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PoolLayers.cpp
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PoolLayers.cpp
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//
// PoolLayers.cpp
// ConvNet
//
// Created by Márton Szemenyei on 2017. 09. 28..
// Copyright © 2017. Márton Szemenyei. All rights reserved.
//
#include "PoolLayers.h"
#include "Utils.h"
#include "BLAS.h"
#include <cfloat>
MaxPoolLayer::MaxPoolLayer(int32_t _h, int32_t _w, int32_t _outCh, Tuple _size, Tuple _stride, ACTIVATION _activation)
{
// Setup parameters
type = MAXPOOL;
inH = _h;
inW = _w;
inCh = _outCh;
stride = _stride;
size = _size;
outW = inW/stride.x;
outH = inH/stride.y;
outCh = _outCh;
outputs = new float [outH*outW*outCh];
activation = _activation;
cropRows = 0;
}
MaxPoolLayer::~MaxPoolLayer()
{
delete [] outputs;
}
void MaxPoolLayer::forward()
{
if (inputs)
{
int32_t currInH = inH - cropRows;
int32_t currOutH = outH - getNextCropRows();
int32_t w_offset = 0;
int32_t h_offset = 0;
// Go through all output elements
for(int32_t k = 0; k < outCh; ++k){
for(int32_t i = 0; i < currOutH; ++i){
for(int32_t j = 0; j < outW; ++j){
// Get the max value amongts the input pixels
int32_t out_index = j + outW*(i + currOutH*k);
float max = -FLT_MAX;
for(int32_t n = 0; n < size.y; ++n){
for(int32_t m = 0; m < size.x; ++m){
int32_t cur_h = h_offset + i*stride.y + n;
int32_t cur_w = w_offset + j*stride.x + m;
int32_t index = cur_w + inW*(cur_h + currInH*k);
bool valid = (cur_h < currInH && cur_w < inW);
float val = valid ? inputs[index] : -FLT_MAX;
max = (val > max) ? val : max;
}
}
outputs[out_index] = max;
}
}
}
// Activate output
activate(outputs, currOutH*outW*outCh, activation);
}
}
void MaxPoolLayer::print()
{
#ifndef NN_SILENT
// Print layer parameters aligned
std::cout << "Max Pooling:" << std::setw(8) << "(" << std::setw(3) << inCh << " x " << std::setw(3) << inW << " x " << std::setw(3) << inH << ")->(" << std::setw(3) << outCh << " x " << std::setw(3) << outW << " x " << std::setw(3) << outH << ") Size: (" << size.x << "x" << size.y << ")" << std::setw(8) << " -> " << act2string(activation) << std::endl;
#endif
}
AvgPoolLayer::AvgPoolLayer(int32_t _h, int32_t _w, int32_t _outCh, Tuple _size, Tuple _stride, ACTIVATION _activation)
{
// Setup parameters
type = AVGPOOL;
inH = _h;
inW = _w;
inCh = _outCh;
stride = _stride;
size = _size;
outW = inW/stride.x;
outH = inH/stride.y;
outCh = _outCh;
outputs = new float [outH*outW*outCh];
activation = _activation;
cropRows = 0;
}
AvgPoolLayer::~AvgPoolLayer()
{
delete [] outputs;
}
void AvgPoolLayer::forward()
{
if (inputs)
{
int32_t currInH = inH - cropRows;
int32_t currOutH = outH - getNextCropRows();
int32_t w_offset = 0;
int32_t h_offset = 0;
// Compute averaging denominator
float denum = 1.f/(size.x*size.y);
// Fill output with 0
fill(getN(), 0.f, outputs);
// Go through all output elements
for(int32_t k = 0; k < outCh; ++k){
for(int32_t i = 0; i < currOutH; ++i){
for(int32_t j = 0; j < outW; ++j){
// Sum input elements
int32_t out_index = j + outW*(i + currOutH*k);
for(int32_t n = 0; n < size.y; ++n){
for(int32_t m = 0; m < size.x; ++m){
int32_t cur_h = h_offset + i*stride.y + n;
int32_t cur_w = w_offset + j*stride.x + m;
int32_t index = cur_w + inW*(cur_h + currInH*k);
if (cur_h < currInH && cur_w < inW)
outputs[out_index] += inputs[index];
}
}
// Get average
outputs[out_index] *= denum;
}
}
}
// Activate output
activate(outputs, currOutH*outW*outCh, activation);
}
}
void AvgPoolLayer::print()
{
#ifndef NN_SILENT
// Print layer parameters aligned
std::cout << "Avg Pooling:" << std::setw(8) << "(" << std::setw(3) << inCh << " x " << std::setw(3) << inW << " x " << std::setw(3) << inH << ")->(" << std::setw(3) << outCh << " x " << std::setw(3) << outW << " x " << std::setw(3) << outH << ") Size: (" << size.x << "x" << size.y << ")" << std::setw(8) << " -> " << act2string(activation) << std::endl;
#endif
}