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local THNN = require 'nn.THNN'
local SpatialConvolution, parent = torch.class('nn.SpatialConvolution', 'nn.Module')
function SpatialConvolution:__init(nInputPlane, nOutputPlane, kW, kH, dW, dH, padW, padH)
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.padW = padW or 0
self.padH = padH or self.padW
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:noBias()
self.bias = nil
self.gradBias = nil
return self
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)
if self.bias then
self.bias:apply(function()
return torch.uniform(-stdv, stdv)
end)
end
else
self.weight:uniform(-stdv, stdv)
if self.bias then
self.bias:uniform(-stdv, stdv)
end
end
end
local function backCompatibility(self)
self.finput = self.finput or self.weight.new()
self.fgradInput = self.fgradInput or self.weight.new()
if self.padding then
self.padW = self.padding
self.padH = self.padding
self.padding = nil
else
self.padW = self.padW or 0
self.padH = self.padH or 0
end
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
function SpatialConvolution:updateOutput(input)
assert(input.THNN, torch.type(input)..'.THNN backend not imported')
backCompatibility(self)
input.THNN.SpatialConvolutionMM_updateOutput(
input:cdata(),
self.output:cdata(),
self.weight:cdata(),
THNN.optionalTensor(self.bias),
self.finput:cdata(),
self.fgradInput:cdata(),
self.kW, self.kH,
self.dW, self.dH,
self.padW, self.padH
)
return self.output
end
function SpatialConvolution:updateGradInput(input, gradOutput)
assert(input.THNN, torch.type(input)..'.THNN backend not imported')
if self.gradInput then
backCompatibility(self)
input.THNN.SpatialConvolutionMM_updateGradInput(
input:cdata(),
gradOutput:cdata(),
self.gradInput:cdata(),
self.weight:cdata(),
self.finput:cdata(),
self.fgradInput:cdata(),
self.kW, self.kH,
self.dW, self.dH,
self.padW, self.padH
)
return self.gradInput
end
end
function SpatialConvolution:accGradParameters(input, gradOutput, scale)
assert(input.THNN, torch.type(input)..'.THNN backend not imported')
scale = scale or 1
backCompatibility(self)
input.THNN.SpatialConvolutionMM_accGradParameters(
input:cdata(),
gradOutput:cdata(),
self.gradWeight:cdata(),
THNN.optionalTensor(self.gradBias),
self.finput:cdata(),
self.fgradInput:cdata(),
self.kW, self.kH,
self.dW, self.dH,
self.padW, self.padH,
scale
)
end
function SpatialConvolution:type(type,tensorCache)
self.finput = self.finput and torch.Tensor()
self.fgradInput = self.fgradInput and torch.Tensor()
return parent.type(self,type,tensorCache)
end
function SpatialConvolution:__tostring__()
local s = string.format('%s(%d -> %d, %dx%d', torch.type(self),
self.nInputPlane, self.nOutputPlane, self.kW, self.kH)
if self.dW ~= 1 or self.dH ~= 1 or self.padW ~= 0 or self.padH ~= 0 then
s = s .. string.format(', %d,%d', self.dW, self.dH)
end
if (self.padW or self.padH) and (self.padW ~= 0 or self.padH ~= 0) then
s = s .. ', ' .. self.padW .. ',' .. self.padH
end
if self.bias then
return s .. ')'
else
return s .. ') without bias'
end
end
function SpatialConvolution:clearState()
nn.utils.clear(self, 'finput', 'fgradInput', '_input', '_gradOutput')
return parent.clearState(self)
end
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