initial revamp of torch7 tree

1 files changed, 119 insertions, 0 deletions

diff --git a/SpatialConvolutionMap.lua b/SpatialConvolutionMap.lua
new file mode 100644
index 0000000..0dbff2f
--- /dev/null
+++ b/SpatialConvolutionMap.lua
@@ -0,0 +1,119 @@
+local SpatialConvolutionMap, parent = torch.class('nn.SpatialConvolutionMap', 'nn.Module')
+
+nn.tables = nn.tables or {}
+
+function nn.tables.full(nin, nout)
+   local ft = torch.Tensor(nin*nout,2)
+   local p = 1
+   for j=1,nout do
+      for i=1,nin do
+	 ft[p][1] = i
+	 ft[p][2] = j
+	 p = p + 1
+      end
+   end
+   return ft
+end
+
+function nn.tables.oneToOne(nfeat)
+   local ft = torch.Tensor(nfeat,2)
+   for i=1,nfeat do
+      ft[i][1] = i
+      ft[i][2] = i
+   end
+   return ft
+end
+
+function nn.tables.random(nin, nout, nto)
+   local nker = nto * nout
+   local tbl = torch.Tensor(nker, 2)
+   local fi = torch.randperm(nin)
+   local frcntr = 1
+   local tocntr = 1
+   local nfi = math.floor(nin/nto) -- number of distinct nto chunks 
+   local rfi = math.mod(nin,nto) -- number of remaining from maps
+   local totbl = tbl:select(2,2)
+   local frtbl = tbl:select(2,1)
+   local fitbl = fi:narrow(1, 1, (nfi * nto)) -- part of fi that covers distinct chunks
+   local ufrtbl= frtbl:unfold(1, nto, nto)
+   local utotbl= totbl:unfold(1, nto, nto)
+   local ufitbl= fitbl:unfold(1, nto, nto)
+   
+   -- start filling frtbl
+   for i=1,nout do -- fro each unit in target map
+      ufrtbl:select(1,i):copy(ufitbl:select(1,frcntr))
+      frcntr = frcntr + 1
+      if frcntr-1 ==  nfi then -- reset fi
+	 fi:copy(torch.randperm(nin))
+	 frcntr = 1
+      end
+   end
+   for tocntr=1,utotbl:size(1) do
+      utotbl:select(1,tocntr):fill(tocntr)
+   end
+   return tbl
+end
+
+function SpatialConvolutionMap:__init(conMatrix, kW, kH, dW, dH)
+   parent.__init(self)
+
+   dW = dW or 1
+   dH = dH or 1
+
+   self.kW = kW
+   self.kH = kH
+   self.dW = dW
+   self.dH = dH
+   self.connTable = conMatrix
+   self.nInputPlane = self.connTable:select(2,1):maxall()
+   self.nOutputPlane = self.connTable:select(2,2):maxall()
+
+   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)
+   self.gradBias = torch.Tensor(self.nOutputPlane)
+   
+   self:reset()
+end
+
+function SpatialConvolutionMap:reset(stdv)
+   if stdv then
+      stdv = stdv * math.sqrt(3)
+      self.weight:apply(function()
+			   return torch.uniform(-stdv, stdv)
+			end)
+      self.bias:apply(function()
+			 return torch.uniform(-stdv, stdv)
+		      end)
+   else
+      local ninp = torch.Tensor(self.nOutputPlane):zero()
+      for i=1,self.connTable:size(1) do ninp[self.connTable[i][2]] =  ninp[self.connTable[i][2]]+1 end
+      for k=1,self.connTable:size(1) do
+	 stdv = 1/math.sqrt(self.kW*self.kH*ninp[self.connTable[k][2]])
+	 self.weight:select(1,k):apply(function() return torch.uniform(-stdv,stdv) end)
+      end
+      for k=1,self.bias:size(1) do
+	 stdv = 1/math.sqrt(self.kW*self.kH*ninp[k])
+	 self.bias[k] = torch.uniform(-stdv,stdv)
+      end
+   end
+end
+
+function SpatialConvolutionMap:updateOutput(input)
+   input.nn.SpatialConvolutionMap_updateOutput(self, input)
+   return self.output
+end
+
+function SpatialConvolutionMap:updateGradInput(input, gradOutput)
+   input.nn.SpatialConvolutionMap_updateGradInput(self, input, gradOutput)
+   return self.gradInput
+end
+
+function SpatialConvolutionMap:accGradParameters(input, gradOutput, scale)
+   return input.nn.SpatialConvolutionMap_accGradParameters(self, input, gradOutput, scale)
+end
+
+function SpatialConvolutionMap:decayParameters(decay)
+   self.weight:add(-decay, self.weight)
+   self.bias:add(-decay, self.bias)
+end