Merge pull request #126 from torch/typefix

fix for :type() to typecast the children as well

2 files changed, 19 insertions, 5 deletions

diff --git a/gmodule.lua b/gmodule.lua
index 0a81da9..6e118d8 100644
--- a/gmodule.lua
+++ b/gmodule.lua
@@ -263,12 +263,18 @@ function gModule:type(type, tensorCache)
          node.data.gradOutputBuffer =
             recursiveType(node.data.gradOutputBuffer, type)
       end
+      for k, child in ipairs(node.children) do
+         applyTypeToTable(child.data)
+      end
    end
 
    for i,node in ipairs(self.forwardnodes) do
       if node.data.input ~= nil then
          node.data.input = recursiveType(node.data.input, type)
       end
+      for k, child in ipairs(node.children) do
+         applyTypeToTable(child.data)
+      end
    end
 
    self._type = type
diff --git a/test/test_nngraph.lua b/test/test_nngraph.lua
index 8e5aadc..340ab24 100644
--- a/test/test_nngraph.lua
+++ b/test/test_nngraph.lua
@@ -214,6 +214,9 @@ function test.test_gradInputType()
       local module = nn.gModule({in1}, {out1})
       local input = torch.rand(20)
       local output = module:forward(input)
+      local gradOutput = output:clone():normal()
+      local gradInput = module:backward(input, gradOutput)
+
       module:backward(input, output)
       tester:eq(torch.typename(output), "torch.DoubleTensor")
       tester:eq(torch.typename(module.output), "torch.DoubleTensor")
@@ -222,17 +225,22 @@ function test.test_gradInputType()
       tester:eq(torch.typename(module.outnode.data.input[1]), "torch.DoubleTensor")
       tester:eq(torch.typename(module.forwardnodes[1].data.input[1]), "torch.DoubleTensor")
       tester:eq(torch.typename(module.forwardnodes[1].children[1].data.input[1]), "torch.DoubleTensor")
+      tester:eq(torch.typename(module.backwardnodes[1].children[1].data.gradOutput[1]), "torch.DoubleTensor")
 
       module:float()
-      local output = module:forward(input:float())
-      tester:eq(torch.typename(output), "torch.FloatTensor")
       tester:eq(torch.typename(module.output), "torch.FloatTensor")
       tester:eq(torch.typename(module.gradInput), "torch.FloatTensor")
       tester:eq(torch.typename(module.innode.data.input[1]), "torch.FloatTensor")
       tester:eq(torch.typename(module.outnode.data.input[1]), "torch.FloatTensor")
       tester:eq(torch.typename(module.forwardnodes[1].data.input[1]), "torch.FloatTensor")
       tester:eq(torch.typename(module.forwardnodes[1].children[1].data.input[1]), "torch.FloatTensor")
-   end   
+      tester:eq(torch.typename(module.backwardnodes[1].children[1].data.gradOutput[1]), "torch.FloatTensor")
+      local output = module:forward(input:float())
+      tester:eq(torch.typename(output), "torch.FloatTensor")
+      local gradInput = module:backward(input:float(), gradOutput:float())
+      tester:eq(torch.typename(gradInput), "torch.FloatTensor")
+
+   end
 
    function test.test_nestedGradInput()
       local x = nn.Identity()()
@@ -384,8 +392,8 @@ function test.test_gradInputType()
    end
 
    function test.test_gradOutputZeroOptim()
-      local unpack = function(...) 
-	 if _G[unpack] then return _G[unpack](...) 
+      local unpack = function(...)
+	 if _G[unpack] then return _G[unpack](...)
 	 else return table.unpack(...) end
       end
       -- Make module that produces an expanded zero gradInput tensor