1 files changed, 62 insertions, 0 deletions
diff --git a/SpatialFullConvolutionMap.lua b/SpatialFullConvolutionMap.lua
new file mode 100644
index 0000000..4ae8293
--- /dev/null
+++ b/SpatialFullConvolutionMap.lua
@@ -0,0 +1,62 @@
+local SpatialFullConvolutionMap, parent = torch.class('nn.SpatialFullConvolutionMap', 'nn.Module')
+
+function SpatialFullConvolutionMap:__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):max()
+   self.nOutputPlane = self.connTable:select(2,2):max()
+
+   self.weight = torch.Tensor(self.connTable:size(1), kH, kW)
+   self.gradWeight = torch.Tensor(self.connTable:size(1), kH, kW)
+
+   self.bias = torch.Tensor(self.nOutputPlane)
+   self.gradBias = torch.Tensor(self.nOutputPlane)
+   
+   self:reset()
+end
+
+function SpatialFullConvolutionMap: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 SpatialFullConvolutionMap:updateOutput(input)
+   input.nn.SpatialFullConvolutionMap_updateOutput(self, input)
+   return self.output
+end
+
+function SpatialFullConvolutionMap:updateGradInput(input, gradOutput)
+   input.nn.SpatialFullConvolutionMap_updateGradInput(self, input, gradOutput)
+   return self.gradInput
+end
+
+function SpatialFullConvolutionMap:accGradParameters(input, gradOutput, scale)
+   return input.nn.SpatialFullConvolutionMap_accGradParameters(self, input, gradOutput, scale)
+end