nn/SpatialConvolution.lua at master · timharley/nn · GitHub

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
local SpatialConvolution, parent = torch.class('nn.SpatialConvolution', 'nn.Module')

function SpatialConvolution:__init(nInputPlane, nOutputPlane, kW, kH, dW, dH, padding)
   parent.__init(self)

   dW = dW or 1
   dH = dH or 1

   self.nInputPlane = nInputPlane
   self.nOutputPlane = nOutputPlane
   self.kW = kW
   self.kH = kH

   self.dW = dW
   self.dH = dH
   self.padding = padding or 0

   self.weight = torch.Tensor(nOutputPlane, nInputPlane, kH, kW)
   self.bias = torch.Tensor(nOutputPlane)
   self.gradWeight = torch.Tensor(nOutputPlane, nInputPlane, kH, kW)
   self.gradBias = torch.Tensor(nOutputPlane)

   self:reset()
end

function SpatialConvolution:reset(stdv)
   if stdv then
      stdv = stdv * math.sqrt(3)
   else
      stdv = 1/math.sqrt(self.kW*self.kH*self.nInputPlane)
   end
   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
end

local function backCompatibility(self)
   self.finput = self.finput or self.weight.new()
   self.fgradInput = self.fgradInput or self.weight.new()
   self.padding = self.padding or 0
   if self.weight:dim() == 2 then
      self.weight = self.weight:view(self.nOutputPlane, self.nInputPlane, self.kH, self.kW)
   end
   if self.gradWeight and self.gradWeight:dim() == 2 then
      self.gradWeight = self.gradWeight:view(self.nOutputPlane, self.nInputPlane, self.kH, self.kW)
   end
end

local function makeContiguous(self, input, gradOutput)
   if not input:isContiguous() then
      self._input = self._input or input.new()
      self._input:resizeAs(input):copy(input)
      input = self._input
   end
   if gradOutput then
      if not gradOutput:isContiguous() then
	 self._gradOutput = self._gradOutput or gradOutput.new()
	 self._gradOutput:resizeAs(gradOutput):copy(gradOutput)
	 gradOutput = self._gradOutput
      end
   end
   return input, gradOutput
end

-- function to re-view the weight layout in a way that would make the MM ops happy
local function viewWeight(self)
   self.weight = self.weight:view(self.nOutputPlane, self.nInputPlane * self.kH * self.kW)
   self.gradWeight = self.gradWeight and self.gradWeight:view(self.nOutputPlane, self.nInputPlane * self.kH * self.kW)
end

local function unviewWeight(self)
   self.weight = self.weight:view(self.nOutputPlane, self.nInputPlane, self.kH, self.kW)
   self.gradWeight = self.gradWeight and self.gradWeight:view(self.nOutputPlane, self.nInputPlane, self.kH, self.kW)
end

function SpatialConvolution:updateOutput(input)
   backCompatibility(self)
   viewWeight(self)
   input = makeContiguous(self, input)
   local out = input.nn.SpatialConvolutionMM_updateOutput(self, input)
   unviewWeight(self)
   return out
end

function SpatialConvolution:updateGradInput(input, gradOutput)
   if self.gradInput then
      backCompatibility(self)
      viewWeight(self)
      input, gradOutput = makeContiguous(self, input, gradOutput)
      local out = input.nn.SpatialConvolutionMM_updateGradInput(self, input, gradOutput)
      unviewWeight(self)
      return out
   end
end

function SpatialConvolution:accGradParameters(input, gradOutput, scale)
   backCompatibility(self)
   input, gradOutput = makeContiguous(self, input, gradOutput)
   viewWeight(self)
   local out = input.nn.SpatialConvolutionMM_accGradParameters(self, input, gradOutput, scale)
   unviewWeight(self)
   return out
end