-
Notifications
You must be signed in to change notification settings - Fork 24
/
Convolution.cpp
138 lines (122 loc) · 3.4 KB
/
Convolution.cpp
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
/*
==============================================================================
Convolution.cpp
Created: 3 Jan 2019 10:58:34am
Author: Damskägg Eero-Pekka
==============================================================================
*/
#include "Convolution.h"
Convolution::Convolution(size_t inputChannels, size_t outputChannels, int filterWidth, int dilation) :
bias(outputChannels),
outVec(outputChannels),
pos(0),
dilation(dilation),
inputChannels(inputChannels),
outputChannels(outputChannels),
filterWidth(filterWidth)
{
resetFifo();
resetKernel();
}
void Convolution::resetKernel()
{
kernel.clear();
kernel.reserve(filterWidth);
for (int i = 0; i < filterWidth; ++i)
{
Eigen::MatrixXf x(inputChannels, outputChannels);
x.setZero();
kernel.push_back(x);
}
bias = Eigen::RowVectorXf (outputChannels);
bias.setZero();
}
void Convolution::resetFifo()
{
memory.clear();
memory.reserve(getFilterOrder());
for (int i = 0; i < getFilterOrder(); ++i)
{
Eigen::RowVectorXf x(inputChannels);
x.setZero();
memory.push_back(x);
}
pos = 0;
}
void Convolution::setParams(size_t newInputChannels, size_t newOutputChannels,
int newFilterWidth, int newDilation)
{
inputChannels = newInputChannels;
outputChannels = newOutputChannels;
filterWidth = newFilterWidth;
dilation = newDilation;
outVec = Eigen::RowVectorXf (outputChannels);
resetFifo();
resetKernel();
}
int Convolution::getFilterOrder() const
{
return (filterWidth-1)*dilation + 1;
}
void Convolution::process(float* data, int numSamples)
{
for (int i = 0; i < numSamples; ++i)
{
processSingleSample(data, i , numSamples);
}
}
void Convolution::processSingleSample(float* data, int i, int numSamples)
{
if (memory.size() != getFilterOrder())
resetFifo();
auto fifo = memory.begin();
for (int ch = 0; ch < inputChannels; ++ch)
(*(fifo+pos))[ch] = data[idx(ch, i, numSamples)];
outVec.setZero();
std::vector<Eigen::MatrixXf>::iterator it;
int j = 0;
for (auto it = kernel.begin(); it != kernel.end(); it++)
{
int readPos = mod((pos - j * dilation), getFilterOrder());
outVec = outVec + *(fifo+readPos) * (*it);
j += 1;
}
outVec = outVec + bias;
for (int ch = 0; ch < outputChannels; ++ch)
data[idx(ch, i, numSamples)] = outVec[ch];
pos = mod(pos + 1, getFilterOrder());
}
int Convolution::mod(int a, int b)
{
int r = a % b;
return r < 0 ? r + b : r;
}
int Convolution::idx(int ch, int i, int numSamples)
{
return ch * numSamples + i;
}
void Convolution::setWeight(std::vector<float> W, std::string name)
{
if (name == "W")
setKernel(W);
else if (name == "b")
setBias(W);
}
void Convolution::setKernel(std::vector<float> W)
{
assert(W.size() == inputChannels*outputChannels*filterWidth);
size_t i = 0;
for (size_t k = 0; k < filterWidth; ++k)
for(size_t row = 0; row < inputChannels; ++row)
for (size_t col = 0; col < outputChannels; ++col)
{
kernel[filterWidth-1-k](row,col) = W[i];
i += 1;
}
}
void Convolution::setBias(std::vector<float> W)
{
assert(W.size() == outputChannels);
for (size_t i = 0; i < outputChannels; ++i)
bias(i) = W[i];
}