Fixed normalization for 1D kernels, and added Contrastive Normalization

1 files changed, 42 insertions, 0 deletions

diff --git a/SpatialContrastiveNormalization.lua b/SpatialContrastiveNormalization.lua
new file mode 100644
index 0000000..262d3b1
--- /dev/null
+++ b/SpatialContrastiveNormalization.lua
@@ -0,0 +1,42 @@
+local SpatialContrastiveNormalization, parent = torch.class('nn.SpatialContrastiveNormalization','nn.Module')
+
+function SpatialContrastiveNormalization:__init(nInputPlane, kernel, threshold, thresval)
+   parent.__init(self)
+
+   -- get args
+   self.nInputPlane = nInputPlane or 1
+   self.kernel = kernel or torch.Tensor(9,9):fill(1)
+   self.threshold = threshold or 1e-4
+   self.thresval = thresval or 1e-4
+   local kdim = self.kernel:nDimension()
+
+   -- check args
+   if kdim ~= 2 and kdim ~= 1 then
+      error('<SpatialContrastiveNormalization> 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('<SpatialContrastiveNormalization> averaging kernel must have ODD dimensions')
+   end
+
+   -- instantiate sub+div normalization
+   self.normalizer = nn.Sequential()
+   self.normalizer:add(nn.SpatialSubtractiveNormalization(self.nInputPlane, self.kernel))
+   self.normalizer:add(nn.SpatialDivisiveNormalization(self.nInputPlane, self.kernel,
+                                                       self.threshold, self.threshval))
+end
+
+function SpatialContrastiveNormalization:updateOutput(input)
+   self.output = self.normalizer:forward(input)
+   return self.output
+end
+
+function SpatialContrastiveNormalization:updateGradInput(input, gradOutput)
+   self.gradInput = self.normalizer:backward(input, gradOutput)
+   return self.gradInput
+end
+
+function SpatialContrastiveNormalization:type(type)
+   parent.type(self,type)
+   self.normalizer:type(type)
+   return self
+end