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local THNN = require 'nn.THNN'
local SpatialFullConvolution, parent = torch.class('nn.SpatialFullConvolution','nn.Module')
function SpatialFullConvolution:__init(nInputPlane, nOutputPlane,
kW, kH, dW, dH, padW, padH, adjW, adjH)
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 0
self.adjW = adjW or 0
self.adjH = adjH or 0
if self.adjW > self.dW - 1 or self.adjH > self.dH - 1 then
error('adjW and adjH must be smaller than self.dW - 1' ..
' and self.dH - 1 respectively')
end
self.weight = torch.Tensor(nInputPlane, nOutputPlane, kH, kW)
self.gradWeight = torch.Tensor(nInputPlane, nOutputPlane, kH, kW)
self.bias = torch.Tensor(self.nOutputPlane)
self.gradBias = torch.Tensor(self.nOutputPlane)
self.ones = torch.Tensor()
self:reset()
end
function SpatialFullConvolution:noBias()
self.bias = nil
self.gradBias = nil
return self
end
function SpatialFullConvolution:reset(stdv)
if stdv then
stdv = stdv * math.sqrt(3)
else
local nInputPlane = self.nInputPlane
local kH = self.kH
local kW = self.kW
stdv = 1/math.sqrt(kW*kH*nInputPlane)
end
self.weight:uniform(-stdv, stdv)
if self.bias then
self.bias:uniform(-stdv, stdv)
end
end
local function calculateAdj(targetSize, ker, pad, stride)
return (targetSize + 2 * pad - ker) % stride
end
function SpatialFullConvolution:backCompatibility()
self.adjW = self.adjW or 0
self.adjH = self.adjH or 0
end
function SpatialFullConvolution:updateOutput(input)
self:backCompatibility()
local inputTensor = input
local adjW, adjH = self.adjW, self.adjH
-- The input can be a table where the second element indicates the target
-- output size, in which case the adj factors are computed automatically
if torch.type(inputTensor) == 'table' then
inputTensor = input[1]
local targetTensor = input[2]
local tDims = targetTensor:dim()
local tH = targetTensor:size(tDims-1)
local tW = targetTensor:size(tDims)
adjW = calculateAdj(tW, self.kW, self.padW, self.dW)
adjH = calculateAdj(tH, self.kH, self.padH, self.dH)
self.finput = self.finput or input[1].new()
self.fgradInput = self.fgradInput or input[1].new()
else
self.finput = self.finput or input.new()
self.fgradInput = self.fgradInput or input.new()
end
inputTensor.THNN.SpatialFullConvolution_updateOutput(
inputTensor: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,
adjW, adjH
)
return self.output
end
function SpatialFullConvolution:updateGradInput(input, gradOutput)
self:backCompatibility()
if self.gradInput then
local inputTensor = input
local adjW, adjH = self.adjW, self.adjH
-- The input can be a table where the second element indicates the target
-- output size, in which case the adj factors are computed automatically
if torch.type(inputTensor) == 'table' then
inputTensor = input[1]
local targetTensor = input[2]
local tDims = targetTensor:dim()
local tH = targetTensor:size(tDims-1)
local tW = targetTensor:size(tDims)
adjW = calculateAdj(tW, self.kW, self.padW, self.dW)
adjH = calculateAdj(tH, self.kH, self.padH, self.dH)
-- Momentarily extract the gradInput tensor
if torch.type(self.gradInput) == 'table' then
self.gradInput = self.gradInput[1] or inputTensor.new()
end
end
inputTensor.THNN.SpatialFullConvolution_updateGradInput(
inputTensor:cdata(),
gradOutput:cdata(),
self.gradInput:cdata(),
self.weight:cdata(),
self.finput:cdata(),
self.kW, self.kH,
self.dW, self.dH,
self.padW, self.padH,
adjW, adjH
)
if torch.type(input) == 'table' then
-- Create a zero tensor to be expanded and used as gradInput[2].
self.zeroScalar = self.zeroScalar or input[2].new(1):zero()
self.ones:resize(input[2]:dim()):fill(1)
local zeroTensor = self.zeroScalar
:view(table.unpack(self.ones:totable()))
:expandAs(input[2])
self.gradInput = {self.gradInput, zeroTensor}
end
return self.gradInput
end
end
function SpatialFullConvolution:accGradParameters(input, gradOutput, scale)
scale = scale or 1
self:backCompatibility()
local inputTensor = input
local adjW, adjH = self.adjW, self.adjH
-- The input can be a table where the second element indicates the target
-- output size, in which case the adj factors are computed automatically
if torch.type(inputTensor) == 'table' then
inputTensor = input[1]
local targetTensor = input[2]
local tDims = targetTensor:dim()
local tH = targetTensor:size(tDims-1)
local tW = targetTensor:size(tDims)
adjW = calculateAdj(tW, self.kW, self.padW, self.dW)
adjH = calculateAdj(tH, self.kH, self.padH, self.dH)
end
inputTensor.THNN.SpatialFullConvolution_accGradParameters(
inputTensor: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,
adjW, adjH,
scale
)
end
function SpatialFullConvolution: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 SpatialFullConvolution:__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.adjW or self.adjH) and (self.adjW ~= 0 or self.adjH ~= 0) then
s = s .. ', ' .. self.adjW .. ',' .. self.adjH
end
if self.bias then
return s .. ')'
else
return s .. ') without bias'
end
end
function SpatialFullConvolution:clearState()
nn.utils.clear(self, 'finput', 'fgradInput', '_input', '_gradOutput')
return parent.clearState(self)
end
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