updated ConcatTable so that it works with table inputs as well as tensors.

removed a temporary line.

added a test for getParameters to ConcatTable.

2 files changed, 76 insertions, 2 deletions

diff --git a/ConcatTable.lua b/ConcatTable.lua
index c42776d..de61ca5 100644
--- a/ConcatTable.lua
+++ b/ConcatTable.lua
@@ -30,9 +30,32 @@ function ConcatTable:updateGradInput(input, gradOutput)
    for i,module in ipairs(self.modules) do
       local currentGradInput = module:updateGradInput(input, gradOutput[i])
       if i == 1 then
-         self.gradInput:resizeAs(currentGradInput):copy(currentGradInput)
+         if type(input) == 'table' then
+            assert(type(currentGradInput) == 'table', 
+              'currentGradInput is not a table!')
+            assert(#input == #currentGradInput, 
+              'table size mismatch')
+            -- gradInput is also a table
+            self.gradInput = {}
+            for j = 1, #currentGradInput do
+               self.gradInput[j] = currentGradInput[j]:clone()
+            end
+         else
+            -- gradInput is a tensor
+            self.gradInput:resizeAs(currentGradInput):copy(currentGradInput)
+         end
       else
-         self.gradInput:add(currentGradInput)
+         if type(input) == 'table' then
+            assert(type(currentGradInput) == 'table', 
+               'currentGradInput is not a table!')
+            assert(#input == #currentGradInput, 
+               'table size mismatch')
+            for j = 1, #self.gradInput do
+               self.gradInput[j]:add(currentGradInput[j])
+            end
+         else
+            self.gradInput:add(currentGradInput)
+         end
       end
    end
    return self.gradInput
diff --git a/test/test.lua b/test/test.lua
index c88c908..9ecc923 100644
--- a/test/test.lua
+++ b/test/test.lua
@@ -1920,6 +1920,57 @@ function nntest.SpatialUpSamplingNearest()
   end
 end
 
+function nntest.ConcatTable()
+   -- Test tensor input
+   local input = torch.rand(10, 10, 10)
+   local m = nn.Sequential()
+   
+   local concat = nn.ConcatTable()
+   concat:add(nn.Identity())
+   
+   m:add(concat)  -- Output of concat is a table of length 1
+   m:add(nn.JoinTable(1))  -- jac needs a tensor tensor output
+
+   local err = jac.testJacobian(m, input)
+   mytester:assertlt(err, precision, ' error on state ')
+
+   local ferr, berr = jac.testIO(m, input)
+   mytester:asserteq(ferr, 0, torch.typename(m)..' - i/o forward err ')
+   mytester:asserteq(berr, 0, torch.typename(m)..' - i/o backward err ')
+
+   -- Now test a table input
+   -- jac needs a tensor input, so we have to form a network that creates
+   -- a table internally:  Do this using a Reshape and a SplitTable
+   m = nn.Sequential()
+   m:add(nn.Reshape(1,10,10,10))
+   m:add(nn.SplitTable(1))  -- output of Split table is a table of length 1
+ 
+   concat = nn.ConcatTable()
+   concat:add(nn.JoinTable(1))
+ 
+   m:add(concat)
+   m:add(nn.JoinTable(1))
+
+   err = jac.testJacobian(m, input)
+   mytester:assertlt(err, precision, ' error on state ')
+
+   ferr, berr = jac.testIO(m, input)
+   mytester:asserteq(ferr, 0, torch.typename(m)..' - i/o forward err ')
+   mytester:asserteq(berr, 0, torch.typename(m)..' - i/o backward err ')
+
+   -- As per Soumith's suggestion, make sure getParameters works:
+   m = nn.ConcatTable()
+   local l = nn.Linear(16,16)
+   m:add(l)
+   mparams = m:getParameters()
+   -- I don't know of a way to make sure that the storage is equal, however
+   -- the linear weight and bias will be randomly initialized, so just make
+   -- sure both parameter sets are equal
+   lparams = l:getParameters()
+   err = (mparams - lparams):abs():max()
+   mytester:assertlt(err, precision, ' getParameters error ')
+end
+
 mytester:add(nntest)
 
 if not nn then