move dpnn modules

AbstractRecurrent.lua

@@ -33,6 +33,9 @@ function AbstractRecurrent:getStepModule(step)
 end
 
 function AbstractRecurrent:updateOutput(input)
+   if self.train ~= false then
+      self:recycle()
+   end
    if self.zeroMask then
       -- where zeroMask = 1, the past is forgotten, that is, the output/gradOutput is zeroed
       local stepmodule = (self.train==false) and self.modules[1] or self:getStepModule(self.step)
@@ -189,6 +192,7 @@ end
 
 function AbstractRecurrent:maskZero(v1)
    if not self.maskzero then
+      assert(not torch.isTypeOf(self.modules[1], 'nn.AbstractRecurrent'), "Doesn't support zero-masking on nested AbstractRecurrent instances")
       self.maskzero = true
       local stepmodule = nn.MaskZero(self.modules[1], v1)
       self.sharedClones = {stepmodule}

AbstractSequencerCriterion.lua

@@ -46,3 +46,9 @@ function AbstractSequencerCriterion:setZeroMask(zeroMask)
    end
 end
 
+function AbstractSequencerCriterion:type(type, typecache)
+   for key, clone in pairs(self.clones) do
+      clone:type(type, typecache)
+   end
+   return parent.type(self, type, typecache)
+end

BatchNormalization.lua

@@ -1,16 +1,6 @@
 local _ = require 'moses'
 local BN, parent = nn.BatchNormalization, nn.Module
 
-local empty = _.clone(parent.dpnn_mediumEmpty)
-table.insert(empty, 'buffer')
-table.insert(empty, 'buffer2')
-table.insert(empty, 'centered')
-table.insert(empty, 'std')
-table.insert(empty, 'normalized')
-table.insert(empty, 'output')
-table.insert(empty, 'gradInput')
-BN.dpnn_mediumEmpty = empty
-
 -- for sharedClone
 local params = _.clone(parent.dpnn_parameters)
 table.insert(params, 'running_mean')

CMakeLists.txt

@@ -17,21 +17,15 @@ SET(luasrc
   AbstractSequencer.lua
   AbstractSequencerCriterion.lua
   BiSequencer.lua
-  deprecated/BiSequencerLM.lua
   CopyGrad.lua
   Dropout.lua
   ExpandAs.lua
-  deprecated/FastLSTM.lua
-  deprecated/GRU.lua
-  LinearNoBias.lua
   LookupTableMaskZero.lua
-  deprecated/LSTM.lua
   MaskZero.lua
   MaskZeroCriterion.lua
   Module.lua
   Mufuru.lua
   NormStabilizer.lua
-  Padding.lua
   Recurrence.lua
   RecurrentAttention.lua
   Recursor.lua
@@ -42,11 +36,8 @@ SET(luasrc
   SeqBLSTM.lua
   SeqGRU.lua
   SeqLSTM.lua
-  deprecated/SeqLSTMP.lua
-  deprecated/SeqReverseSequence.lua
   Sequencer.lua
   SequencerCriterion.lua
-  ZeroGrad.lua
   test/bigtest.lua
   test/test.lua
   VariableLength.lua
@@ -60,52 +51,30 @@ SET(luasrc
   ArgMax.lua
   BatchNormalization.lua
   BinaryClassReward.lua
-  BinaryLogisticRegression.lua
-  CAddTensorTable.lua
   CategoricalEntropy.lua
-  Clip.lua
-  Collapse.lua
-  Constant.lua
   Container.lua
-  Convert.lua
   Criterion.lua
-  Dictionary.lua
-  FireModule.lua
-  Inception.lua
-  Kmeans.lua
   LookupTable.lua
-  ModuleCriterion.lua
   NCECriterion.lua
   NCEModule.lua
-  OneHot.lua
-  PCAColorTransform.lua
   ParallelTable.lua
-  PrintSize.lua
   Reinforce.lua
   ReinforceBernoulli.lua
   ReinforceCategorical.lua
   ReinforceGamma.lua
   ReinforceNormal.lua
   ReverseSequence.lua
   Sequential.lua
-  Serial.lua
-  SimpleColorTransform.lua
-  SpatialBatchNormalization.lua
-  SpatialBinaryConvolution.lua
-  SpatialBinaryLogisticRegression.lua
-  SpatialConvolution.lua
-  SpatialConvolutionMM.lua
-  SpatialFeatNormalization.lua
   SpatialGlimpse.lua
-  SpatialMaxPooling.lua
-  SpatialRegionDropout.lua
-  SpatialUniformCrop.lua
   TotalDropout.lua
   VRClassReward.lua
-  WhiteNoise.lua
-  ZipTable.lua
-  ZipTableOneToMany.lua
   ReverseUnreverse.lua
+  deprecated/SeqLSTMP.lua
+  deprecated/SeqReverseSequence.lua
+  deprecated/BiSequencerLM.lua
+  deprecated/FastLSTM.lua
+  deprecated/GRU.lua
+  deprecated/LSTM.lua
 )
 
 ADD_TORCH_PACKAGE(rnn "${src}" "${luasrc}" "An RNN library for Torch")