Merge branch 'master' into mergeopenmp

Conflicts:
	extra/openmp/pkg/openmp/generic/SpatialMaxPoolingOmp.c
	extra/openmp/pkg/openmp/generic/SpatialSubSamplingOmp.c
	extra/openmp/pkg/openmp/generic/SqrtOmp.c
	extra/openmp/pkg/openmp/generic/SquareOmp.c

10 files changed, 328 insertions, 12 deletions

diff --git a/DistKLDivCriterion.lua b/DistKLDivCriterion.lua
new file mode 100644
index 0000000..b6b6216
--- /dev/null
+++ b/DistKLDivCriterion.lua
@@ -0,0 +1,64 @@
+local DistKLDivCriterion, parent = torch.class('nn.DistKLDivCriterion', 'nn.Criterion')
+
+local epsilon = 1e-100
+
+function DistKLDivCriterion:__init()
+   parent.__init(self)
+   self.sizeAverage = true
+end
+
+function DistKLDivCriterion:updateOutput(input, target)
+   local log = math.log
+   if input:dim() == 1 then
+      self.output = 0
+      for i = 1,input:size(1) do
+         local acc = 0
+         if target[i] > 0 then
+            acc = target[i] * (log(target[i]) - input[i])
+         end
+         self.output = self.output + acc
+      end
+   elseif input:dim() == 2 then
+      self.output = 0
+      for i=1,target:size(1) do
+         local tar = target[i]
+         local inp = input[i]
+         for i = 1,inp:size(1) do
+            local acc = 0
+            if tar[i] > epsilon then
+               acc = tar[i] * (log(tar[i]) - inp[i])
+            end
+            self.output = self.output + acc
+         end
+      end
+      if self.sizeAverage then
+         self.output = self.output / target:size(1)
+      end
+   else
+      error('matrix or vector expected')
+   end
+   return self.output
+end
+
+function DistKLDivCriterion:updateGradInput(input, target)
+   local gradInput = self.gradInput
+   gradInput:resizeAs(input)
+
+   if input:dim() == 1 then
+      for i = 1,input:size(1) do
+         gradInput[i] = -target[i]
+      end
+   else
+      for i=1,target:size(1) do
+         local tar = target[i]
+         for i = 1,tar:size(1) do
+            gradInput[i] = -tar[i]
+         end
+      end
+      if self.sizeAverage then
+         gradInput:div(target:size(1))
+      end
+   end
+
+   return self.gradInput
+end
diff --git a/Log.lua b/Log.lua
new file mode 100644
index 0000000..fec4664
--- /dev/null
+++ b/Log.lua
@@ -0,0 +1,20 @@
+local Log, parent = torch.class('nn.Log', 'nn.Module')
+
+function Log:__init(inputSize)
+   parent.__init(self)
+end
+
+function Log:updateOutput(input)
+   self.output:resizeAs(input)
+   self.output:copy(input)
+   self.output:log()
+   return self.output 
+end
+
+function Log:updateGradInput(input, gradOutput) 
+   self.gradInput:resizeAs(input)
+   self.gradInput:fill(1)
+   self.gradInput:cdiv(input)
+   self.gradInput:cmul(gradOutput)
+   return self.gradInput
+end
diff --git a/LookupTable.lua b/LookupTable.lua
index 115f19c..7dfda7a 100644
--- a/LookupTable.lua
+++ b/LookupTable.lua
@@ -4,9 +4,10 @@ LookupTable.__version = 2
 
 function LookupTable:__init(nIndex, ...)
    parent.__init(self)
+   local arg = {...}
 
-   if select('#', ...) == 1 and type(select(1, ...)) ~= "number" then
-      local size = select(1, ...)
+   if select('#', ...) == 1 and type(arg[1]) ~= "number" then
+      local size = arg[1]
       self.size = torch.LongStorage(#size + 1)
       for i=1,#size do
          self.size[i+1] = size[i]
@@ -14,7 +15,7 @@ function LookupTable:__init(nIndex, ...)
    else
       self.size = torch.LongStorage(select('#', ...)+1)
       for i=1,select('#',...) do
-         self.size[i+1] = select(i, ...)
+         self.size[i+1] = arg[i]
       end
    end
 
diff --git a/Module.lua b/Module.lua
index dd61dc6..8235558 100644
--- a/Module.lua
+++ b/Module.lua
@@ -81,6 +81,7 @@ function Module:updateParameters(learningRate)
 end
 
 function Module:share(mlp, ...)
+   local arg = {...}
    for i,v in ipairs(arg) do
       if self[v] ~= nil then
          self[v]:set(mlp[v])
diff --git a/Reshape.lua b/Reshape.lua
index 0be793f..753dd17 100644
--- a/Reshape.lua
+++ b/Reshape.lua
@@ -2,21 +2,25 @@ local Reshape, parent = torch.class('nn.Reshape', 'nn.Module')
 
 function Reshape:__init(...)
    parent.__init(self)
+   local arg = {...}
+
    self.size = torch.LongStorage()
    self.batchsize = torch.LongStorage()
-   local n = select('#', ...)
-   if n == 1 and torch.typename(select(1, ...)) == 'torch.LongStorage' then
-      self.size:resize(#select(1, ...)):copy(select(1, ...))
+   local n = #arg
+   if n == 1 and torch.typename(arg[1]) == 'torch.LongStorage' then
+      self.size:resize(#arg[1]):copy(arg[1])
    else
       self.size:resize(n)
-      self.batchsize:resize(n+1)
-      self.nelement = 1
       for i=1,n do
-         self.size[i] = select(i, ...)
-         self.batchsize[i+1] = select(i, ...)
-         self.nelement = self.nelement * self.size[i]
+         self.size[i] = arg[i]
       end
    end
+   self.nelement = 1
+   self.batchsize:resize(#self.size+1)
+   for i=1,#self.size do
+      self.nelement = self.nelement * self.size[i]
+      self.batchsize[i+1] = self.size[i]
+   end
 end
 
 function Reshape:updateOutput(input)
diff --git a/SpatialContrastiveNormalization.lua b/SpatialContrastiveNormalization.lua
new file mode 100644
index 0000000..262d3b1
--- /dev/null
+++ b/SpatialContrastiveNormalization.lua
@@ -0,0 +1,42 @@
+local SpatialContrastiveNormalization, parent = torch.class('nn.SpatialContrastiveNormalization','nn.Module')
+
+function SpatialContrastiveNormalization:__init(nInputPlane, kernel, threshold, thresval)
+   parent.__init(self)
+
+   -- get args
+   self.nInputPlane = nInputPlane or 1
+   self.kernel = kernel or torch.Tensor(9,9):fill(1)
+   self.threshold = threshold or 1e-4
+   self.thresval = thresval or 1e-4
+   local kdim = self.kernel:nDimension()
+
+   -- check args
+   if kdim ~= 2 and kdim ~= 1 then
+      error('<SpatialContrastiveNormalization> averaging kernel must be 2D or 1D')
+   end
+   if (self.kernel:size(1) % 2) == 0 or (kdim == 2 and (self.kernel:size(2) % 2) == 0) then
+      error('<SpatialContrastiveNormalization> averaging kernel must have ODD dimensions')
+   end
+
+   -- instantiate sub+div normalization
+   self.normalizer = nn.Sequential()
+   self.normalizer:add(nn.SpatialSubtractiveNormalization(self.nInputPlane, self.kernel))
+   self.normalizer:add(nn.SpatialDivisiveNormalization(self.nInputPlane, self.kernel,
+                                                       self.threshold, self.threshval))
+end
+
+function SpatialContrastiveNormalization:updateOutput(input)
+   self.output = self.normalizer:forward(input)
+   return self.output
+end
+
+function SpatialContrastiveNormalization:updateGradInput(input, gradOutput)
+   self.gradInput = self.normalizer:backward(input, gradOutput)
+   return self.gradInput
+end
+
+function SpatialContrastiveNormalization:type(type)
+   parent.type(self,type)
+   self.normalizer:type(type)
+   return self
+end
diff --git a/SpatialDivisiveNormalization.lua b/SpatialDivisiveNormalization.lua
new file mode 100644
index 0000000..33668e9
--- /dev/null
+++ b/SpatialDivisiveNormalization.lua
@@ -0,0 +1,133 @@
+local SpatialDivisiveNormalization, parent = torch.class('nn.SpatialDivisiveNormalization','nn.Module')
+
+function SpatialDivisiveNormalization:__init(nInputPlane, kernel, threshold, thresval)
+   parent.__init(self)
+
+   -- get args
+   self.nInputPlane = nInputPlane or 1
+   self.kernel = kernel or torch.Tensor(9,9):fill(1)
+   self.threshold = threshold or 1e-4
+   self.thresval = thresval or 1e-4
+   local kdim = self.kernel:nDimension()
+
+   -- check args
+   if kdim ~= 2 and kdim ~= 1 then
+      error('<SpatialDivisiveNormalization> averaging kernel must be 2D or 1D')
+   end
+   if (self.kernel:size(1) % 2) == 0 or (kdim == 2 and (self.kernel:size(2) % 2) == 0) then
+      error('<SpatialDivisiveNormalization> averaging kernel must have ODD dimensions')
+   end
+
+   -- padding values
+   local padH = math.floor(self.kernel:size(1)/2)
+   local padW = padH
+   if kdim == 2 then
+      padW = math.floor(self.kernel:size(2)/2)
+   end
+
+   -- create convolutional mean estimator
+   self.meanestimator = nn.Sequential()
+   self.meanestimator:add(nn.SpatialZeroPadding(padW, padW, padH, padH))
+   if kdim == 2 then
+      self.meanestimator:add(nn.SpatialConvolutionMap(nn.tables.oneToOne(self.nInputPlane),
+                                                      self.kernel:size(2), self.kernel:size(1)))
+   else
+      self.meanestimator:add(nn.SpatialConvolutionMap(nn.tables.oneToOne(self.nInputPlane),
+                                                      self.kernel:size(1), 1))
+      self.meanestimator:add(nn.SpatialConvolutionMap(nn.tables.oneToOne(self.nInputPlane),
+                                                      1, self.kernel:size(1)))
+   end
+   self.meanestimator:add(nn.Sum(1))
+   self.meanestimator:add(nn.Replicate(self.nInputPlane))
+
+   -- create convolutional std estimator
+   self.stdestimator = nn.Sequential()
+   self.stdestimator:add(nn.Square())
+   self.stdestimator:add(nn.SpatialZeroPadding(padW, padW, padH, padH))
+   if kdim == 2 then
+      self.stdestimator:add(nn.SpatialConvolutionMap(nn.tables.oneToOne(self.nInputPlane),
+                                                     self.kernel:size(2), self.kernel:size(1)))
+   else
+      self.stdestimator:add(nn.SpatialConvolutionMap(nn.tables.oneToOne(self.nInputPlane),
+                                                     self.kernel:size(1), 1))
+      self.stdestimator:add(nn.SpatialConvolutionMap(nn.tables.oneToOne(self.nInputPlane),
+                                                     1, self.kernel:size(1)))
+   end
+   self.stdestimator:add(nn.Sum(1))
+   self.stdestimator:add(nn.Replicate(self.nInputPlane))
+   self.stdestimator:add(nn.Sqrt())
+
+   -- set kernel and bias
+   if kdim == 2 then
+      self.kernel:div(self.kernel:sum() * self.nInputPlane)
+      for i = 1,self.nInputPlane do 
+         self.meanestimator.modules[2].weight[i] = self.kernel
+         self.stdestimator.modules[3].weight[i] = self.kernel
+      end
+      self.meanestimator.modules[2].bias:zero()
+      self.stdestimator.modules[3].bias:zero()
+   else
+      self.kernel:div(self.kernel:sum() * math.sqrt(self.nInputPlane))
+      for i = 1,self.nInputPlane do 
+         self.meanestimator.modules[2].weight[i]:copy(self.kernel)
+         self.meanestimator.modules[3].weight[i]:copy(self.kernel)
+         self.stdestimator.modules[3].weight[i]:copy(self.kernel)
+         self.stdestimator.modules[4].weight[i]:copy(self.kernel)
+      end
+      self.meanestimator.modules[2].bias:zero()
+      self.meanestimator.modules[3].bias:zero()
+      self.stdestimator.modules[3].bias:zero()
+      self.stdestimator.modules[4].bias:zero()
+   end
+
+   -- other operation
+   self.normalizer = nn.CDivTable()
+   self.divider = nn.CDivTable()
+   self.thresholder = nn.Threshold(self.threshold, self.thresval)
+
+   -- coefficient array, to adjust side effects
+   self.coef = torch.Tensor(1,1,1)
+end
+
+function SpatialDivisiveNormalization:updateOutput(input)
+   -- compute side coefficients
+   if (input:size(3) ~= self.coef:size(3)) or (input:size(2) ~= self.coef:size(2)) then
+      local ones = input.new():resizeAs(input):fill(1)
+      self.coef = self.meanestimator:updateOutput(ones)
+      self.coef = self.coef:clone()
+   end
+
+   -- normalize std dev
+   self.localstds = self.stdestimator:updateOutput(input)
+   self.adjustedstds = self.divider:updateOutput{self.localstds, self.coef}
+   self.thresholdedstds = self.thresholder:updateOutput(self.adjustedstds)
+   self.output = self.normalizer:updateOutput{input, self.thresholdedstds}
+
+   -- done
+   return self.output
+end
+
+function SpatialDivisiveNormalization:updateGradInput(input, gradOutput)
+   -- resize grad
+   self.gradInput:resizeAs(input):zero()
+
+   -- backprop through all modules
+   local gradnorm = self.normalizer:updateGradInput({input, self.thresholdedstds}, gradOutput)
+   local gradadj = self.thresholder:updateGradInput(self.adjustedstds, gradnorm[2])
+   local graddiv = self.divider:updateGradInput({self.localstds, self.coef}, gradadj)
+   self.gradInput:add(self.stdestimator:updateGradInput(input, graddiv[1]))
+   self.gradInput:add(gradnorm[1])
+
+   -- done
+   return self.gradInput
+end
+
+function SpatialDivisiveNormalization:type(type)
+   parent.type(self,type)
+   self.meanestimator:type(type)
+   self.stdestimator:type(type)
+   self.divider:type(type)
+   self.normalizer:type(type)
+   self.thresholder:type(type)
+   return self
+end
diff --git a/dok/index.dok b/dok/index.dok
index 5a112e5..a687db3 100644
--- a/dok/index.dok
+++ b/dok/index.dok
@@ -61,7 +61,7 @@ Some important remarks:
 Building a simple neural network can be achieved by constructing an available layer.
 A linear neural network (perceptron!) is built only in one line:
 <file lua>
-nn = nn.Linear(10,1) -- perceptron with 10 inputs
+mlp = nn.Linear(10,1) -- perceptron with 10 inputs
 </file>
 
 More complex neural networks are easily built using container classes
diff --git a/init.lua b/init.lua
index c6e7df0..c1c7b79 100644
--- a/init.lua
+++ b/init.lua
@@ -35,6 +35,7 @@ torch.include('nn', 'CosineDistance.lua')
 torch.include('nn', 'DotProduct.lua')  
 
 torch.include('nn', 'Exp.lua')
+torch.include('nn', 'Log.lua')
 torch.include('nn', 'HardTanh.lua')
 torch.include('nn', 'LogSigmoid.lua')
 torch.include('nn', 'LogSoftMax.lua')
@@ -61,6 +62,8 @@ torch.include('nn', 'SpatialLPPooling.lua')
 torch.include('nn', 'TemporalConvolution.lua')
 torch.include('nn', 'TemporalSubSampling.lua')
 torch.include('nn', 'SpatialSubtractiveNormalization.lua')
+torch.include('nn', 'SpatialDivisiveNormalization.lua')
+torch.include('nn', 'SpatialContrastiveNormalization.lua')
 torch.include('nn', 'SpatialZeroPadding.lua')
 
 torch.include('nn', 'VolumetricConvolution.lua')
@@ -77,6 +80,7 @@ torch.include('nn', 'MSECriterion.lua')
 torch.include('nn', 'MarginCriterion.lua')
 torch.include('nn', 'AbsCriterion.lua')
 torch.include('nn', 'ClassNLLCriterion.lua')
+torch.include('nn', 'DistKLDivCriterion.lua')
 torch.include('nn', 'MultiCriterion.lua')
 torch.include('nn', 'L1HingeEmbeddingCriterion.lua')
 torch.include('nn', 'HingeEmbeddingCriterion.lua')
diff --git a/test/test.lua b/test/test.lua
index 112f041..b8536ca 100644
--- a/test/test.lua
+++ b/test/test.lua
@@ -75,6 +75,21 @@ function nntest.Exp()
    mytester:asserteq(berr, 0, torch.typename(module) .. ' - i/o backward err ')
 end
 
+function nntest.Log()
+   local ini = math.random(10,20)
+   local inj = math.random(10,20)
+   local ink = math.random(10,20)
+   local input = torch.Tensor(ini,inj,ink):zero()
+   local module = nn.Log()
+
+   local err = jac.testJacobian(module,input)
+   mytester:assertlt(err,precision, 'error on state ')
+
+   local ferr,berr = jac.testIO(module,input)
+   mytester:asserteq(ferr, 0, torch.typename(module) .. ' - i/o forward err ')
+   mytester:asserteq(berr, 0, torch.typename(module) .. ' - i/o backward err ')
+end
+
 function nntest.HardTanh()
    local ini = math.random(5,10)
    local inj = math.random(5,10)
@@ -542,6 +557,38 @@ function nntest.SpatialSubtractiveNormalization_1dkernel()
    mytester:asserteq(berr, 0, torch.typename(module) .. ' - i/o backward err ')
 end
 
+function nntest.SpatialDivisiveNormalization_2dkernel()
+   local inputSize = math.random(11,20)
+   local kersize = 9
+   local nbfeatures = math.random(5,10)
+   local kernel = torch.Tensor(kersize,kersize):fill(1)
+   local module = nn.SpatialDivisiveNormalization(nbfeatures,kernel)
+   local input = torch.rand(nbfeatures,inputSize,inputSize)
+
+   local err = jac.testJacobian(module,input)
+   mytester:assertlt(err,precision, 'error on state ')
+
+   local ferr,berr = jac.testIO(module,input)
+   mytester:asserteq(ferr, 0, torch.typename(module) .. ' - i/o forward err ')
+   mytester:asserteq(berr, 0, torch.typename(module) .. ' - i/o backward err ')
+end
+
+function nntest.SpatialDivisiveNormalization_1dkernel()
+   local inputSize = math.random(11,20)
+   local kersize = 9
+   local nbfeatures = math.random(5,10)
+   local kernel = torch.Tensor(kersize):fill(1)
+   local module = nn.SpatialDivisiveNormalization(nbfeatures,kernel)
+   local input = torch.rand(nbfeatures,inputSize,inputSize)
+
+   local err = jac.testJacobian(module,input)
+   mytester:assertlt(err,precision, 'error on state ')
+
+   local ferr,berr = jac.testIO(module,input)
+   mytester:asserteq(ferr, 0, torch.typename(module) .. ' - i/o forward err ')
+   mytester:asserteq(berr, 0, torch.typename(module) .. ' - i/o backward err ')
+end
+
 function nntest.SpatialConvolution()
    local from = math.random(1,10)
    local to = math.random(1,10)