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local SpatialFullConvolutionMap, parent = torch.class('nn.SpatialFullConvolutionMap', 'nn.Module')
function SpatialFullConvolutionMap:__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.gradWeight = torch.Tensor(self.connTable:size(1), kH, kW)
self.bias = torch.Tensor(self.nOutputPlane)
self.gradBias = torch.Tensor(self.nOutputPlane)
self:reset()
end
function SpatialFullConvolutionMap:reset(stdv)
if stdv then
stdv = stdv * math.sqrt(3)
self.weight:apply(function()
return torch.uniform(-stdv, stdv)
end)
self.bias:apply(function()
return torch.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]])
self.weight:select(1,k):apply(function() return torch.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 SpatialFullConvolutionMap:updateOutput(input)
input.THNN.SpatialFullConvolutionMap_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 SpatialFullConvolutionMap:updateGradInput(input, gradOutput)
input.THNN.SpatialFullConvolutionMap_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 SpatialFullConvolutionMap:accGradParameters(input, gradOutput, scale)
input.THNN.SpatialFullConvolutionMap_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
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