refactoring Deconvolution -> FullConvolution

1 files changed, 66 insertions, 0 deletions

diff --git a/VolumetricFullConvolution.lua b/VolumetricFullConvolution.lua
new file mode 100644
index 0000000..e8e91e3
--- /dev/null
+++ b/VolumetricFullConvolution.lua
@@ -0,0 +1,66 @@
+local VolumetricFullConvolution, parent = torch.class('nn.VolumetricFullConvolution', 'nn.Module')
+
+function VolumetricFullConvolution:__init(nInputPlane, nOutputPlane, kT, kH, kW, dT, dH, dW, pT, pH, pW)
+   parent.__init(self)
+
+   dT = dT or 1
+   dW = dW or 1
+   dH = dH or 1
+
+   pT = pT or 0
+   pW = pW or 0
+   pH = pH or 0
+
+   self.nInputPlane = nInputPlane
+   self.nOutputPlane = nOutputPlane
+   self.kT = kT
+   self.kW = kW
+   self.kH = kH
+   self.dT = dT
+   self.dW = dW
+   self.dH = dH
+   self.pT = pT
+   self.pW = pW
+   self.pH = pH
+
+   self.weight = torch.Tensor(nOutputPlane, nInputPlane, kT, kH, kW)
+   self.bias = torch.Tensor(nOutputPlane)
+   self.gradWeight = torch.Tensor(nOutputPlane, nInputPlane, kT, kH, kW)
+   self.gradBias = torch.Tensor(nOutputPlane)
+   -- temporary buffers for unfolding (CUDA)
+   self.finput = torch.Tensor()
+   self.fgradInput = torch.Tensor()
+   self:reset()
+end
+
+function VolumetricFullConvolution:reset(stdv)
+  -- initialization of parameters
+   if stdv then
+      stdv = stdv * math.sqrt(3)
+   else
+      stdv = 1/math.sqrt(self.kT*self.kW*self.kH*self.nInputPlane)
+   end
+   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
+end
+
+function VolumetricFullConvolution:updateOutput(input)
+   return input.nn.VolumetricFullConvolution_updateOutput(self, input)
+end
+
+function VolumetricFullConvolution:updateGradInput(input, gradOutput)
+   return input.nn.VolumetricFullConvolution_updateGradInput(self, input, gradOutput)
+end
+
+function VolumetricFullConvolution:accGradParameters(input, gradOutput, scale)
+   return input.nn.VolumetricFullConvolution_accGradParameters(self, input, gradOutput, scale)
+end