Merge pull request #38 from nicholas-leonard/concat

ConcatTable nested table input

1 files changed, 33 insertions, 39 deletions

diff --git a/test/test.lua b/test/test.lua
index a5550b7..1330607 100644
--- a/test/test.lua
+++ b/test/test.lua
@@ -9,6 +9,16 @@ local expprecision = 1e-4
 
 local nntest = {}
 
+local function equal(t1, t2, msg)
+   if (torch.type(t1) == "table") then
+      for k, v in pairs(t2) do
+         equal(t1[k], t2[k])
+      end
+   else
+      mytester:assertTensorEq(t1, t2, 0.00001, msg)
+   end
+end
+
 function nntest.Add()
    local ini = math.random(10,20)
    local inj = math.random(10,20)
@@ -1916,15 +1926,6 @@ function nntest.SelectTable()
       {torch.Tensor(3,4,5):zero()}, 
       {torch.Tensor(3,4,5):zero(), {torch.Tensor(3,4,5):zero()}}
    }
-   local function equal(t1, t2, msg)
-      if (torch.type(t1) == "table") then
-         for k, v in pairs(t2) do
-            equal(t1[k], t2[k])
-         end
-      else
-         mytester:assertTensorEq(t1, t2, 0.00001, msg)
-      end
-   end
    local nonIdx = {2,3,4,1}
    local module
    for idx = 1,#input do
@@ -2013,36 +2014,29 @@ function nntest.ConcatTable()
    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 ')
+   local input = {
+      torch.randn(3,4):float(), torch.randn(3,4):float(), {torch.randn(3,4):float()}
+   }
+   local _gradOutput = {
+      torch.randn(3,3,4):float(), torch.randn(3,3,4):float(), torch.randn(3,3,4):float()
+   }
+   local gradOutput = {
+      {_gradOutput[1][1], _gradOutput[2][1], {_gradOutput[3][1]}}, 
+      {_gradOutput[1][2], _gradOutput[2][2], {_gradOutput[3][2]}},
+      {_gradOutput[1][3], _gradOutput[2][3], {_gradOutput[3][3]}}
+   }
+   local module = nn.ConcatTable()
+   module:add(nn.Identity())
+   module:add(nn.Identity())
+   module:add(nn.Identity())
+   module:float()
+   
+   local output = module:forward(input)
+   local output2 = {input, input, input}
+   equal(output2, output, "ConcatTable table output")
+   local gradInput = module:backward(input, gradOutput)
+   local gradInput2 = {_gradOutput[1]:sum(1), _gradOutput[2]:sum(1), {_gradOutput[3]:sum(1)}}
+   equal(gradInput, gradInput2, "ConcatTable table gradInput")
 end
 
 mytester:add(nntest)