Repeater unit tests

4 files changed, 56 insertions, 11 deletions

diff --git a/AbstractRecurrent.lua b/AbstractRecurrent.lua
index af5cba0..cf40626 100644
--- a/AbstractRecurrent.lua
+++ b/AbstractRecurrent.lua
@@ -30,7 +30,7 @@ local function recursiveResizeAs(t1,t2)
          t1[key], t2[key] = recursiveResizeAs(t1[key], t2[key])
       end
    elseif torch.isTensor(t2) then
-      t1 = t1 or t2.new()
+      t1 = torch.isTensor(t1) and t1 or t2.new()
       t1:resizeAs(t2)
    else
       error("expecting nested tensors or tables. Got "..
@@ -64,7 +64,7 @@ local function recursiveCopy(t1,t2)
          t1[key], t2[key] = recursiveCopy(t1[key], t2[key])
       end
    elseif torch.isTensor(t2) then
-      t1 = t1 or t2.new()
+      t1 = torch.isTensor(t1) and t1 or t2.new()
       t1:resizeAs(t2):copy(t2)
    else
       error("expecting nested tensors or tables. Got "..
diff --git a/Repeater.lua b/Repeater.lua
index 3cf7f86..68ea41b 100644
--- a/Repeater.lua
+++ b/Repeater.lua
@@ -6,11 +6,12 @@
 ------------------------------------------------------------------------
 local Repeater, parent = torch.class("nn.Repeater", "nn.Container")
 
-function Repeater:__init(nStep, rnn)
+function Repeater:__init(rnn, nStep)
    parent.__init(self)
+   assert(torch.type(nStep) == 'number', "expecting number value for arg 2")
    self.nStep = nStep
    self.rnn = rnn
-   assert(rnn.backwardThroughTime, "expecting AbstractRecurrent instance for arg 2")
+   assert(rnn.backwardThroughTime, "expecting AbstractRecurrent instance for arg 1")
    self.modules[1] = rnn
    self.output = {}
 end
@@ -23,6 +24,9 @@ function Repeater:updateOutput(input)
    return self.output
 end
 
+local recursiveAdd = nn.AbstractRecurrent.recursiveAdd
+local recursiveCopy = nn.AbstractRecurrent.recursiveCopy
+
 function Repeater:updateGradInput(input, gradOutput)
    assert(self.rnn.step - 1 == self.nStep, "inconsistent rnn steps")
    assert(torch.type(gradOutput) == 'table', "expecting gradOutput table")
@@ -33,7 +37,15 @@ function Repeater:updateGradInput(input, gradOutput)
    end
    -- back-propagate through time (BPTT)
    self.rnn:updateGradInputThroughTime()
-   self.gradInput = self.rnn.gradInputs
+   
+   for i,currentGradInput in ipairs(self.rnn.gradInputs) do
+      if i == 1 then
+         self.gradInput = recursiveCopy(self.gradInput, currentGradInput)
+      else
+         recursiveAdd(self.gradInput, currentGradInput)
+      end
+   end
+   
    return self.gradInput
 end
 
@@ -66,9 +78,9 @@ function Repeater:__tostring__()
    local line = '\n'
    local str = torch.type(self) .. ' {' .. line
    str = str .. tab .. '[  input,    input,  ...,  input  ]'.. line
-   str = str .. tab .. '               V                   '.. line
+   str = str .. tab .. '     V         V             V     '.. line
    str = str .. tab .. tostring(self.modules[1]):gsub(line, line .. tab) .. line
-   str = str .. tab .. '               V                   '.. line
+   str = str .. tab .. '     V         V             V     '.. line
    str = str .. tab .. '[output(1),output(2),...,output('..self.nStep..')]' .. line
    str = str .. '}'
    return str
diff --git a/Sequencer.lua b/Sequencer.lua
index f6e8bd0..97f9d6c 100644
--- a/Sequencer.lua
+++ b/Sequencer.lua
@@ -188,9 +188,9 @@ function Sequencer:__tostring__()
    local line = '\n'
    local str = torch.type(self) .. ' {' .. line
    str = str .. tab .. '[input(1), input(2), ..., input(T)]'.. line
-   str = str .. tab .. '               V                   '.. line
+   str = str .. tab .. '   V           V            V      '.. line
    str = str .. tab .. tostring(self.modules[1]):gsub(line, line .. tab) .. line
-   str = str .. tab .. '               V                   '.. line
+   str = str .. tab .. '   V           V            V      '.. line
    str = str .. tab .. '[output(1),output(2),...,output(T)]' .. line
    str = str .. '}'
    return str
diff --git a/test/test-all.lua b/test/test-all.lua
index 265b14d..bf489a3 100644
--- a/test/test-all.lua
+++ b/test/test-all.lua
@@ -725,8 +725,41 @@ function nnxtest.Sequencer()
       mytester:assertTensorEq(outputs3[step], output, 0.00001, "Sequencer output "..step)
       mytester:assertTensorEq(gradInputs3[step], rnn.gradInputs[step], 0.00001, "Sequencer gradInputs "..step)
    end
-   print""
-   print(rnn3)
+end
+
+function nnxtest.Repeater()
+   local batchSize = 4
+   local inputSize = 10
+   local outputSize = 7
+   local nSteps = 5 
+   local inputModule = nn.Linear(inputSize, outputSize)
+   local transferModule = nn.Sigmoid()
+   -- test MLP feedback Module (because of Module:representations())
+   local feedbackModule = nn.Linear(outputSize, outputSize)
+   -- rho = nSteps
+   local rnn = nn.Recurrent(outputSize, inputModule, feedbackModule, transferModule, nSteps)
+   local rnn2 = rnn:clone()
+   
+   local inputs, outputs, gradOutputs = {}, {}, {}
+   local input = torch.randn(batchSize, inputSize)
+   for step=1,nSteps do
+      outputs[step] = rnn:forward(input)
+      gradOutputs[step] = torch.randn(batchSize, outputSize)
+      rnn:backward(input, gradOutputs[step])
+   end
+   rnn:backwardThroughTime()
+   
+   local rnn3 = nn.Repeater(rnn2, nSteps)
+   local outputs3 = rnn3:forward(input)
+   local gradInput3 = rnn3:backward(input, gradOutputs)
+   mytester:assert(#outputs3 == #outputs, "Repeater output size err")
+   mytester:assert(#outputs3 == #rnn.gradInputs, "Repeater gradInputs size err")
+   local gradInput = rnn.gradInputs[1]:clone():zero()
+   for step,output in ipairs(outputs) do
+      mytester:assertTensorEq(outputs3[step], output, 0.00001, "Sequencer output "..step)
+      gradInput:add(rnn.gradInputs[step])
+   end
+   mytester:assertTensorEq(gradInput3, gradInput, 0.00001, "Repeater gradInput err")
 end
 
 function nnxtest.SpatialNormalization_Gaussian2D()