Merge branch 'master' of https://github.com/2ndforks/torch into cudaconv

1 files changed, 32 insertions, 1 deletions

diff --git a/SpatialConvolutionMap.lua b/SpatialConvolutionMap.lua
index 4b525ba..11718fd 100644
--- a/SpatialConvolutionMap.lua
+++ b/SpatialConvolutionMap.lua
@@ -54,6 +54,37 @@ function nn.tables.random(nin, nout, nto)
    return tbl
 end
 
+function constructTableRev(conMatrix)
+   local conMatrixL = conMatrix:type('torch.LongTensor')
+   -- Construct reverse lookup connection table
+   local thickness = conMatrixL:select(2,2):max()
+   -- approximate fanin check
+   if (#conMatrixL)[1] % thickness == 0 then 
+      -- do a proper fanin check and set revTable
+      local fanin = (#conMatrixL)[1] / thickness
+      local revTable = torch.Tensor(thickness, fanin, 2)
+      for ii=1,thickness do
+	 local tempf = fanin
+	 for jj=1,(#conMatrixL)[1] do
+	    if conMatrixL[jj][2] == ii then
+	       if tempf <= 0 then break end
+	       revTable[ii][tempf][1] = conMatrixL[jj][1]
+	       revTable[ii][tempf][2] = jj
+	       tempf = tempf - 1
+	    end
+	 end
+	 if tempf ~= 0 then 
+	    fanin = -1
+	    break
+	 end
+      end
+      if fanin ~= -1 then
+	 return revTable
+      end
+   end
+   return {}
+end
+
 function SpatialConvolutionMap:__init(conMatrix, kW, kH, dW, dH)
    parent.__init(self)
 
@@ -65,9 +96,9 @@ function SpatialConvolutionMap:__init(conMatrix, kW, kH, dW, dH)
    self.dW = dW
    self.dH = dH
    self.connTable = conMatrix
+   self.connTableRev = constructTableRev(conMatrix)
    self.nInputPlane = self.connTable:select(2,1):max()
    self.nOutputPlane = self.connTable:select(2,2):max()
-
    self.weight = torch.Tensor(self.connTable:size(1), kH, kW)
    self.bias = torch.Tensor(self.nOutputPlane)
    self.gradWeight = torch.Tensor(self.connTable:size(1), kH, kW)