Add ScaleTable for Squeeze and Excitation Networks

lib/ScaleTable.lua

@@ -0,0 +1,34 @@
+local ScaleTable, parent = torch.class("w2nn.ScaleTable", "nn.Module")
+
+function ScaleTable:__init()
+   parent.__init(self)
+   self.gradInput = {}
+   self.grad_tmp = torch.Tensor()
+   self.scale = torch.Tensor()
+end
+function ScaleTable:updateOutput(input)
+   assert(#input == 2)
+   assert(input[1]:size(2) == input[2]:size(2))
+
+   self.scale:resizeAs(input[1]):expandAs(input[2], input[1])
+   self.output:resizeAs(self.scale):copy(self.scale)
+   self.output:cmul(input[1])
+   return self.output
+end
+
+function ScaleTable:updateGradInput(input, gradOutput)
+   self.gradInput[1] = self.gradInput[1] or input[1].new()
+   self.gradInput[1]:resizeAs(input[1]):copy(gradOutput)
+   self.gradInput[1]:cmul(self.scale)
+
+   self.grad_tmp:resizeAs(input[1]):copy(gradOutput)
+   self.grad_tmp:cmul(input[1])
+   self.gradInput[2] = self.gradInput[2] or input[2].new()
+   self.gradInput[2]:resizeAs(input[2]):sum(self.grad_tmp:reshape(self.grad_tmp:size(1), self.grad_tmp:size(2), self.grad_tmp:size(3) * self.grad_tmp:size(4)), 3):resizeAs(input[2])
+
+   for i=#input+1, #self.gradInput do
+       self.gradInput[i] = nil
+   end
+
+   return self.gradInput
+end

lib/w2nn.lua

@@ -84,6 +84,7 @@ else
    require 'RandomBinaryConvolution'
    require 'RandomBinaryCriterion'
    require 'EdgeFilter'
+   require 'ScaleTable'
    return w2nn
 end