initial revamp of torch7 tree

1 files changed, 104 insertions, 0 deletions

diff --git a/SpatialSubtractiveNormalization.lua b/SpatialSubtractiveNormalization.lua
new file mode 100644
index 0000000..4df0fc1
--- /dev/null
+++ b/SpatialSubtractiveNormalization.lua
@@ -0,0 +1,104 @@
+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:sumall() * 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.SpatialConvolutionMap(nn.tables.oneToOne(self.nInputPlane),
+                                                      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.SpatialConvolutionMap(nn.tables.oneToOne(self.nInputPlane),
+                                                      1, self.kernel:size(1)))
+   end
+   self.meanestimator:add(nn.Sum(1))
+   self.meanestimator:add(nn.Replicate(self.nInputPlane))
+
+   -- set kernel and bias
+   if kdim == 2 then
+      for i = 1,self.nInputPlane do 
+         self.meanestimator.modules[2].weight[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[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
+   if (input:size(3) ~= self.coef:size(2)) or (input:size(2) ~= self.coef:size(1)) then
+      local ones = input.new():resizeAs(input):fill(1)
+      self.coef = self.meanestimator:updateOutput(ones)
+      self.coef = self.coef:clone()
+   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])
+   self.gradInput:add(self.meanestimator:updateGradInput(input, graddiv[1]))
+   self.gradInput:add(gradsub[1])
+
+   -- done
+   return self.gradInput
+end
+
+function SpatialSubtractiveNormalization:type(type)
+   parent.type(self,type)
+   self.meanestimator:type(type)
+   self.divider:type(type)
+   self.subtractor:type(type)
+   return self
+end