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Copy pathSpatialNormalization.lua
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Copy pathSpatialNormalization.lua
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264 lines (241 loc) · 9.52 KB
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local SpatialNormalization, parent = torch.class('nn.SpatialNormalization','nn.Module')
local help_desc =
[[a spatial (2D) contrast normalizer
? computes the local mean and local std deviation
across all input features, using the given 2D kernel
? the local mean is then removed from all maps, and the std dev
used to divide the inputs, with a threshold
? if no threshold is given, the global std dev is used
? weight replication is used to preserve sizes (this is
better than zero-padding, but more costly to compute, use
nn.ContrastNormalization to use zero-padding)
? two 1D kernels can be used instead of a single 2D kernel. This
is beneficial to integrate information over large neiborhoods.
]]
local help_example =
[[EX:
-- create a spatial normalizer, with a 9x9 gaussian kernel
-- works on 8 input feature maps, therefore the mean+dev will
-- be estimated on 8x9x9 cubes
stimulus = torch.randn(8,500,500)
gaussian = image.gaussian(9)
mod = nn.SpatialNormalization(gaussian, 8)
result = mod:forward(stimulus)]]
function SpatialNormalization:__init(...) -- kernel for weighted mean | nb of features
parent.__init(self)
print('<SpatialNormalization> WARNING: this module has been deprecated,')
print(' please use SpatialContrastiveNormalization instead')
-- get args
local args, nf, ker, thres
= xlua.unpack(
{...},
'nn.SpatialNormalization',
help_desc .. '\n' .. help_example,
{arg='nInputPlane', type='number', help='number of input maps', req=true},
{arg='kernel', type='torch.Tensor | table', help='a KxK filtering kernel or two {1xK, Kx1} 1D kernels'},
{arg='threshold', type='number', help='threshold, for division [default = adaptive]'}
)
-- check args
if not ker then
xerror('please provide kernel(s)', 'nn.SpatialNormalization', args.usage)
end
self.kernel = ker
local ker2
if type(ker) == 'table' then
ker2 = ker[2]
ker = ker[1]
end
self.nfeatures = nf
self.fixedThres = thres
-- padding values
self.padW = math.floor(ker:size(2)/2)
self.padH = math.floor(ker:size(1)/2)
self.kerWisPair = 0
self.kerHisPair = 0
-- padding values for 2nd kernel
if ker2 then
self.pad2W = math.floor(ker2:size(2)/2)
self.pad2H = math.floor(ker2:size(1)/2)
else
self.pad2W = 0
self.pad2H = 0
end
self.ker2WisPair = 0
self.ker2HisPair = 0
-- normalize kernel
ker:div(ker:sum())
if ker2 then ker2:div(ker2:sum()) end
-- manage the case where ker is even size (for padding issue)
if (ker:size(2)/2 == math.floor(ker:size(2)/2)) then
print ('Warning, kernel width is even -> not symetric padding')
self.kerWisPair = 1
end
if (ker:size(1)/2 == math.floor(ker:size(1)/2)) then
print ('Warning, kernel height is even -> not symetric padding')
self.kerHisPair = 1
end
if (ker2 and ker2:size(2)/2 == math.floor(ker2:size(2)/2)) then
print ('Warning, kernel width is even -> not symetric padding')
self.ker2WisPair = 1
end
if (ker2 and ker2:size(1)/2 == math.floor(ker2:size(1)/2)) then
print ('Warning, kernel height is even -> not symetric padding')
self.ker2HisPair = 1
end
-- create convolution for computing the mean
local convo1 = nn.Sequential()
convo1:add(nn.SpatialPadding(self.padW,self.padW-self.kerWisPair,
self.padH,self.padH-self.kerHisPair))
local ctable = nn.tables.oneToOne(nf)
convo1:add(nn.SpatialConvolutionMap(ctable,ker:size(2),ker:size(1)))
convo1:add(nn.Sum(1))
convo1:add(nn.Replicate(nf))
-- set kernel
local fb = convo1.modules[2].weight
for i=1,fb:size(1) do fb[i]:copy(ker) end
-- set bias to 0
convo1.modules[2].bias:zero()
-- 2nd ker ?
if ker2 then
local convo2 = nn.Sequential()
convo2:add(nn.SpatialPadding(self.pad2W,self.pad2W-self.ker2WisPair,
self.pad2H,self.pad2H-self.ker2HisPair))
local ctable = nn.tables.oneToOne(nf)
convo2:add(nn.SpatialConvolutionMap(ctable,ker2:size(2),ker2:size(1)))
convo2:add(nn.Sum(1))
convo2:add(nn.Replicate(nf))
-- set kernel
local fb = convo2.modules[2].weight
for i=1,fb:size(1) do fb[i]:copy(ker2) end
-- set bias to 0
convo2.modules[2].bias:zero()
-- convo is a double convo now:
local convopack = nn.Sequential()
convopack:add(convo1)
convopack:add(convo2)
self.convo = convopack
else
self.convo = convo1
end
-- create convolution for computing the meanstd
local convostd1 = nn.Sequential()
convostd1:add(nn.SpatialPadding(self.padW,self.padW-self.kerWisPair,
self.padH,self.padH-self.kerHisPair))
convostd1:add(nn.SpatialConvolutionMap(ctable,ker:size(2),ker:size(1)))
convostd1:add(nn.Sum(1))
convostd1:add(nn.Replicate(nf))
-- set kernel
local fb = convostd1.modules[2].weight
for i=1,fb:size(1) do fb[i]:copy(ker) end
-- set bias to 0
convostd1.modules[2].bias:zero()
-- 2nd ker ?
if ker2 then
local convostd2 = nn.Sequential()
convostd2:add(nn.SpatialPadding(self.pad2W,self.pad2W-self.ker2WisPair,
self.pad2H,self.pad2H-self.ker2HisPair))
convostd2:add(nn.SpatialConvolutionMap(ctable,ker2:size(2),ker2:size(1)))
convostd2:add(nn.Sum(1))
convostd2:add(nn.Replicate(nf))
-- set kernel
local fb = convostd2.modules[2].weight
for i=1,fb:size(1) do fb[i]:copy(ker2) end
-- set bias to 0
convostd2.modules[2].bias:zero()
-- convo is a double convo now:
local convopack = nn.Sequential()
convopack:add(convostd1)
convopack:add(convostd2)
self.convostd = convopack
else
self.convostd = convostd1
end
-- other operation
self.squareMod = nn.Square()
self.sqrtMod = nn.Sqrt()
self.subtractMod = nn.CSubTable()
self.meanDiviseMod = nn.CDivTable()
self.stdDiviseMod = nn.CDivTable()
self.diviseMod = nn.CDivTable()
self.thresMod = nn.Threshold()
-- some tempo states
self.coef = torch.Tensor(1,1)
self.inConvo = torch.Tensor()
self.inMean = torch.Tensor()
self.inputZeroMean = torch.Tensor()
self.inputZeroMeanSq = torch.Tensor()
self.inConvoVar = torch.Tensor()
self.inVar = torch.Tensor()
self.inStdDev = torch.Tensor()
self.thstd = torch.Tensor()
end
function SpatialNormalization:updateOutput(input)
-- auto switch to 3-channel
self.input = input
if (input:nDimension() == 2) then
self.input = input:clone():resize(1,input:size(1),input:size(2))
end
-- recompute coef only if necessary
if (self.input:size(3) ~= self.coef:size(2)) or (self.input:size(2) ~= self.coef:size(1)) then
local intVals = self.input.new(self.nfeatures,self.input:size(2),self.input:size(3)):fill(1)
self.coef = self.convo:updateOutput(intVals)
self.coef = self.coef:clone()
end
-- compute mean
self.inConvo = self.convo:updateOutput(self.input)
self.inMean = self.meanDiviseMod:updateOutput{self.inConvo,self.coef}
self.inputZeroMean = self.subtractMod:updateOutput{self.input,self.inMean}
-- compute std dev
self.inputZeroMeanSq = self.squareMod:updateOutput(self.inputZeroMean)
self.inConvoVar = self.convostd:updateOutput(self.inputZeroMeanSq)
self.inStdDevNotUnit = self.sqrtMod:updateOutput(self.inConvoVar)
self.inStdDev = self.stdDiviseMod:updateOutput({self.inStdDevNotUnit,self.coef})
local meanstd = self.inStdDev:mean()
self.thresMod.threshold = self.fixedThres or math.max(meanstd,1e-3)
self.thresMod.val = self.fixedThres or math.max(meanstd,1e-3)
self.stdDev = self.thresMod:updateOutput(self.inStdDev)
--remove std dev
self.diviseMod:updateOutput{self.inputZeroMean,self.stdDev}
self.output = self.diviseMod.output
return self.output
end
function SpatialNormalization:updateGradInput(input, gradOutput)
-- auto switch to 3-channel
self.input = input
if (input:nDimension() == 2) then
self.input = input:clone():resize(1,input:size(1),input:size(2))
end
self.gradInput:resizeAs(self.input):zero()
-- backprop all
local gradDiv = self.diviseMod:updateGradInput({self.inputZeroMean,self.stdDev},gradOutput)
local gradThres = gradDiv[2]
local gradZeroMean = gradDiv[1]
local gradinStdDev = self.thresMod:updateGradInput(self.inStdDev,gradThres)
local gradstdDiv = self.stdDiviseMod:updateGradInput({self.inStdDevNotUnit,self.coef},gradinStdDev)
local gradinStdDevNotUnit = gradstdDiv[1]
local gradinConvoVar = self.sqrtMod:updateGradInput(self.inConvoVar,gradinStdDevNotUnit)
local gradinputZeroMeanSq = self.convostd:updateGradInput(self.inputZeroMeanSq,gradinConvoVar)
gradZeroMean:add(self.squareMod:updateGradInput(self.inputZeroMean,gradinputZeroMeanSq))
local gradDiff = self.subtractMod:updateGradInput({self.input,self.inMean},gradZeroMean)
local gradinMean = gradDiff[2]
local gradinConvoNotUnit = self.meanDiviseMod:updateGradInput({self.inConvo,self.coef},gradinMean)
local gradinConvo = gradinConvoNotUnit[1]
-- first part of the gradInput
self.gradInput:add(gradDiff[1])
-- second part of the gradInput
self.gradInput:add(self.convo:updateGradInput(self.input,gradinConvo))
return self.gradInput
end
function SpatialNormalization:type(type)
parent.type(self,type)
self.convo:type(type)
self.meanDiviseMod:type(type)
self.subtractMod:type(type)
self.squareMod:type(type)
self.convostd:type(type)
self.sqrtMod:type(type)
self.stdDiviseMod:type(type)
self.thresMod:type(type)
self.diviseMod:type(type)
return self
end
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