lint fixes

5 files changed, 77 insertions, 71 deletions

diff --git a/Pointwise.lua b/Pointwise.lua
index b9157bd..f20f865 100644
--- a/Pointwise.lua
+++ b/Pointwise.lua
@@ -23,8 +23,12 @@ function Pointwise:createIODescriptors(input)
       self.iDesc = cudnn.toDescriptor(input)
       self.oDesc = cudnn.toDescriptor(self.output)
       if not batch then
-         self.gradInput = self.gradInput:view(self.gradInput:size(2), self.gradInput:size(3), self.gradInput:size(4))
-         self.output = self.output:view(self.output:size(2), self.output:size(3), self.output:size(4))
+         self.gradInput = self.gradInput:view(self.gradInput:size(2),
+                                              self.gradInput:size(3),
+                                              self.gradInput:size(4))
+         self.output = self.output:view(self.output:size(2),
+                                        self.output:size(3),
+                                        self.output:size(4))
       end
    end
 end
@@ -39,7 +43,8 @@ function Pointwise:updateOutput(input)
 end
 
 function Pointwise:updateGradInput(input, gradOutput)
-   assert((gradOutput:dim() == 4 or gradOutput:dim() == 3) and gradOutput:isContiguous());
+   assert((gradOutput:dim() == 4 or gradOutput:dim() == 3)
+         and gradOutput:isContiguous());
    self:createIODescriptors(input)
    errcheck('cudnnActivationBackward',
             cudnn.handle[cutorch.getDevice()-1], self.mode,
diff --git a/Pooling.lua b/Pooling.lua
index 94c616a..97eb617 100644
--- a/Pooling.lua
+++ b/Pooling.lua
@@ -46,8 +46,12 @@ function Pooling:createIODescriptors(input)
       self.iDesc = cudnn.toDescriptor(input)
       self.oDesc = cudnn.toDescriptor(self.output)
       if not batch then
-         self.gradInput = self.gradInput:view(self.gradInput:size(2), self.gradInput:size(3), self.gradInput:size(4))
-         self.output = self.output:view(self.output:size(2), self.output:size(3), self.output:size(4))
+         self.gradInput = self.gradInput:view(self.gradInput:size(2),
+                                              self.gradInput:size(3),
+                                              self.gradInput:size(4))
+         self.output = self.output:view(self.output:size(2),
+                                        self.output:size(3),
+                                        self.output:size(4))
       end
    end
 end
diff --git a/SpatialConvolution.lua b/SpatialConvolution.lua
index d175f90..e939592 100644
--- a/SpatialConvolution.lua
+++ b/SpatialConvolution.lua
@@ -1,6 +1,5 @@
 local SpatialConvolution, parent = torch.class('cudnn.SpatialConvolution', 'nn.SpatialConvolution')
 local ffi = require 'ffi'
-local C = cudnn.C
 local errcheck = cudnn.errcheck
 
 function SpatialConvolution:__init(nInputPlane, nOutputPlane, kW, kH, dW, dH, padW, padH)
@@ -64,8 +63,12 @@ function SpatialConvolution:createIODescriptors(input)
          -- create descriptor for output
          self.oDesc = cudnn.toDescriptor(self.output)
          if not batch then
-            self.gradInput = self.gradInput:view(self.gradInput:size(2), self.gradInput:size(3), self.gradInput:size(4))
-            self.output = self.output:view(self.output:size(2), self.output:size(3), self.output:size(4))
+            self.gradInput = self.gradInput:view(self.gradInput:size(2),
+                                                 self.gradInput:size(3),
+                                                 self.gradInput:size(4))
+            self.output = self.output:view(self.output:size(2),
+                                           self.output:size(3),
+                                           self.output:size(4))
          end
    end
 end
@@ -87,7 +90,8 @@ end
 
 function SpatialConvolution:updateGradInput(input, gradOutput)
    if not self.gradInput then return end
-   assert((gradOutput:dim() == 3 or gradOutput:dim() == 4) and gradOutput:isContiguous());
+   assert((gradOutput:dim() == 3 or gradOutput:dim() == 4)
+         and gradOutput:isContiguous());
    if not self.weightDesc then self:resetWeightDescriptors() end
    self:createIODescriptors(input)
    errcheck('cudnnConvolutionBackwardData', cudnn.handle[cutorch.getDevice()-1],
@@ -101,7 +105,8 @@ end
 
 function SpatialConvolution:accGradParameters(input, gradOutput, scale)
    assert(scale == nil or scale == 1)
-   assert((gradOutput:dim() == 3 or gradOutput:dim() == 4) and gradOutput:isContiguous());
+   assert((gradOutput:dim() == 3 or gradOutput:dim() == 4)
+         and gradOutput:isContiguous());
    self:createIODescriptors(input)
    if not self.weightDesc then self:resetWeightDescriptors() end
    -- gradBias
diff --git a/SpatialSoftMax.lua b/SpatialSoftMax.lua
index e245f24..3a4106d 100644
--- a/SpatialSoftMax.lua
+++ b/SpatialSoftMax.lua
@@ -1,6 +1,4 @@
 local SpatialSoftMax, parent = torch.class('cudnn.SpatialSoftMax', 'nn.Module')
-local ffi = require 'ffi'
-local C = cudnn.C
 local errcheck = cudnn.errcheck
 
 function SpatialSoftMax:__init(fast)
@@ -30,8 +28,12 @@ function SpatialSoftMax:createIODescriptors(input)
       self.iDesc = cudnn.toDescriptor(input)
       self.oDesc = cudnn.toDescriptor(self.output)
       if not batch then
-         self.gradInput = self.gradInput:view(self.gradInput:size(2), self.gradInput:size(3), self.gradInput:size(4))
-         self.output = self.output:view(self.output:size(2), self.output:size(3), self.output:size(4))
+         self.gradInput = self.gradInput:view(self.gradInput:size(2),
+                                              self.gradInput:size(3),
+                                              self.gradInput:size(4))
+         self.output = self.output:view(self.output:size(2),
+                                        self.output:size(3),
+                                        self.output:size(4))
       end
    end
 end
@@ -47,7 +49,8 @@ function SpatialSoftMax:updateOutput(input)
 end
 
 function SpatialSoftMax:updateGradInput(input, gradOutput)
-   assert((gradOutput:dim() == 4 or gradOutput:dim() == 3) and gradOutput:isContiguous());
+   assert((gradOutput:dim() == 4 or gradOutput:dim() == 3)
+         and gradOutput:isContiguous());
    self:createIODescriptors(input)
    errcheck('cudnnSoftmaxBackward',
             cudnn.handle[cutorch.getDevice()-1],
diff --git a/test/test.lua b/test/test.lua
index 192f187..855ea71 100644
--- a/test/test.lua
+++ b/test/test.lua
@@ -7,6 +7,7 @@ local precision_backward = 1e-2
 local precision_jac = 1e-3
 local nloop = 1
 local times = {}
+local mytester
 
 
 function cudnntest.SpatialConvolution_forward_batch()
@@ -107,7 +108,8 @@ function cudnntest.SpatialConvolution_forward_single()
    cutorch.synchronize()
    mytester:asserteq(rescuda:dim(), 3, 'error in dimension')
    local error = rescuda:float() - groundtruth:float()
-   mytester:assertlt(error:abs():max(), precision_forward, 'error on state (forward) ')
+   mytester:assertlt(error:abs():max(), precision_forward,
+                     'error on state (forward) ')
 end
 
 
@@ -154,9 +156,12 @@ function cudnntest.SpatialConvolution_backward_single()
    local werror = weightcuda:float() - groundweight:float()
    local berror = biascuda:float() - groundbias:float()
 
-   mytester:assertlt(error:abs():max(), precision_backward, 'error on state (backward) ')
-   mytester:assertlt(werror:abs():max(), precision_backward, 'error on weight (backward) ')
-   mytester:assertlt(berror:abs():max(), precision_backward, 'error on bias (backward) ')
+   mytester:assertlt(error:abs():max(), precision_backward,
+                     'error on state (backward) ')
+   mytester:assertlt(werror:abs():max(), precision_backward,
+                     'error on weight (backward) ')
+   mytester:assertlt(berror:abs():max(), precision_backward,
+                     'error on bias (backward) ')
 end
 
 
@@ -212,7 +217,7 @@ function cudnntest.SpatialMaxPooling_single()
    local groundgrad = sconv:backward(input, gradOutput)
    cutorch.synchronize()
    local gconv = cudnn.SpatialMaxPooling(ki,kj,si,sj):cuda()
-   local rescuda = gconv:forward(input)
+   local _ = gconv:forward(input)
    -- serialize and deserialize
    torch.save('modelTemp.t7', gconv)
    gconv = torch.load('modelTemp.t7')
@@ -222,17 +227,15 @@ function cudnntest.SpatialMaxPooling_single()
    mytester:asserteq(rescuda:dim(), 3, 'error in dimension')
    mytester:asserteq(resgrad:dim(), 3, 'error in dimension')
    local error = rescuda:float() - groundtruth:float()
-   mytester:assertlt(error:abs():max(), precision_forward, 'error on state (forward) ')
+   mytester:assertlt(error:abs():max(), precision_forward,
+                     'error on state (forward) ')
    error = resgrad:float() - groundgrad:float()
-   mytester:assertlt(error:abs():max(), precision_backward, 'error on state (backward) ')
+   mytester:assertlt(error:abs():max(), precision_backward,
+                     'error on state (backward) ')
 end
 
 function cudnntest.ReLU_single()
    local from = math.random(1,32)
-   local ki = math.random(2,4)
-   local kj = math.random(2,4)
-   local si = ki
-   local sj = kj
    local outi = math.random(1,64)
    local outj = math.random(1,64)
    local ini = outi
@@ -245,7 +248,7 @@ function cudnntest.ReLU_single()
    local groundgrad = sconv:backward(input, gradOutput)
    cutorch.synchronize()
    local gconv = cudnn.ReLU():cuda()
-   local rescuda = gconv:forward(input)
+   local _ = gconv:forward(input)
 
    -- serialize and deserialize
    torch.save('modelTemp.t7', gconv)
@@ -257,18 +260,16 @@ function cudnntest.ReLU_single()
    mytester:asserteq(rescuda:dim(), 3, 'error in dimension')
    mytester:asserteq(resgrad:dim(), 3, 'error in dimension')
    local error = rescuda:float() - groundtruth:float()
-   mytester:assertlt(error:abs():max(), precision_forward, 'error on state (forward) ')
+   mytester:assertlt(error:abs():max(), precision_forward,
+                     'error on state (forward) ')
    error = resgrad:float() - groundgrad:float()
-   mytester:assertlt(error:abs():max(), precision_backward, 'error on state (backward) ')
+   mytester:assertlt(error:abs():max(), precision_backward,
+                     'error on state (backward) ')
 end
 
 function cudnntest.ReLU_batch()
    local bs = math.random(1,32)
    local from = math.random(1,32)
-   local ki = math.random(2,4)
-   local kj = math.random(2,4)
-   local si = ki
-   local sj = kj
    local outi = math.random(1,64)
    local outj = math.random(1,64)
    local ini = outi
@@ -293,17 +294,15 @@ function cudnntest.ReLU_batch()
    mytester:asserteq(rescuda:dim(), 4, 'error in dimension')
    mytester:asserteq(resgrad:dim(), 4, 'error in dimension')
    local error = rescuda:float() - groundtruth:float()
-   mytester:assertlt(error:abs():max(), precision_forward, 'error on state (forward) ')
+   mytester:assertlt(error:abs():max(), precision_forward,
+                     'error on state (forward) ')
    error = resgrad:float() - groundgrad:float()
-   mytester:assertlt(error:abs():max(), precision_backward, 'error on state (backward) ')
+   mytester:assertlt(error:abs():max(), precision_backward,
+                     'error on state (backward) ')
 end
 
 function cudnntest.Tanh_single()
    local from = math.random(1,32)
-   local ki = math.random(2,4)
-   local kj = math.random(2,4)
-   local si = ki
-   local sj = kj
    local outi = math.random(1,64)
    local outj = math.random(1,64)
    local ini = outi
@@ -316,7 +315,7 @@ function cudnntest.Tanh_single()
    local groundgrad = sconv:backward(input, gradOutput)
    cutorch.synchronize()
    local gconv = cudnn.Tanh():cuda()
-   local rescuda = gconv:forward(input)
+   local _ = gconv:forward(input)
 
    -- serialize and deserialize
    torch.save('modelTemp.t7', gconv)
@@ -328,18 +327,16 @@ function cudnntest.Tanh_single()
    mytester:asserteq(rescuda:dim(), 3, 'error in dimension')
    mytester:asserteq(resgrad:dim(), 3, 'error in dimension')
    local error = rescuda:float() - groundtruth:float()
-   mytester:assertlt(error:abs():max(), precision_forward, 'error on state (forward) ')
+   mytester:assertlt(error:abs():max(), precision_forward,
+                     'error on state (forward) ')
    error = resgrad:float() - groundgrad:float()
-   mytester:assertlt(error:abs():max(), precision_backward, 'error on state (backward) ')
+   mytester:assertlt(error:abs():max(), precision_backward,
+                     'error on state (backward) ')
 end
 
 function cudnntest.Tanh_batch()
    local bs = math.random(1,32)
    local from = math.random(1,32)
-   local ki = math.random(2,4)
-   local kj = math.random(2,4)
-   local si = ki
-   local sj = kj
    local outi = math.random(1,64)
    local outj = math.random(1,64)
    local ini = outi
@@ -364,17 +361,15 @@ function cudnntest.Tanh_batch()
    mytester:asserteq(rescuda:dim(), 4, 'error in dimension')
    mytester:asserteq(resgrad:dim(), 4, 'error in dimension')
    local error = rescuda:float() - groundtruth:float()
-   mytester:assertlt(error:abs():max(), precision_forward, 'error on state (forward) ')
+   mytester:assertlt(error:abs():max(), precision_forward,
+                     'error on state (forward) ')
    error = resgrad:float() - groundgrad:float()
-   mytester:assertlt(error:abs():max(), precision_backward, 'error on state (backward) ')
+   mytester:assertlt(error:abs():max(), precision_backward,
+                     'error on state (backward) ')
 end
 
 function cudnntest.Sigmoid_single()
    local from = math.random(1,32)
-   local ki = math.random(2,4)
-   local kj = math.random(2,4)
-   local si = ki
-   local sj = kj
    local outi = math.random(1,64)
    local outj = math.random(1,64)
    local ini = outi
@@ -387,7 +382,7 @@ function cudnntest.Sigmoid_single()
    local groundgrad = sconv:backward(input, gradOutput)
    cutorch.synchronize()
    local gconv = cudnn.Sigmoid():cuda()
-   local rescuda = gconv:forward(input)
+   local _ = gconv:forward(input)
 
    -- serialize and deserialize
    torch.save('modelTemp.t7', gconv)
@@ -399,18 +394,16 @@ function cudnntest.Sigmoid_single()
    mytester:asserteq(rescuda:dim(), 3, 'error in dimension')
    mytester:asserteq(resgrad:dim(), 3, 'error in dimension')
    local error = rescuda:float() - groundtruth:float()
-   mytester:assertlt(error:abs():max(), precision_forward, 'error on state (forward) ')
+   mytester:assertlt(error:abs():max(), precision_forward,
+                     'error on state (forward) ')
    error = resgrad:float() - groundgrad:float()
-   mytester:assertlt(error:abs():max(), precision_backward, 'error on state (backward) ')
+   mytester:assertlt(error:abs():max(), precision_backward,
+                     'error on state (backward) ')
 end
 
 function cudnntest.Sigmoid_batch()
    local bs = math.random(1,32)
    local from = math.random(1,32)
-   local ki = math.random(2,4)
-   local kj = math.random(2,4)
-   local si = ki
-   local sj = kj
    local outi = math.random(1,64)
    local outj = math.random(1,64)
    local ini = outi
@@ -435,17 +428,15 @@ function cudnntest.Sigmoid_batch()
    mytester:asserteq(rescuda:dim(), 4, 'error in dimension')
    mytester:asserteq(resgrad:dim(), 4, 'error in dimension')
    local error = rescuda:float() - groundtruth:float()
-   mytester:assertlt(error:abs():max(), precision_forward, 'error on state (forward) ')
+   mytester:assertlt(error:abs():max(), precision_forward,
+                     'error on state (forward) ')
    error = resgrad:float() - groundgrad:float()
-   mytester:assertlt(error:abs():max(), precision_backward, 'error on state (backward) ')
+   mytester:assertlt(error:abs():max(), precision_backward,
+                     'error on state (backward) ')
 end
 
 function cudnntest.SoftMax_single()
    local from = math.random(1,32)
-   local ki = math.random(2,4)
-   local kj = math.random(2,4)
-   local si = ki
-   local sj = kj
    local outi = math.random(1,64)
    local outj = math.random(1,64)
    local ini = outi
@@ -458,7 +449,7 @@ function cudnntest.SoftMax_single()
    local groundgrad = sconv:backward(input, gradOutput)
    cutorch.synchronize()
    local gconv = cudnn.SoftMax():cuda()
-   local rescuda = gconv:forward(input)
+   local _ = gconv:forward(input)
 
    -- serialize and deserialize
    torch.save('modelTemp.t7', gconv)
@@ -481,10 +472,6 @@ end
 function cudnntest.SoftMax_batch()
    local bs = math.random(1,32)
    local from = math.random(1,32)
-   local ki = math.random(2,4)
-   local kj = math.random(2,4)
-   local si = ki
-   local sj = kj
    local outi = math.random(1,64)
    local outj = math.random(1,64)
    local ini = outi
@@ -527,5 +514,7 @@ mytester:add(cudnntest)
 for i=1,cutorch.getDeviceCount() do
    print('Running test on device: ' .. i)
    cutorch.setDevice(i)
-   mytester:run()
+   mytester:run(tests)
 end
+
+os.execute('rm -f modelTemp.t7')