lint fixes (80 columns)

1 files changed, 37 insertions, 21 deletions

diff --git a/VolumetricConvolution.lua b/VolumetricConvolution.lua
index 05a08d4..74857e2 100644
--- a/VolumetricConvolution.lua
+++ b/VolumetricConvolution.lua
@@ -1,8 +1,12 @@
-local VolumetricConvolution, parent = torch.class('cudnn.VolumetricConvolution', 'nn.VolumetricConvolution')
+local VolumetricConvolution, parent
+   = torch.class('cudnn.VolumetricConvolution', 'nn.VolumetricConvolution')
 local ffi = require 'ffi'
 local errcheck = cudnn.errcheck
 
-function VolumetricConvolution:__init(nInputPlane, nOutputPlane, kT, kW, kH, dT, dW, dH, padT, padW, padH)
+function VolumetricConvolution:__init(nInputPlane, nOutputPlane,
+                               kT, kW, kH,
+                               dT, dW, dH,
+                               padT, padW, padH)
    parent.__init(self, nInputPlane, nOutputPlane, kT, kW, kH, dT, dW, dH)
    self.padT = padT or 0
    self.padW = padW or 0
@@ -13,12 +17,15 @@ end
 
 -- if you change the configuration of the module manually, call this
 function VolumetricConvolution:resetWeightDescriptors()
-   assert(torch.typename(self.weight) == 'torch.CudaTensor', 'Only Cuda supported duh!')
-   assert(torch.typename(self.bias) == 'torch.CudaTensor', 'Only Cuda supported duh!')
+   assert(torch.typename(self.weight) == 'torch.CudaTensor',
+          'Only Cuda supported duh!')
+   assert(torch.typename(self.bias) == 'torch.CudaTensor',
+          'Only Cuda supported duh!')
    -- create filterDescriptor for weight
    self.weightDesc = ffi.new('struct cudnnFilterStruct*[1]')
    errcheck('cudnnCreateFilterDescriptor', self.weightDesc)
-   local desc = torch.IntTensor({self.nOutputPlane, self.nInputPlane, self.kT, self.kH, self.kW})
+   local desc = torch.IntTensor({self.nOutputPlane, self.nInputPlane,
+                             self.kT, self.kH, self.kW})
    errcheck('cudnnSetFilterNdDescriptor', self.weightDesc[0],
             'CUDNN_DATA_FLOAT', 5,
             desc:data());
@@ -28,13 +35,15 @@ function VolumetricConvolution:resetWeightDescriptors()
    ffi.gc(self.weightDesc, destroyWDesc)
 
    -- create descriptor for bias
-   self.biasDesc = cudnn.toDescriptor(self.bias:view(1, self.nOutputPlane, 1, 1))
+   self.biasDesc = cudnn.toDescriptor(self.bias:view(1, self.nOutputPlane,
+                                                     1, 1))
 end
 
 function VolumetricConvolution:createIODescriptors(input)
    local batch = true
    if input:dim() == 4 then
-      input = input:view(1, input:size(1), input:size(2), input:size(3), input:size(4))
+      input = input:view(1, input:size(1), input:size(2),
+                         input:size(3), input:size(4))
       batch = false
    end
    assert(input:dim() == 5 and input:isContiguous());
@@ -53,7 +62,8 @@ function VolumetricConvolution:createIODescriptors(input)
          local pad = torch.IntTensor({self.padT, self.padH, self.padW})
          local stride = torch.IntTensor({self.dT, self.dH, self.dW})
          local upscale = torch.IntTensor({1,1,1})
-         errcheck('cudnnSetConvolutionNdDescriptor', self.convDesc[0], 3, pad:data(),
+         errcheck('cudnnSetConvolutionNdDescriptor', self.convDesc[0],
+                  3, pad:data(),
                   stride:data(), upscale:data(), 'CUDNN_CROSS_CORRELATION');
          local function destroyConvDesc(d)
             errcheck('cudnnDestroyConvolutionDescriptor', d[0]);
@@ -63,28 +73,31 @@ function VolumetricConvolution:createIODescriptors(input)
          -- create output descriptor and resize output
          local oSize = torch.IntTensor(5)
          local oSizeD = oSize:data()
-         errcheck('cudnnGetConvolutionNdForwardOutputDim', self.convDesc[0], self.iDesc[0],
+         errcheck('cudnnGetConvolutionNdForwardOutputDim',
+                  self.convDesc[0], self.iDesc[0],
                   self.weightDesc[0], 5, oSizeD)
          self.output:resize(oSize:long():storage())
          -- create descriptor for output
          self.oDesc = cudnn.toDescriptor(self.output)
-         self.oDescBias = cudnn.toDescriptor(self.output:view(self.output:size(1),
-                                                              self.output:size(2),
-                                                              self.output:size(3)
-                                                                 *self.output:size(4),
-                                                              self.output:size(5)))
+         self.oDescBias = cudnn.toDescriptor(
+            self.output:view(self.output:size(1),
+                             self.output:size(2),
+                             self.output:size(3)*self.output:size(4),
+                             self.output:size(5)))
 
          -- create forwardAlgorithm descriptors for
          local algType = ffi.new("cudnnConvolutionFwdAlgo_t[?]", 1)
          errcheck('cudnnGetConvolutionForwardAlgorithm',
                   cudnn.handle[cutorch.getDevice()-1],
-                  self.iDesc[0], self.weightDesc[0], self.convDesc[0], self.oDesc[0],
-                  'CUDNN_CONVOLUTION_FWD_PREFER_FASTEST', -1, algType)
+                  self.iDesc[0], self.weightDesc[0], self.convDesc[0],
+                  self.oDesc[0], 'CUDNN_CONVOLUTION_FWD_PREFER_FASTEST',
+                     -1, algType)
          self.algType = algType
          local bufSize = torch.LongTensor(1)
          errcheck('cudnnGetConvolutionForwardWorkspaceSize',
                   cudnn.handle[cutorch.getDevice()-1],
-                  self.iDesc[0], self.weightDesc[0], self.convDesc[0], self.oDesc[0],
+                  self.iDesc[0], self.weightDesc[0],
+                  self.convDesc[0], self.oDesc[0],
                   algType[0], bufSize:data())
          self.extraBuffer = self.extraBuffer or input.new(1)
          if bufSize[1] ~= 0 then self.extraBuffer:resize(bufSize[1]) end
@@ -116,8 +129,9 @@ function VolumetricConvolution:updateOutput(input)
             self.extraBuffer:data(), self.extraBuffer:nElement(),
             zero:data(),
             self.oDesc[0], self.output:data());
-   errcheck('cudnnAddTensor', cudnn.handle[cutorch.getDevice()-1], 'CUDNN_ADD_SAME_C',
-            one:data(), self.biasDesc[0], self.bias:data(), one:data(),
+   errcheck('cudnnAddTensor', cudnn.handle[cutorch.getDevice()-1],
+            'CUDNN_ADD_SAME_C', one:data(),
+            self.biasDesc[0], self.bias:data(), one:data(),
             self.oDescBias[0], self.output:data());
    return self.output
 end
@@ -140,7 +154,8 @@ end
 
 function VolumetricConvolution:accGradParameters(input, gradOutput, scale)
    self.scaleT = self.scaleT or torch.FloatTensor(1):fill(1.0)
-   self.scaleT = self.scaleT:float() -- this line forces this member to always be on CPU (needed for cudnn)
+   -- this line forces this member to always be on CPU (needed for cudnn)
+   self.scaleT = self.scaleT:float()
 
    scale = scale or 1.0
    self.scaleT[1] = scale
@@ -155,7 +170,8 @@ function VolumetricConvolution:accGradParameters(input, gradOutput, scale)
             one:data(),
             self.biasDesc[0], self.gradBias:data());
    -- gradWeight
-   errcheck('cudnnConvolutionBackwardFilter', cudnn.handle[cutorch.getDevice()-1],
+   errcheck('cudnnConvolutionBackwardFilter',
+            cudnn.handle[cutorch.getDevice()-1],
             self.scaleT:data(),
             self.iDesc[0], input:data(),
             self.oDesc[0], gradOutput:data(),