BIG UPDATE.

1 files changed, 0 insertions, 188 deletions

diff --git a/init.lua b/init.lua
index 53e6447..e8bd190 100755
--- a/init.lua
+++ b/init.lua
@@ -47,23 +47,13 @@ torch.include('nnx', 'test-all.lua')
 torch.include('nnx', 'test-omp.lua')
 
 -- tools:
-torch.include('nnx', 'ConfusionMatrix.lua')
-torch.include('nnx', 'Logger.lua')
 torch.include('nnx', 'Probe.lua')
 torch.include('nnx', 'Tic.lua')
 torch.include('nnx', 'Toc.lua')
 
--- OpenMP module:
-torch.include('nnx', 'OmpModule.lua')
-
--- pointwise modules:
-torch.include('nnx', 'Minus.lua')
-
 -- spatial (images) operators:
-torch.include('nnx', 'SpatialLinear.lua')
 torch.include('nnx', 'SpatialClassifier.lua')
 torch.include('nnx', 'SpatialPadding.lua')
-torch.include('nnx', 'SpatialNormalization.lua')
 torch.include('nnx', 'SpatialReSamplingEx.lua')
 torch.include('nnx', 'SpatialUpSampling.lua')
 torch.include('nnx', 'SpatialDownSampling.lua')
@@ -76,195 +66,17 @@ torch.include('nnx', 'SpatialMatching.lua')
 torch.include('nnx', 'SpatialRadialMatching.lua')
 torch.include('nnx', 'SpatialMaxSampling.lua')
 torch.include('nnx', 'SpatialColorTransform.lua')
-torch.include('nnx', 'SpatialConvolutionSparse.lua')
 
 -- other modules
 torch.include('nnx', 'FunctionWrapper.lua')
 
 -- criterions:
 torch.include('nnx', 'SuperCriterion.lua')
-torch.include('nnx', 'SparseCriterion.lua')
 torch.include('nnx', 'DistNLLCriterion.lua')
-torch.include('nnx', 'KLDivCriterion.lua')
 torch.include('nnx', 'DistMarginCriterion.lua')
-torch.include('nnx', 'SpatialMSECriterion.lua')
-torch.include('nnx', 'SpatialClassNLLCriterion.lua')
-torch.include('nnx', 'SpatialSparseCriterion.lua')
-
--- optimizations:
-torch.include('nnx', 'Optimization.lua')
-torch.include('nnx', 'BatchOptimization.lua')
-torch.include('nnx', 'SNESOptimization.lua')
-torch.include('nnx', 'SGDOptimization.lua')
-torch.include('nnx', 'ASGDOptimization.lua')
-torch.include('nnx', 'LBFGSOptimization.lua')
-torch.include('nnx', 'CGOptimization.lua')
-torch.include('nnx', 'newCGOptimization.lua')
-torch.include('nnx', 'GeneticSGDOptimization.lua')
-torch.include('nnx', 'DiagHessian.lua')
-
--- trainers:
-torch.include('nnx', 'Trainer.lua')
-torch.include('nnx', 'OnlineTrainer.lua')
-torch.include('nnx', 'BatchTrainer.lua')
-
--- conversion helper:
-torch.include('nnx', 'Type.lua')
 
 -- datasets:
 torch.include('nnx', 'DataSet.lua')
 torch.include('nnx', 'DataList.lua')
 torch.include('nnx', 'DataSetLabelMe.lua')
 torch.include('nnx', 'DataSetSamplingPascal.lua')
-
-
--- nn helpers:
-function nnx.empty(module)
-   if module.modules then
-      -- find submodules in classic containers 'modules'
-      for _,module in ipairs(module.modules) do
-         nnx.empty(module)
-      end
-   else
-      -- find arbitrary submodules
-      for k,entry in pairs(module) do
-         local type = torch.typename(entry)
-         if type and type:find('^nn.') then
-            nnx.empty(entry)
-         elseif type(entry) == 'table' then
-            for i,entry in ipairs(entry) do
-               local type = torch.typename(entry)
-               if type and type:find('^nn.') then
-                  nnx.empty(entry)
-               end
-            end
-         end
-      end
-   end
-   -- empty module
-   if module.output and module.output.resize then 
-      module.output:resize()
-      module.output:storage():resize(0)
-   end
-   if module.gradInput and module.gradInput.resize then 
-      module.gradInput:resize()
-      module.gradInput:storage():resize(0)
-   end
-end
-
-local function get(module, holder, params)
-   -- find submodules in classic containers 'modules'
-   if module.modules then
-      for _,module in ipairs(module.modules) do
-         get(module, holder, params)
-      end
-   else
-      -- find parameters and store them
-      for _,param in ipairs(params) do
-         if module[param] then
-            table.insert(holder, module[param])
-         end
-      end
-   end
-end
-
-function nnx.getParameters(...)
-   -- to hold all parameters found
-   local holder = {}
-   -- call recursive call
-   local modules = {...}
-   for _,module in ipairs(modules) do
-      get(module, holder, {'weight', 'bias'})
-   end
-   -- return all parameters found
-   return holder
-end
-
-function nnx.getGradParameters(...)
-   -- to hold all parameters found
-   local holder = {}
-   -- call recursive call
-   local modules = {...}
-   for _,module in ipairs(modules) do
-      get(module, holder, {'gradWeight', 'gradBias'})
-   end
-   -- return all parameters found
-   return holder
-end
-
-function nnx.getDiagHessianParameters(...)
-   -- to hold all parameters found
-   local holder = {}
-   -- call recursive call
-   local modules = {...}
-   for _,module in ipairs(modules) do
-      get(module, holder, {'diagHessianWeight', 'diagHessianBias'})
-   end
-   -- return all parameters found
-   return holder
-end
-
-function nnx.flattenParameters(parameters)
-   -- already flat ?
-   local flat = true
-   for k = 2,#parameters do
-      if parameters[k]:storage() ~= parameters[k-1]:storage() then
-         flat = false
-         break
-      end
-   end
-   if flat then
-      local nParameters = 0
-      for k,param in ipairs(parameters) do
-         nParameters = nParameters + param:nElement()
-      end
-      flatParameters = parameters[1].new(parameters[1]:storage())
-      if nParameters ~= flatParameters:nElement() then
-         error('<nnx.flattenParameters> weird parameters')
-      end
-      return flatParameters
-   end
-   -- compute offsets of each parameter
-   local offsets = {}
-   local sizes = {}
-   local strides = {}
-   local elements = {}
-   local storageOffsets = {}
-   local params = {}
-   local nParameters = 0
-   for k,param in ipairs(parameters) do
-      table.insert(offsets, nParameters+1)
-      table.insert(sizes, param:size())
-      table.insert(strides, param:stride())
-      table.insert(elements, param:nElement())
-      table.insert(storageOffsets, param:storageOffset())
-      local isView = false
-      for i = 1,k-1 do
-         if param:storage() == parameters[i]:storage() then
-            offsets[k] = offsets[i]
-            if storageOffsets[k] ~= storageOffsets[i] or elements[k] ~= elements[i] then
-               error('<nnx.flattenParameters> cannot flatten shared weights with different structures')
-            end
-            isView = true
-            break
-         end
-      end
-      if not isView then
-         nParameters = nParameters + param:nElement()
-      end
-   end
-   -- create flat vector
-   local flatParameters = parameters[1].new(nParameters)
-   local storage = flatParameters:storage()
-   -- reallocate all parameters in flat vector
-   for i = 1,#parameters do
-      local data = parameters[i]:clone()
-      parameters[i]:set(storage, offsets[i], elements[i]):resize(sizes[i],strides[i]):copy(data)
-      data = nil
-      collectgarbage()
-   end
-   -- cleanup
-   collectgarbage()
-   -- return new flat vector that contains all discrete parameters
-   return flatParameters
-end