Put SpatialLinear back

1 files changed, 65 insertions, 0 deletions

diff --git a/SpatialLinear.lua b/SpatialLinear.lua
new file mode 100644
index 0000000..92767f4
--- /dev/null
+++ b/SpatialLinear.lua
@@ -0,0 +1,65 @@
+local SpatialLinear, parent = torch.class('nn.SpatialLinear', 'nn.Module')
+
+function SpatialLinear:__init(fanin, fanout)
+   parent.__init(self)
+
+   self.fanin = fanin or 1
+   self.fanout = fanout or 1
+
+   self.weightDecay = 0   
+   self.weight = torch.Tensor(self.fanout, self.fanin)
+   self.bias = torch.Tensor(self.fanout)
+   self.gradWeight = torch.Tensor(self.fanout, self.fanin)
+   self.gradBias = torch.Tensor(self.fanout)
+   
+   self.output = torch.Tensor(fanout,1,1)
+   self.gradInput = torch.Tensor(fanin,1,1)
+
+   self:reset()
+end
+
+function SpatialLinear:reset(stdv)
+   if stdv then
+      stdv = stdv * math.sqrt(3)
+   else
+      stdv = 1./math.sqrt(self.weight:size(1))
+   end
+   for i=1,self.weight:size(1) do
+      self.weight:select(1, i):apply(function()
+                                        return torch.uniform(-stdv, stdv)
+                                     end)
+      self.bias[i] = torch.uniform(-stdv, stdv)
+   end
+end
+
+function SpatialLinear:zeroGradParameters(momentum)
+   if momentum then
+      self.gradWeight:mul(momentum)
+      self.gradBias:mul(momentum)
+   else
+      self.gradWeight:zero()
+      self.gradBias:zero()
+   end
+end
+
+function SpatialLinear:updateParameters(learningRate)
+   self.weight:add(-learningRate, self.gradWeight)
+   self.bias:add(-learningRate, self.gradBias)
+end
+
+function SpatialLinear:decayParameters(decay)
+   self.weight:add(-decay, self.weight)
+   self.bias:add(-decay, self.bias)
+end
+
+function SpatialLinear:updateOutput(input)
+   self.output:resize(self.fanout, input:size(2), input:size(3))
+   input.nn.SpatialLinear_updateOutput(self, input)
+   return self.output
+end
+
+function SpatialLinear:updateGradInput(input, gradOutput)
+   self.gradInput:resize(self.fanin, input:size(2), input:size(3))
+   input.nn.SpatialLinear_updateGradInput(self, input, gradOutput)
+   return self.gradInput
+end