/2;
constexpr unsigned int PaddingUp = Padding - PaddingDown;
// Padding might be asymmetrical
constexpr unsigned int PaddingRight = Padding/2;
constexpr unsigned int PaddingLeft = Padding - PaddingRight;
ap_uint<NumChannels * In_t::width> outData, inData;
ap_uint<NumChannels * In_t::width> RowBuf[IFMDim];
int count_row = -PaddingUp; // Counter used to understand whether reading (and buffering) a row or not - Made in order to avoid modulo operations
for (unsigned int y = 0; y < OFMDim; y++) {
for (unsigned int x = 0; x < OFMDim; x++) {
#pragma HLS PIPELINE II=1
bool read_row = (y ==0) || count_row==scale_factor;
if ((x < IFMDim) && read_row)
{
inData = in.read();
RowBuf[x] = inData;
}
// Padding Cols
if(x < PaddingLeft){
outData = RowBuf[0];
}
else if (x >= (OFMDim - PaddingRight)){
outData = RowBuf[IFMDim-1];
}
// Padding Rows
else if(y < PaddingUp || y >= (OFMDim - PaddingDown)){
outData = RowBuf[(x-PaddingLeft)/scale_factor];
}
// No Padding
else{
outData = RowBuf[(x-PaddingLeft)/scale_factor];
}
//std::cout << outData << " " ;
out.write(outData);
}// end for y
//std::cout << std::endl;
count_row++;
if (count_row > scale_factor)
count_row =1;
} // end for x
}
/**
* \brief Upsampling with the Nearest Neighbour algorithm. Works with square feature maps on multiple images
*
* \tparam OFMDim Size of the output feature map
* \tparam IFMDim Size of the input feature map
* \tparam NumChannels Amount of channels of the input feature map
* \tparam In_t Input datatype
*
* \param in Input stream
* \param out Output stream
* \param numReps Number of time the function has to be repeatedly executed (e.g. number of images)
*/
template<unsigned int OFMDim,
unsigned int IFMDim,
unsigned int NumChannels,
typename In_t>
void UpsampleNearestNeighbour_Batch(
stream<ap_uint<NumChannels * In_t::width>> & in,
stream<ap_uint<NumChannels * In_t::width>> & out,
unsigned int numReps) {
for (unsigned int rep = 0; rep < numReps; rep++) {
UpsampleNearestNeighbour<OFMDim, IFMDim, NumChannels, In_t>(in, out);
}
}
#endif