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-rw-r--r--contrib/lua-torch/nn/SpatialConvolutionMap.lua154
1 files changed, 154 insertions, 0 deletions
diff --git a/contrib/lua-torch/nn/SpatialConvolutionMap.lua b/contrib/lua-torch/nn/SpatialConvolutionMap.lua
new file mode 100644
index 000000000..9051c119e
--- /dev/null
+++ b/contrib/lua-torch/nn/SpatialConvolutionMap.lua
@@ -0,0 +1,154 @@
+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 nfi = math.floor(nin/nto) -- number of distinct nto chunks
+ 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):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)
+ self.gradBias = torch.Tensor(self.nOutputPlane)
+
+ self:reset()
+end
+
+function SpatialConvolutionMap:reset(stdv)
+ if stdv then
+ stdv = stdv * math.sqrt(3)
+ if nn.oldSeed then
+ self.weight:apply(function()
+ return torch.uniform(-stdv, stdv)
+ end)
+ self.bias:apply(function()
+ return torch.uniform(-stdv, stdv)
+ end)
+ else
+ self.weight:uniform(-stdv, stdv)
+ self.bias: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]])
+ if nn.oldSeed then
+ self.weight:select(1,k):apply(function() return torch.uniform(-stdv,stdv) end)
+ else
+ self.weight:select(1,k):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.THNN.SpatialConvolutionMap_updateOutput(
+ input:cdata(),
+ self.output:cdata(),
+ self.weight:cdata(),
+ self.bias:cdata(),
+ self.connTable:cdata(),
+ self.nInputPlane,
+ self.nOutputPlane,
+ self.dW, self.dH
+ )
+ return self.output
+end
+
+function SpatialConvolutionMap:updateGradInput(input, gradOutput)
+ input.THNN.SpatialConvolutionMap_updateGradInput(
+ input:cdata(),
+ gradOutput:cdata(),
+ self.gradInput:cdata(),
+ self.weight:cdata(),
+ self.bias:cdata(),
+ self.connTable:cdata(),
+ self.nInputPlane,
+ self.nOutputPlane,
+ self.dW, self.dH
+ )
+ return self.gradInput
+end
+
+function SpatialConvolutionMap:accGradParameters(input, gradOutput, scale)
+ input.THNN.SpatialConvolutionMap_accGradParameters(
+ input:cdata(),
+ gradOutput:cdata(),
+ self.gradWeight:cdata(),
+ self.gradBias:cdata(),
+ self.connTable:cdata(),
+ self.nInputPlane,
+ self.nOutputPlane,
+ self.dW, self.dH,
+ scale or 1
+ )
+end
+
+function SpatialConvolutionMap:decayParameters(decay)
+ self.weight:add(-decay, self.weight)
+ self.bias:add(-decay, self.bias)
+end