Fix various unused variables in nn

17 files changed, 46 insertions, 51 deletions

diff --git a/Concat.lua b/Concat.lua
index 286a351..5743af9 100644
--- a/Concat.lua
+++ b/Concat.lua
@@ -63,9 +63,10 @@ function Concat:accGradParameters(input, gradOutput, scale)
    local offset = 1
    for i,module in ipairs(self.modules) do
       local currentOutput = module.output
-      local currentGradInput = module:accGradParameters(input,
-                                                        gradOutput:narrow(self.dimension, offset, currentOutput:size(self.dimension)),
-                                                        scale)
+      module:accGradParameters(
+          input,
+          gradOutput:narrow(self.dimension, offset, currentOutput:size(self.dimension)),
+          scale)
       offset = offset + currentOutput:size(self.dimension)
    end
 end
@@ -74,9 +75,10 @@ function Concat:accUpdateGradParameters(input, gradOutput, lr)
    local offset = 1
    for i,module in ipairs(self.modules) do
       local currentOutput = module.output
-      local currentGradInput = module:accUpdateGradParameters(input,
-                                                              gradOutput:narrow(self.dimension, offset, currentOutput:size(self.dimension)),
-                                                              lr)
+      module:accUpdateGradParameters(
+          input,
+          gradOutput:narrow(self.dimension, offset, currentOutput:size(self.dimension)),
+          lr)
       offset = offset + currentOutput:size(self.dimension)
    end
 end
diff --git a/CosineEmbeddingCriterion.lua b/CosineEmbeddingCriterion.lua
index 93348fb..293ae23 100644
--- a/CosineEmbeddingCriterion.lua
+++ b/CosineEmbeddingCriterion.lua
@@ -23,12 +23,6 @@ function CosineEmbeddingCriterion:updateOutput(input,y)
    return self.output
 end
 
-local function mathsign(t)
-   if t>0 then return 1; end
-   if t<0 then return -1; end
-   return 2*torch.random(2)-3;
-end
-
 function CosineEmbeddingCriterion:updateGradInput(input, y)
    local v1  = input[1]
    local v2  = input[2]
diff --git a/CriterionTable.lua b/CriterionTable.lua
index e5538f7..be00837 100644
--- a/CriterionTable.lua
+++ b/CriterionTable.lua
@@ -1,6 +1,7 @@
 local CriterionTable, parent = torch.class('nn.CriterionTable', 'nn.Module')
 
 function CriterionTable:__init(criterion)
+   parent.__init(self)
    self.criterion = criterion
    self.gradInput = {criterion.gradInput}
 end
diff --git a/DepthConcat.lua b/DepthConcat.lua
index 70646f4..7187d61 100644
--- a/DepthConcat.lua
+++ b/DepthConcat.lua
@@ -9,7 +9,7 @@
 -- this, we select the largest spatial dimensions and add zero-padding
 -- around the smaller dimensions.
 ------------------------------------------------------------------------
-local DepthConcat, parent = torch.class('nn.DepthConcat', 'nn.Concat')
+local DepthConcat, _ = torch.class('nn.DepthConcat', 'nn.Concat')
 
 function DepthConcat:windowNarrow(output, currentOutput, offset)
    local outputWindow = output:narrow(self.dimension, offset, currentOutput:size(self.dimension))
@@ -79,7 +79,7 @@ function DepthConcat:accGradParameters(input, gradOutput, scale)
    for i,module in ipairs(self.modules) do
       local currentOutput = module.output
       local gradOutputWindow = self:windowNarrow(gradOutput, currentOutput, offset)
-      local currentGradInput = module:accGradParameters(input, gradOutputWindow, scale)
+      module:accGradParameters(input, gradOutputWindow, scale)
       offset = offset + currentOutput:size(self.dimension)
    end
 end
@@ -89,7 +89,7 @@ function DepthConcat:accUpdateGradParameters(input, gradOutput, lr)
    for i,module in ipairs(self.modules) do
       local currentOutput = module.output
       local gradOutputWindow = self:windowNarrow(gradOutput, currentOutput, offset)
-      local currentGradInput = module:accUpdateGradParameters(input, gradOutputWindow, lr)
+      module:accUpdateGradParameters(input, gradOutputWindow, lr)
       offset = offset + currentOutput:size(self.dimension)
    end
 end
diff --git a/Identity.lua b/Identity.lua
index 79b5c08..088cc34 100644
--- a/Identity.lua
+++ b/Identity.lua
@@ -1,4 +1,4 @@
-local Identity, parent = torch.class('nn.Identity', 'nn.Module')
+local Identity, _ = torch.class('nn.Identity', 'nn.Module')
 
 function Identity:updateOutput(input)
    self.output = input
diff --git a/Jacobian.lua b/Jacobian.lua
index 24014b5..c3797bd 100644
--- a/Jacobian.lua
+++ b/Jacobian.lua
@@ -52,7 +52,7 @@ function nn.Jacobian.backwardUpdate(module, input, param)
       end
       dout:zero()
       sdout[i] = 1
-      local din = module:updateGradInput(input, dout)
+      module:updateGradInput(input, dout)
       module:accUpdateGradParameters(input, dout, 1)
       jacobian:select(2,i):copy(param)
    end
@@ -242,7 +242,7 @@ function nn.Jacobian.testAllUpdate(module, input, weight, gradWeight)
    macshu2:updateGradInput(input, gradOutput)
    macshu1:accUpdateGradParameters(input, gradOutput, lr)
    macshu2:accUpdateGradParameters(input, gradOutput, lr)
-   local err = (weightc-maccgp[gradWeight]*(lr*2)-macshu1[weight]):norm()
+   err = (weightc-maccgp[gradWeight]*(lr*2)-macshu1[weight]):norm()
    err = err + (weightc-maccgp[gradWeight]*(lr*2)-macshu2[weight]):norm()
    errors["accUpdateGradParameters [shared]"] = err
 
diff --git a/Module.lua b/Module.lua
index d9410c9..c9b73cc 100644
--- a/Module.lua
+++ b/Module.lua
@@ -171,7 +171,7 @@ function Module:getParameters()
       if storageAndOffset == nil then
           return nil
       end
-      local storage, offset = unpack(storageAndOffset)
+      local _, offset = unpack(storageAndOffset)
       return offset
    end
 
diff --git a/Parallel.lua b/Parallel.lua
index 547f444..3057ba2 100644
--- a/Parallel.lua
+++ b/Parallel.lua
@@ -71,10 +71,12 @@ function Parallel:accGradParameters(input, gradOutput, scale)
    for i=1,nModule do 
       local module = self.modules[i];
       local currentOutput = module.output
-      local currentGradInput = 
-         module:accGradParameters(input:select(self.inputDimension,i),
-                                  gradOutput:narrow(self.outputDimension, 
-                                                    offset, currentOutput:size(self.outputDimension)), scale)
+      module:accGradParameters(
+          input:select(self.inputDimension,i),
+          gradOutput:narrow(
+              self.outputDimension, offset,
+              currentOutput:size(self.outputDimension)),
+          scale)
         
       offset = offset + currentOutput:size(self.outputDimension)
    end
@@ -87,10 +89,11 @@ function Parallel:accUpdateGradParameters(input, gradOutput, lr)
    for i=1,nModule do 
       local module = self.modules[i];
       local currentOutput = module.output
-      local currentGradInput = 
-         module:accUpdateGradParameters(input:select(self.inputDimension,i),
-                                        gradOutput:narrow(self.outputDimension, 
-                                                          offset, currentOutput:size(self.outputDimension)), lr)
+      module:accUpdateGradParameters(
+          input:select(self.inputDimension,i),
+          gradOutput:narrow(self.outputDimension, offset,
+                            currentOutput:size(self.outputDimension)),
+          lr)
         
       offset = offset + currentOutput:size(self.outputDimension)
    end
diff --git a/Sequential.lua b/Sequential.lua
index ec3247b..97554b3 100644
--- a/Sequential.lua
+++ b/Sequential.lua
@@ -1,6 +1,7 @@
 local Sequential, parent = torch.class('nn.Sequential', 'nn.Module')
 
 function Sequential:__init()
+   parent.__init(self)
    self.modules = {}
 end
 
diff --git a/SoftMax.lua b/SoftMax.lua
index 609b353..22f0eda 100644
--- a/SoftMax.lua
+++ b/SoftMax.lua
@@ -1,4 +1,4 @@
-local SoftMax, parent = torch.class('nn.SoftMax', 'nn.Module')
+local SoftMax, _ = torch.class('nn.SoftMax', 'nn.Module')
 
 function SoftMax:updateOutput(input)
    return input.nn.SoftMax_updateOutput(self, input)
diff --git a/SoftMin.lua b/SoftMin.lua
index 90c6c60..7d2358c 100644
--- a/SoftMin.lua
+++ b/SoftMin.lua
@@ -1,4 +1,4 @@
-local SoftMin, parent = torch.class('nn.SoftMin', 'nn.Module')
+local SoftMin, _ = torch.class('nn.SoftMin', 'nn.Module')
 
 function SoftMin:updateOutput(input)
    self.mininput = self.mininput or input.new()
diff --git a/SparseJacobian.lua b/SparseJacobian.lua
index b778e67..19334d1 100644
--- a/SparseJacobian.lua
+++ b/SparseJacobian.lua
@@ -61,7 +61,7 @@ function nn.SparseJacobian.backwardUpdate (module, input, param)
       dout:zero()
       sdout[i] = 1
       module:zeroGradParameters()
-      local din = module:updateGradInput(input, dout)
+      module:updateGradInput(input, dout)
       module:accUpdateGradParameters(input, dout, 1)
       jacobian:select(2,i):copy(param)
    end
@@ -269,7 +269,7 @@ function nn.SparseJacobian.testAllUpdate(module, input, weight, gradWeight)
    macshu2:updateGradInput(input, gradOutput)
    macshu1:accUpdateGradParameters(input, gradOutput, lr)
    macshu2:accUpdateGradParameters(input, gradOutput, lr)
-   local err = (weightc-maccgp[gradWeight]*(lr*2)-macshu1[weight]):norm()
+   err = (weightc-maccgp[gradWeight]*(lr*2)-macshu1[weight]):norm()
    err = err + (weightc-maccgp[gradWeight]*(lr*2)-macshu2[weight]):norm()
    errors["accUpdateGradParameters [shared]"] = err
 
diff --git a/SpatialConvolutionMap.lua b/SpatialConvolutionMap.lua
index e05ce6e..390ace0 100644
--- a/SpatialConvolutionMap.lua
+++ b/SpatialConvolutionMap.lua
@@ -29,9 +29,7 @@ function nn.tables.random(nin, nout, nto)
    local tbl = torch.Tensor(nker, 2)
    local fi = torch.randperm(nin)
    local frcntr = 1
-   local tocntr = 1
    local nfi = math.floor(nin/nto) -- number of distinct nto chunks 
-   local rfi = math.fmod(nin,nto) -- number of remaining from maps
    local totbl = tbl:select(2,2)
    local frtbl = tbl:select(2,1)
    local fitbl = fi:narrow(1, 1, (nfi * nto)) -- part of fi that covers distinct chunks
diff --git a/SplitTable.lua b/SplitTable.lua
index 70b45f6..bd46b71 100644
--- a/SplitTable.lua
+++ b/SplitTable.lua
@@ -28,7 +28,6 @@ function SplitTable:updateGradInput(input, gradOutput)
    local slices = input:size(dimension)
    self.gradInput:resizeAs(input)
 
-   local offset = 1
    for i=1,slices do 
       local currentGradInput = gradOutput[i];        
       self.gradInput:select(dimension,i):copy(currentGradInput)
diff --git a/Transpose.lua b/Transpose.lua
index a43729b..263db60 100644
--- a/Transpose.lua
+++ b/Transpose.lua
@@ -18,7 +18,6 @@ function Transpose:updateOutput(input)
 end
 
 function Transpose:updateGradInput(input, gradOutput)
-   local ndim = gradOutput:nDimension()
    for i = #self.permutations,1,-1 do
       local perm = self.permutations[i]
       gradOutput = gradOutput:transpose(perm[1],perm[2])
diff --git a/hessian.lua b/hessian.lua
index 3d336fe..21302cb 100644
--- a/hessian.lua
+++ b/hessian.lua
@@ -330,7 +330,7 @@ function nn.hessian.enable()
          if storageAndOffset == nil then
              return nil
          end
-         local storage, offset = unpack(storageAndOffset)
+         local _, offset = unpack(storageAndOffset)
          return offset
       end
 
diff --git a/test/test.lua b/test/test.lua
index 11fc1dd..ed7fd21 100644
--- a/test/test.lua
+++ b/test/test.lua
@@ -473,7 +473,7 @@ end
 
 local function criterionJacobianTest1D(cri, input, target)
    local eps = 1e-6
-   local fx = cri:forward(input, target)
+   local _ = cri:forward(input, target)
    local dfdx = cri:backward(input, target)
    -- for each input perturbation, do central difference
    local centraldiff_dfdx = torch.Tensor():resizeAs(dfdx)
@@ -1702,7 +1702,6 @@ end
 
 function nntest.VolumetricMaxPooling()
    local from = math.random(2,3)
-   local to = from
    local kt = math.random(3,4)
    local ki = math.random(3,4)
    local kj = math.random(3,4)
@@ -1738,10 +1737,10 @@ end
 function nntest.Module_getParameters_2()
    local n = nn.Sequential()
    n:add( nn.Linear(10,10) )
-   local p = n:getParameters()
+   local _ = n:getParameters()
 
    n:add( nn.Linear(10,10) )
-   p = n:getParameters()
+   local p = n:getParameters()
 
    mytester:asserteq((p[{ {111,210} }] - n.modules[2].weight):norm(), 0, 'error when appending new module')
    mytester:asserteq((p[{ {211,220} }] - n.modules[2].bias):norm(), 0, 'error when appending new module')
@@ -1772,10 +1771,10 @@ function nntest.Module_getParameters_4()
    local n = nn.Sequential()
    n:add( nn.Linear(10,10) )
    n:add( n.modules[1]:clone() )
-   local p = n:getParameters()
+   local _ = n:getParameters()
 
    n:add(nn.Linear(10,10))
-   p = n:getParameters()
+   local p = n:getParameters()
 
    mytester:asserteq((p[{ {1,100} }] - n.modules[1].weight):norm(), 0, 'error when using cloning')
    mytester:asserteq((p[{ {101,110} }] - n.modules[1].bias):norm(), 0, 'error when using cloning')
@@ -1813,10 +1812,10 @@ function nntest.Module_getParameters_6()
    local n = nn.Sequential()
    n:add( nn.Linear(10,10) )
    n:add( n.modules[1]:clone('weight','bias') )
-   local p = n:getParameters()
+   local _ = n:getParameters()
 
    n:add(nn.Linear(10,10))
-   p = n:getParameters()
+   local p = n:getParameters()
 
    mytester:asserteq((p[{ {1,100} }] - n.modules[1].weight):norm(), 0, 'error when using cloning+sharing')
    mytester:asserteq((p[{ {101,110} }] - n.modules[1].bias):norm(), 0, 'error when using cloning+sharing')
@@ -1834,10 +1833,10 @@ function nntest.Module_getParameters_7()
    local n = nn.Sequential()
    n:add( nn.Linear(10,10) )
    n:add( n.modules[1]:clone('weight','bias') )
-   local p = n:getParameters()
+   local _ = n:getParameters()
 
    n:add(nn.Linear(10,10))
-   p = n:getParameters()
+   local _ = n:getParameters()
 
    local n1 = nn.Sequential()
    n1:add( nn.Linear(10,10) )
@@ -1849,7 +1848,7 @@ function nntest.Module_getParameters_7()
    n:add( n1 )
    n:add( n2 )
 
-   local p = n:getParameters()
+   local _ = n:getParameters()
 
    local nf = nn.Sequential()
    nf:add( n1 )
@@ -1887,7 +1886,7 @@ function nntest.Module_getParameters_8()
   -- clone the second MLP to ensure that the weights before calling getParameters are preserved
   mlp2 = mlp2:clone()
 
-  local p, gp = net:getParameters()
+  local p, _ = net:getParameters()
 
   mytester:asserteq((p[{ {1,100} }] - net.modules[1].weight):norm(), 0, 'error when using partial realloc')
   mytester:asserteq((p[{ {111,210} }] - net.modules[2].weight):norm(), 0, 'error when using partial realloc')
@@ -2407,7 +2406,7 @@ function nntest.FlattenTable()
    -- CASE 1: Nothing changes so the output table shouldn't be redefined
    local old_input_map = m.input_map
    local old_output = m.output
-   output = m:forward(input)
+   local _ = m:forward(input)
    mytester:assert(old_input_map == m.input_map and old_output == m.output)
 
    -- CASE 2: An element is added to the input table
@@ -2449,7 +2448,7 @@ function nntest.L1Penalty()
    local input = torch.rand(2,10):add(-0.5)
    input[1][1] = 0
 
-   local out = m:forward(input)
+   local _ = m:forward(input)
    local grad = m:backward(input, torch.ones(input:size()))
 
    local err = input:clone():abs():sum()*weight - m.loss
@@ -2482,7 +2481,6 @@ function nntest.DepthConcat()
    local output = torch.Tensor(2, outputSize:sum(), 12, 12):zero() -- zero for padding
    local narrows = { {{},{1,5},{},{}}, {{},{6,11},{2,11},{2,11}}, {{},{12,18},{2,10},{2,10}}, {{},{19,26},{3,10},{3,10}} }
    local gradInput = input:clone():zero()
-   local gradWeights = {}
    for i=1,4 do
       local conv = concat:get(i)
       local gradWeight = conv.gradWeight:clone()