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local SpatialSubtractiveNormalization, parent = torch.class('nn.SpatialSubtractiveNormalization','nn.Module')
function SpatialSubtractiveNormalization:__init(nInputPlane, kernel)
parent.__init(self)
-- get args
self.nInputPlane = nInputPlane or 1
self.kernel = kernel or torch.Tensor(9,9):fill(1)
local kdim = self.kernel:nDimension()
-- check args
if kdim ~= 2 and kdim ~= 1 then
error('<SpatialSubtractiveNormalization> averaging kernel must be 2D or 1D')
end
if (self.kernel:size(1) % 2) == 0 or (kdim == 2 and (self.kernel:size(2) % 2) == 0) then
error('<SpatialSubtractiveNormalization> averaging kernel must have ODD dimensions')
end
-- normalize kernel
self.kernel:div(self.kernel:sum() * self.nInputPlane)
-- padding values
local padH = math.floor(self.kernel:size(1)/2)
local padW = padH
if kdim == 2 then
padW = math.floor(self.kernel:size(2)/2)
end
-- create convolutional mean extractor
self.meanestimator = nn.Sequential()
self.meanestimator:add(nn.SpatialZeroPadding(padW, padW, padH, padH))
if kdim == 2 then
self.meanestimator:add(nn.SpatialConvolution(self.nInputPlane, 1, self.kernel:size(2), self.kernel:size(1)))
else
self.meanestimator:add(nn.SpatialConvolutionMap(nn.tables.oneToOne(self.nInputPlane), self.kernel:size(1), 1))
self.meanestimator:add(nn.SpatialConvolution(self.nInputPlane, 1, 1, self.kernel:size(1)))
end
self.meanestimator:add(nn.Replicate(self.nInputPlane,1,3))
-- set kernel and bias
if kdim == 2 then
for i = 1,self.nInputPlane do
self.meanestimator.modules[2].weight[1][i] = self.kernel
end
self.meanestimator.modules[2].bias:zero()
else
for i = 1,self.nInputPlane do
self.meanestimator.modules[2].weight[i]:copy(self.kernel)
self.meanestimator.modules[3].weight[1][i]:copy(self.kernel)
end
self.meanestimator.modules[2].bias:zero()
self.meanestimator.modules[3].bias:zero()
end
-- other operation
self.subtractor = nn.CSubTable()
self.divider = nn.CDivTable()
-- coefficient array, to adjust side effects
self.coef = torch.Tensor(1,1,1)
end
function SpatialSubtractiveNormalization:updateOutput(input)
-- compute side coefficients
local dim = input:dim()
if not self._inpsz or not input:isSize(self._inpsz) then
self._inpsz = input:size()
self.ones = self.ones or input.new()
self._coef = self._coef or self.coef.new()
if dim == 4 then
-- batch mode
self.ones:resizeAs(input[1]):fill(1)
local coef = self.meanestimator:updateOutput(self.ones)
self._coef:resizeAs(coef):copy(coef) -- make contiguous for view
local size = coef:size():totable()
table.insert(size,1,input:size(1))
self.coef = self._coef:view(1,table.unpack(self._coef:size():totable())):expand(table.unpack(size))
else
self.ones:resizeAs(input):fill(1)
local coef = self.meanestimator:updateOutput(self.ones)
self._coef:resizeAs(coef):copy(coef) -- copy meanestimator.output as it will be used below
self.coef = self._coef
end
end
-- compute mean
self.localsums = self.meanestimator:updateOutput(input)
self.adjustedsums = self.divider:updateOutput{self.localsums, self.coef}
self.output = self.subtractor:updateOutput{input, self.adjustedsums}
-- done
return self.output
end
function SpatialSubtractiveNormalization:updateGradInput(input, gradOutput)
-- resize grad
self.gradInput:resizeAs(input):zero()
-- backprop through all modules
local gradsub = self.subtractor:updateGradInput({input, self.adjustedsums}, gradOutput)
local graddiv = self.divider:updateGradInput({self.localsums, self.coef}, gradsub[2])
local size = self.meanestimator:updateGradInput(input, graddiv[1]):size()
self.gradInput:add(self.meanestimator:updateGradInput(input, graddiv[1]))
self.gradInput:add(gradsub[1])
-- done
return self.gradInput
end
function SpatialSubtractiveNormalization:clearState()
self._inpsz = nil
if self.ones then self.ones:set() end
if self._coef then self._coef:set() end
self.meanestimator:clearState()
return parent.clearState(self)
end
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