Make type() truly recursive.

Recursively iterate over the whole table, converting each tensor to the
given type. Removes need for many specialized type() functions.

14 files changed, 16 insertions, 109 deletions

diff --git a/ConcatTable.lua b/ConcatTable.lua
index 706ee6a..93d9ad5 100644
--- a/ConcatTable.lua
+++ b/ConcatTable.lua
@@ -86,15 +86,6 @@ function ConcatTable:zeroGradParameters()
    end
 end
 
-function ConcatTable:type(type)
-   parent.type(self, type)
-   if torch.type(self.gradInput) == 'table' then
-      for i, gradInput in ipairs(self.gradInput) do
-         self.gradInput[i] = gradInput:type(type)
-      end
-   end
-end
-
 function ConcatTable:__tostring__()
    local tab = '  '
    local line = '\n'
diff --git a/Criterion.lua b/Criterion.lua
index dec514c..0f6e41b 100644
--- a/Criterion.lua
+++ b/Criterion.lua
@@ -31,9 +31,7 @@ end
 function Criterion:type(type)
    -- find all tensors and convert them
    for key,param in pairs(self) do
-      if torch.typename(param) and torch.typename(param):find('torch%..+Tensor') then
-         self[key] = param:type(type)
-      end
+      self[key] = nn._utils.recursiveType(param, type)
    end
    return self
 end
diff --git a/CrossEntropyCriterion.lua b/CrossEntropyCriterion.lua
index 2b3c78c..d4d19e5 100644
--- a/CrossEntropyCriterion.lua
+++ b/CrossEntropyCriterion.lua
@@ -25,11 +25,4 @@ function CrossEntropyCriterion:updateGradInput(input, target)
    return self.gradInput
 end
 
-function CrossEntropyCriterion:type(name)
-   Criterion.type(self, name)
-   self.lsm:type(name)
-   self.nll:type(name)
-   return self
-end
-
 return nn.CrossEntropyCriterion
diff --git a/DotProduct.lua b/DotProduct.lua
index 7f7b524..bc8e854 100644
--- a/DotProduct.lua
+++ b/DotProduct.lua
@@ -27,10 +27,3 @@ function DotProduct:updateGradInput(input, gradOutput)
 
    return self.gradInput
 end
-
-function DotProduct:type(type)
-   for i, tensor in ipairs(self.gradInput) do
-       self.gradInput[i] = tensor:type(type)
-   end
-   return parent.type(self, type)
-end
diff --git a/L1HingeEmbeddingCriterion.lua b/L1HingeEmbeddingCriterion.lua
index 27f715b..6957278 100644
--- a/L1HingeEmbeddingCriterion.lua
+++ b/L1HingeEmbeddingCriterion.lua
@@ -39,9 +39,3 @@ function L1HingeEmbeddingCriterion:updateGradInput(input, y)
   self.gradInput[2]:zero():add(-1, self.gradInput[1])
   return self.gradInput
 end
-
-function L1HingeEmbeddingCriterion:type(type)
-   self.gradInput[1] = self.gradInput[1]:type(type)
-   self.gradInput[2] = self.gradInput[2]:type(type)
-   return parent.type(self, type)
-end
diff --git a/MarginRankingCriterion.lua b/MarginRankingCriterion.lua
index c0573ac..30c6855 100644
--- a/MarginRankingCriterion.lua
+++ b/MarginRankingCriterion.lua
@@ -69,9 +69,3 @@ function MarginRankingCriterion:updateGradInput(input, y)
    end
    return self.gradInput 
 end
-
-function MarginRankingCriterion:type(type)
-   self.gradInput[1] = self.gradInput[1]:type(type)
-   self.gradInput[2] = self.gradInput[2]:type(type)
-   return parent.type(self, type)
-end
diff --git a/MixtureTable.lua b/MixtureTable.lua
index 6111a99..77a7d3e 100644
--- a/MixtureTable.lua
+++ b/MixtureTable.lua
@@ -146,22 +146,3 @@ function MixtureTable:updateGradInput(input, gradOutput)
 
    return self.gradInput
 end
-
-function MixtureTable:type(type)
-   self.output = self.output:type(type)
-   self.gradInput[1] = self.gradInput[1]:type(type)
-   self._gaterView = self._gaterView:type(type)
-   self._expert = self._expert:type(type)
-   self._expertView = self._expertView:type(type)
-   self._sum = self._sum:type(type)
-   self._gradInput = self._gradInput:type(type)
-   self._expert2 = self._expert2:type(type)
-   self._expertView2 = self._expertView2:type(type)
-   if torch.type(self.gradInput[2]) == 'table' then
-      for i,expertGradInput in ipairs(self.gradInput[2]) do
-         self.gradInput[2][i] = expertGradInput:type(type)
-      end
-   else
-      self.gradInput[2] = self._gradInput
-   end
-end
diff --git a/Module.lua b/Module.lua
index d3f5a26..d6b16fb 100644
--- a/Module.lua
+++ b/Module.lua
@@ -113,17 +113,6 @@ function Module:clone(...)
    return clone
 end
 
-local function recursiveType(param, type_str)
-   if torch.type(param) == 'table' then
-      for i = 1, #param do
-         param[i] = recursiveType(param[i], type_str)
-      end
-   elseif torch.isTensor(param) then
-       param = param:type(type_str)
-   end
-   return param
-end
-
 function Module:type(type)
    assert(type, 'Module: must provide a type to convert to')
    -- find all tensors and convert them
@@ -132,7 +121,7 @@ function Module:type(type)
       -- are table's of tensors.  To be general we need to recursively
       -- cast fields that may be nested tables.
       if key ~= 'modules' then
-        self[key] = recursiveType(self[key], type)
+        self[key] = nn._utils.recursiveType(param, type)
       end
    end
    -- find submodules in classic containers 'modules'
diff --git a/PairwiseDistance.lua b/PairwiseDistance.lua
index affc2e5..79569c9 100644
--- a/PairwiseDistance.lua
+++ b/PairwiseDistance.lua
@@ -86,19 +86,3 @@ function PairwiseDistance:updateGradInput(input, gradOutput)
    self.gradInput[2]:zero():add(-1, self.gradInput[1])
    return self.gradInput
 end
-
--- save away Module:type(type) for later use.
-PairwiseDistance._parent_type = parent.type
-
--- Fix the bug where tmp = nn.PairwiseDistance:cuda() fails to convert table
--- contents.  We could, and probably should, change Module.lua to loop over
--- and convert all the table elements in a module, but that might have 
--- repercussions, so this is a safer solution.
-function PairwiseDistance:type(type)
-   self:_parent_type(type)  -- Call the parent (Module) type function
-   -- Now convert the left over table elements
-   self.gradInput[1] = self.gradInput[1]:type(type)
-   self.gradInput[2] = self.gradInput[2]:type(type)
-   return self
-end
-
diff --git a/SpatialContrastiveNormalization.lua b/SpatialContrastiveNormalization.lua
index b5a6ce7..0ad251a 100644
--- a/SpatialContrastiveNormalization.lua
+++ b/SpatialContrastiveNormalization.lua
@@ -34,9 +34,3 @@ function SpatialContrastiveNormalization:updateGradInput(input, gradOutput)
    self.gradInput = self.normalizer:backward(input, gradOutput)
    return self.gradInput
 end
-
-function SpatialContrastiveNormalization:type(type)
-   parent.type(self,type)
-   self.normalizer:type(type)
-   return self
-end
diff --git a/SpatialDivisiveNormalization.lua b/SpatialDivisiveNormalization.lua
index 23a2c0b..92dfac7 100644
--- a/SpatialDivisiveNormalization.lua
+++ b/SpatialDivisiveNormalization.lua
@@ -113,13 +113,3 @@ function SpatialDivisiveNormalization:updateGradInput(input, gradOutput)
    -- done
    return self.gradInput
 end
-
-function SpatialDivisiveNormalization:type(type)
-   parent.type(self,type)
-   self.meanestimator:type(type)
-   self.stdestimator:type(type)
-   self.divider:type(type)
-   self.normalizer:type(type)
-   self.thresholder:type(type)
-   return self
-end
diff --git a/SpatialSubtractiveNormalization.lua b/SpatialSubtractiveNormalization.lua
index f2c2c31..51bf23a 100644
--- a/SpatialSubtractiveNormalization.lua
+++ b/SpatialSubtractiveNormalization.lua
@@ -90,11 +90,3 @@ function SpatialSubtractiveNormalization:updateGradInput(input, gradOutput)
    -- done
    return self.gradInput
 end
-
-function SpatialSubtractiveNormalization:type(type)
-   parent.type(self,type)
-   self.meanestimator:type(type)
-   self.divider:type(type)
-   self.subtractor:type(type)
-   return self
-end
diff --git a/init.lua b/init.lua
index 42946af..c2b2996 100644
--- a/init.lua
+++ b/init.lua
@@ -1,6 +1,8 @@
 require('torch')
 require('libnn')
 
+include('utils.lua')
+
 include('ErrorMessages.lua')
 include('Module.lua')
 
diff --git a/utils.lua b/utils.lua
new file mode 100644
index 0000000..887b82d
--- /dev/null
+++ b/utils.lua
@@ -0,0 +1,12 @@
+nn._utils = {}
+
+function nn._utils.recursiveType(param, type_str)
+   if torch.type(param) == 'table' then
+      for k, v in pairs(param) do
+         param[k] = nn._utils.recursiveType(v, type_str)
+      end
+   elseif torch.isTensor(param) then
+       param = param:type(type_str)
+   end
+   return param
+end