ReverseTable+SeqReverseSequence -> ReverseSequnece

CMakeLists.txt

@@ -42,7 +42,7 @@ SET(luasrc
   SeqGRU.lua
   SeqLSTM.lua
   deprecated/SeqLSTMP.lua
-  SeqReverseSequence.lua
+  deprecated/SeqReverseSequence.lua
   Sequencer.lua
   SequencerCriterion.lua
   ZeroGrad.lua
@@ -85,7 +85,7 @@ SET(luasrc
   ReinforceCategorical.lua
   ReinforceGamma.lua
   ReinforceNormal.lua
-  ReverseTable.lua
+  ReverseSequence.lua
   Sequential.lua
   Serial.lua
   SimpleColorTransform.lua

ReverseSequence.lua

@@ -0,0 +1,75 @@
+local ReverseSequence, parent = torch.class("nn.ReverseSequence", "nn.Module")
+
+function ReverseSequence:updateOutput(input)
+   local seqlen
+   if torch.isTensor(input) then
+      seqlen = input:size(1)
+      self.output = torch.isTensor(self.output) and self.output or input.new()
+      self.output:resizeAs(input)
+
+      self._range = self._range or torch.isCudaTensor(input) and torch.CudaLongTensor() or torch.LongTensor()
+      if self._range:nElement() ~= seqlen then
+         self._range:range(seqlen,1,-1)
+      end
+      self.output:index(input, 1, self._range)
+   else
+      seqlen = #input
+      self.output = torch.type(self.output) == 'table' and self.output or {}
+      assert(torch.type(input) == 'table', "Expecting table or tensor at arg 1")
+
+      -- empty output table
+      for k,v in ipairs(self.output) do
+         self.output[k] = nil
+      end
+
+      -- reverse input
+      local k = 1
+      for i=seqlen,1,-1 do
+         self.output[k] = input[i]
+         k = k + 1
+      end
+   end
+
+   return self.output
+end
+
+function ReverseSequence:updateGradInput(input, gradOutput)
+   local seqlen
+   if torch.isTensor(input) then
+      seqlen = input:size(1)
+      self.gradInput = torch.isTensor(self.gradInput) and self.gradInput or input.new()
+      self.gradInput:resizeAs(input)
+
+      self.gradInput:index(gradOutput, 1, self._range)
+   else
+      seqlen = #input
+      self.gradInput = torch.type(self.gradInput) == 'table' and self.gradInput or {}
+      assert(torch.type(gradOutput) == 'table', "Expecting table or tensor at arg 2")
+
+      -- empty gradInput table
+      for k,v in ipairs(self.gradInput) do
+         self.gradInput[k] = nil
+      end
+
+      -- reverse gradOutput
+      local k = 1
+      for i=seqlen,1,-1 do
+         self.gradInput[k] = gradOutput[i]
+         k = k + 1
+      end
+   end
+
+   return self.gradInput
+end
+
+function ReverseSequence:clearState()
+   self.gradInput = torch.Tensor()
+   self.output = torch.Tensor()
+   self._range = nil
+end
+
+function ReverseSequence:type(...)
+   self:clearState()
+   return parent.type(self, ...)
+end
+

init.lua

@@ -58,7 +58,7 @@ require('rnn.Collapse')
 require('rnn.ZipTable')
 require('rnn.ZipTableOneToMany')
 require('rnn.CAddTensorTable')
-require('rnn.ReverseTable')
+require('rnn.ReverseSequence')
 require('rnn.Dictionary')
 require('rnn.Inception')
 require('rnn.Clip')
@@ -131,7 +131,6 @@ require('rnn.RecurrentAttention')
 -- sequencer + recurrent modules
 require('rnn.SeqLSTM')
 require('rnn.SeqGRU')
-require('rnn.SeqReverseSequence')
 require('rnn.SeqBRNN')
 
 -- recurrent criterions:
@@ -144,6 +143,7 @@ require('rnn.MaskZeroCriterion')
 require('rnn.LSTM')
 require('rnn.FastLSTM')
 require('rnn.SeqLSTMP')
+require('rnn.SeqReverseSequence')
 
 -- prevent likely name conflicts
 nn.rnn = rnn

test/test.lua

@@ -4991,24 +4991,45 @@ function rnntest.CAddTensorTable()
    mytester:assertTensorEq(output[1]+output[2]+output[3], gradInput[1], 0.000001, "CAddTensorTable gradInput1")
 end
 
-function rnntest.ReverseTable()
+function rnntest.ReverseSequence()
+   -- test table
+
    -- input : { a, b, c, d }
    -- output : { c, b, a, d }
-   local r = nn.ReverseTable()
+   local r = nn.ReverseSequence()
    local input = {torch.randn(3,4), torch.randn(3,4), torch.randn(3,4), torch.randn(3,4)}
    local output = r:forward(input)
 
-   mytester:assert(#output == 4, "ReverseTable #output")
+   mytester:assert(#output == 4, "ReverseSequence #output")
    local k = 1
    for i=#input,1,-1 do
-      mytester:assertTensorEq(input[i], output[k], 0.00001, "ReverseTable output err "..k)
+      mytester:assertTensorEq(input[i], output[k], 0.00001, "ReverseSequence output err "..k)
       k = k + 1
    end
 
    local gradInput = r:backward(input, output)
-   mytester:assert(#gradInput == 4, "ReverseTable #gradInput")
+   mytester:assert(#gradInput == 4, "ReverseSequence #gradInput")
    for i=1,#input do
-      mytester:assertTensorEq(gradInput[i], input[i], 0.00001, "ReverseTable gradInput err "..i)
+      mytester:assertTensorEq(gradInput[i], input[i], 0.00001, "ReverseSequence gradInput err "..i)
+   end
+
+   -- test tensor
+
+   local r = nn.ReverseSequence()
+   local input = torch.randn(5,4,3)
+   local output = r:forward(input)
+
+   mytester:assert(output:isSameSizeAs(input), "ReverseSequence #output")
+   local k = 1
+   for i=5,1,-1 do
+      mytester:assertTensorEq(input[i], output[k], 0.00001, "ReverseSequence output err "..k)
+      k = k + 1
+   end
+
+   local gradInput = r:backward(input, output)
+   mytester:assert(gradInput:isSameSizeAs(input), "ReverseSequence #gradInput")
+   for i=1,5 do
+      mytester:assertTensorEq(gradInput[i], input[i], 0.00001, "ReverseSequence gradInput err "..i)
    end
 end