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local SpatialConvolutionLocal, parent = torch.class('nn.SpatialConvolutionLocal', 'nn.Module')
function SpatialConvolutionLocal:__init(nInputPlane, nOutputPlane, iW, iH ,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.iW = iW
self.iH = iH
self.dW = dW
self.dH = dH
self.padW = padW or 0
self.padH = padH or self.padW
self.oW = math.floor((self.padW * 2 + iW - self.kW) / self.dW) + 1
self.oH = math.floor((self.padH * 2 + iH - self.kH) / self.dH) + 1
assert(1 <= self.oW and 1 <= self.oH, 'illegal configuration: output width or height less than 1')
self.weight = torch.Tensor(self.oH, self.oW, nOutputPlane, nInputPlane, kH, kW)
self.bias = torch.Tensor(nOutputPlane, self.oH, self.oW)
self.gradWeight = torch.Tensor():resizeAs(self.weight)
self.gradBias = torch.Tensor():resizeAs(self.bias)
self:reset()
end
function SpatialConvolutionLocal: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 viewWeight(self)
self.weight = self.weight:view(self.oH * self.oW, self.nOutputPlane, self.nInputPlane * self.kH * self.kW)
if self.gradWeight and self.gradWeight:dim() > 0 then
self.gradWeight = self.gradWeight:view(self.oH * self.oW, self.nOutputPlane, self.nInputPlane * self.kH * self.kW)
end
end
local function unviewWeight(self)
self.weight = self.weight:view(self.oH, self.oW, self.nOutputPlane, self.nInputPlane, self.kH, self.kW)
if self.gradWeight and self.gradWeight:dim() > 0 then
self.gradWeight = self.gradWeight:view(self.oH, self.oW, self.nOutputPlane, self.nInputPlane, self.kH, self.kW)
end
end
local function checkInputSize(self, input)
if input:nDimension() == 3 then
if input:size(1) ~= self.nInputPlane or input:size(2) ~= self.iH or input:size(3) ~= self.iW then
error(string.format('Given input size: (%dx%dx%d) inconsistent with expected input size: (%dx%dx%d).',
input:size(1), input:size(2), input:size(3), self.nInputPlane, self.iH, self.iW))
end
elseif input:nDimension() == 4 then
if input:size(2) ~= self.nInputPlane or input:size(3) ~= self.iH or input:size(4) ~= self.iW then
error(string.format('Given input size: (%dx%dx%dx%d) inconsistent with expected input size: (batchsize x%dx%dx%d).',
input:size(1), input:size(2), input:size(3), input:size(4), self.nInputPlane, self.iH, self.iW))
end
else
error('3D or 4D(batch mode) tensor expected')
end
end
local function checkOutputSize(self, input, output)
if output:nDimension() ~= input:nDimension() then
error('inconsistent dimension between output and input.')
end
if output:nDimension() == 3 then
if output:size(1) ~= self.nOutputPlane or output:size(2) ~= self.oH or output:size(3) ~= self.oW then
error(string.format('Given output size: (%dx%dx%d) inconsistent with expected output size: (%dx%dx%d).',
output:size(1), output:size(2), output:size(3), self.nOutputPlane, self.oH, self.oW))
end
elseif output:nDimension() == 4 then
if output:size(2) ~= self.nOutputPlane or output:size(3) ~= self.oH or output:size(4) ~= self.oW then
error(string.format('Given output size: (%dx%dx%dx%d) inconsistent with expected output size: (batchsize x%dx%dx%d).',
output:size(1), output:size(2), output:size(3), output:size(4), self.nOutputPlane, self.oH, self.oW))
end
else
error('3D or 4D(batch mode) tensor expected')
end
end
function SpatialConvolutionLocal:updateOutput(input)
self.finput = self.finput or input.new()
self.fgradInput = self.fgradInput or input.new()
checkInputSize(self, input)
viewWeight(self)
input.THNN.SpatialConvolutionLocal_updateOutput(
input:cdata(),
self.output:cdata(),
self.weight:cdata(),
self.bias:cdata(),
self.finput:cdata(),
self.fgradInput:cdata(),
self.kW, self.kH,
self.dW, self.dH,
self.padW, self.padH,
self.iW, self.iH,
self.oW, self.oH
)
unviewWeight(self)
return self.output
end
function SpatialConvolutionLocal:updateGradInput(input, gradOutput)
checkInputSize(self, input)
checkOutputSize(self, input, gradOutput)
if self.gradInput then
viewWeight(self)
input.THNN.SpatialConvolutionLocal_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,
self.iW, self.iH,
self.oW, self.oH
)
unviewWeight(self)
return self.gradInput
end
end
function SpatialConvolutionLocal:accGradParameters(input, gradOutput, scale)
scale = scale or 1
checkInputSize(self, input)
checkOutputSize(self, input, gradOutput)
viewWeight(self)
input.THNN.SpatialConvolutionLocal_accGradParameters(
input:cdata(),
gradOutput:cdata(),
self.gradWeight:cdata(),
self.gradBias:cdata(),
self.finput:cdata(),
self.fgradInput:cdata(),
self.kW, self.kH,
self.dW, self.dH,
self.padW, self.padH,
self.iW, self.iH,
self.oW, self.oH,
scale
)
unviewWeight(self)
end
function SpatialConvolutionLocal: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 SpatialConvolutionLocal:__tostring__()
local s = string.format('%s(%d -> %d, %dx%d, %dx%d', torch.type(self),
self.nInputPlane, self.nOutputPlane, self.iW, self.iH, 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
return s .. ')'
end
function SpatialConvolutionLocal:clearState()
nn.utils.clear(self, 'finput', 'fgradInput', '_input', '_gradOutput')
return parent.clearState(self)
end
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