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
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
|
local SpatialConvolutionMap, parent = torch.class('nn.SpatialConvolutionMap', 'nn.Module')
nn.tables = nn.tables or {}
function nn.tables.full(nin, nout)
local ft = torch.Tensor(nin*nout,2)
local p = 1
for j=1,nout do
for i=1,nin do
ft[p][1] = i
ft[p][2] = j
p = p + 1
end
end
return ft
end
function nn.tables.oneToOne(nfeat)
local ft = torch.Tensor(nfeat,2)
for i=1,nfeat do
ft[i][1] = i
ft[i][2] = i
end
return ft
end
function nn.tables.random(nin, nout, nto)
local nker = nto * nout
local tbl = torch.Tensor(nker, 2)
local fi = torch.randperm(nin)
local frcntr = 1
local nfi = math.floor(nin/nto) -- number of distinct nto chunks
local totbl = tbl:select(2,2)
local frtbl = tbl:select(2,1)
local fitbl = fi:narrow(1, 1, (nfi * nto)) -- part of fi that covers distinct chunks
local ufrtbl= frtbl:unfold(1, nto, nto)
local utotbl= totbl:unfold(1, nto, nto)
local ufitbl= fitbl:unfold(1, nto, nto)
-- start filling frtbl
for i=1,nout do -- fro each unit in target map
ufrtbl:select(1,i):copy(ufitbl:select(1,frcntr))
frcntr = frcntr + 1
if frcntr-1 == nfi then -- reset fi
fi:copy(torch.randperm(nin))
frcntr = 1
end
end
for tocntr=1,utotbl:size(1) do
utotbl:select(1,tocntr):fill(tocntr)
end
return tbl
end
function SpatialConvolutionMap:__init(conMatrix, kW, kH, dW, dH)
parent.__init(self)
dW = dW or 1
dH = dH or 1
self.kW = kW
self.kH = kH
self.dW = dW
self.dH = dH
self.connTable = conMatrix
self.nInputPlane = self.connTable:select(2,1):max()
self.nOutputPlane = self.connTable:select(2,2):max()
self.weight = torch.Tensor(self.connTable:size(1), kH, kW)
self.bias = torch.Tensor(self.nOutputPlane)
self.gradWeight = torch.Tensor(self.connTable:size(1), kH, kW)
self.gradBias = torch.Tensor(self.nOutputPlane)
self:reset()
end
function SpatialConvolutionMap:reset(stdv)
if stdv then
stdv = stdv * math.sqrt(3)
if nn.oldSeed then
self.weight:apply(function()
return torch.uniform(-stdv, stdv)
end)
self.bias:apply(function()
return torch.uniform(-stdv, stdv)
end)
else
self.weight:uniform(-stdv, stdv)
self.bias:uniform(-stdv, stdv)
end
else
local ninp = torch.Tensor(self.nOutputPlane):zero()
for i=1,self.connTable:size(1) do ninp[self.connTable[i][2]] = ninp[self.connTable[i][2]]+1 end
for k=1,self.connTable:size(1) do
stdv = 1/math.sqrt(self.kW*self.kH*ninp[self.connTable[k][2]])
if nn.oldSeed then
self.weight:select(1,k):apply(function() return torch.uniform(-stdv,stdv) end)
else
self.weight:select(1,k):uniform(-stdv,stdv)
end
end
for k=1,self.bias:size(1) do
stdv = 1/math.sqrt(self.kW*self.kH*ninp[k])
self.bias[k] = torch.uniform(-stdv,stdv)
end
end
end
function SpatialConvolutionMap:updateOutput(input)
input.THNN.SpatialConvolutionMap_updateOutput(
input:cdata(),
self.output:cdata(),
self.weight:cdata(),
self.bias:cdata(),
self.connTable:cdata(),
self.nInputPlane,
self.nOutputPlane,
self.dW, self.dH
)
return self.output
end
function SpatialConvolutionMap:updateGradInput(input, gradOutput)
input.THNN.SpatialConvolutionMap_updateGradInput(
input:cdata(),
gradOutput:cdata(),
self.gradInput:cdata(),
self.weight:cdata(),
self.bias:cdata(),
self.connTable:cdata(),
self.nInputPlane,
self.nOutputPlane,
self.dW, self.dH
)
return self.gradInput
end
function SpatialConvolutionMap:accGradParameters(input, gradOutput, scale)
input.THNN.SpatialConvolutionMap_accGradParameters(
input:cdata(),
gradOutput:cdata(),
self.gradWeight:cdata(),
self.gradBias:cdata(),
self.connTable:cdata(),
self.nInputPlane,
self.nOutputPlane,
self.dW, self.dH,
scale or 1
)
end
function SpatialConvolutionMap:decayParameters(decay)
self.weight:add(-decay, self.weight)
self.bias:add(-decay, self.bias)
end
|
