Revert "Refactoring CUDNN Find"revert-231-algo

1 files changed, 9 insertions, 12 deletions

diff --git a/TemporalConvolution.lua b/TemporalConvolution.lua
index 947cc4f..4648ffd 100644
--- a/TemporalConvolution.lua
+++ b/TemporalConvolution.lua
@@ -6,8 +6,6 @@ local TemporalConvolution, parent =
 --it is recommended to pass padding parameter to this routine and use cudnn implicit padding facilities.
 --limitation is that padding will be equal on both sides.
 
-local Convolution = cudnn.SpatialConvolution
-
 function TemporalConvolution:__init(inputFrameSize, outputFrameSize,
                             kH, dH, padH)
     local delayedReset = self.reset
@@ -16,8 +14,7 @@ function TemporalConvolution:__init(inputFrameSize, outputFrameSize,
     local nOutputPlane = outputFrameSize
     self.inputFrameSize = inputFrameSize
     self.outputFrameSize = outputFrameSize
-    self.nDim = 4
-    Convolution.__init(self, nInputPlane, nOutputPlane, kW, kH, 1, dH,0,padH)
+    cudnn.SpatialConvolution.__init(self, nInputPlane, nOutputPlane, kW, kH, 1, dH,0,padH)
     self.weight = self.weight:view(nOutputPlane,inputFrameSize*kH)
     self.gradWeight = self.gradWeight:view(outputFrameSize, inputFrameSize*kH)
 --self.dW and self.kW now have different meaning than in nn.TemporalConvolution, because
@@ -31,24 +28,24 @@ function TemporalConvolution:createIODescriptors(input)
     or input:size(3) ~= self.iSize[3] or input:size(4) ~= self.iSize[4] then
        sizeChanged = true
     end
-    Convolution.createIODescriptors(self,input)
+    cudnn.SpatialConvolution.createIODescriptors(self,input)
     if sizeChanged then
        self.oSize = self.output:size()
     end
 end
 
 function TemporalConvolution:fastest(mode)
-    self = Convolution.fastest(self,mode)
+    self = cudnn.SpatialConvolution.fastest(self,mode)
     return self
 end
 
 function TemporalConvolution:setMode(fmode, bdmode, bwmode)
-    self = Convolution.setMode(self,fmode, bdmode, bwmode)
+    self = cudnn.SpatialConvolution.setMode(self,fmode, bdmode, bwmode)
     return self
 end
 
 function TemporalConvolution:resetWeightDescriptors()
-    Convolution.resetWeightDescriptors(self)
+    cudnn.SpatialConvolution.resetWeightDescriptors(self)
 end
 
 local function inputview(input)
@@ -66,7 +63,7 @@ function TemporalConvolution:updateOutput(input)
    self._output = self._output or input.new()
    if self.output:storage() then self._output:set(self.output:storage()) else self._output = self.output end
    if self.buffer:storage() then self.output:set(self.buffer:storage(), 1, self.output:size()) else self.output = self.buffer end
-   Convolution.updateOutput(self,_input)
+   cudnn.SpatialConvolution.updateOutput(self,_input)
    self.buffer = self.output:view(self.oSize):transpose(2,3)
    self.output  = self._output:resize(self.buffer:size()):copy(self.buffer)
    -- self.output here is always 4D, use input dimensions to properly view output
@@ -95,7 +92,7 @@ function TemporalConvolution:updateGradInput(input, gradOutput)
    if not self.gradInput then return end
    local _gradOutput = transposeGradOutput(gradOutput,self.buffer)
    local _input = inputview(input)
-   self.gradInput = Convolution.updateGradInput(self,_input, _gradOutput)
+   self.gradInput = cudnn.SpatialConvolution.updateGradInput(self,_input, _gradOutput)
    if input:dim()==3 then
       self.gradInput = self.gradInput:view(self.iSize[1],self.iSize[3],self.iSize[4])
    else
@@ -109,7 +106,7 @@ function TemporalConvolution:accGradParameters(input,gradOutput,scale)
     local _input = inputview(input)
 -- transpose gradOutput (it will likely be transposed twice, hopefully, no big deal
     local _gradOutput = transposeGradOutput(gradOutput,self.buffer)
-    Convolution.accGradParameters(self,_input,_gradOutput,scale)
+    cudnn.SpatialConvolution.accGradParameters(self,_input,_gradOutput,scale)
 end
 
 function TemporalConvolution:clearDesc()
@@ -120,7 +117,7 @@ end
 
 function TemporalConvolution:write(f)
   self:clearDesc()
-  Convolution.clearDesc(self)
+  cudnn.SpatialConvolution.clearDesc(self)
     local var = {}
     for k,v in pairs(self) do
         var[k] = v