OCD cosmetics.

[dagnn.git] / dagnn.lua

diff --git a/dagnn.lua b/dagnn.lua
index de9d29b..5921c05 100755 (executable)
--- a/dagnn.lua
+++ b/dagnn.lua
@@ -26,7 +26,7 @@ local DAG, parent = torch.class('nn.DAG', 'nn.Container')
 function DAG:__init()
    parent.__init(self)
    -- Nodes are indexed by the module they contain
-   self.node = { }
+   self.node = {}
 end
 
 -- Apply f on t recursively; use the corresponding elements from args
@@ -76,7 +76,7 @@ function DAG:putInOrder()
       end
    until nc == 0
 
-   self.sorted = { }
+   self.sorted = {}
    for m, d in pairs(distance) do
       table.insert(self.sorted, { distance = d, nnm = m })
    end
@@ -86,18 +86,37 @@ function DAG:putInOrder()
    for i, a in ipairs(self.sorted) do self.sorted[i] = a.nnm end
 end
 
+-- This accumulates x in a where they are both nested tables of
+-- tensors. If first is true, set a = x. Behavior is undefined if a
+-- and x do not have the exact same structure.
+function DAG:nestedAccTensor(a, x, first)
+   if torch.type(x) == 'table' then
+      local b = {}
+      for i in pairs(x) do
+         b[i] = self:nestedAccTensor(a[i], x[i], first)
+      end
+      a = b
+   else
+      if first then
+         if a then
+            a:resizeAs(x):copy(x)
+         else
+            a = x:clone()
+         end
+      else
+         a:add(x)
+      end
+   end
+   return a
+end
+
 function DAG:updateGradOutput(node)
    local gradInputSucc = node.gradInputSucc
    if #gradInputSucc == 1 then
       node.gradOutput = gradInputSucc[1]
    elseif #gradInputSucc > 1 then
-      if node.gradOutput then
-         node.gradOutput:resize(gradInputSucc[1]):copy(gradInputSucc[1])
-      else
-         node.gradOutput = gradInputSucc[1]:clone()
-      end
-      for k = 2, #gradInputSucc do
-         node.gradOutput:add(gradInputSucc[k])
+      for k = 1, #gradInputSucc do
+         node.gradOutput = self:nestedAccTensor(node.gradOutput, gradInputSucc[k], k == 1)
       end
    end
 end
@@ -127,7 +146,7 @@ function DAG:setInput(i)
             error('Input modules must have outgoing  edges.')
          end
          if #self.node[nnm].pred > 0 then
-            error('Input modules cannog have incoming edges.')
+            error('Input modules cannot have incoming edges.')
          end
       end,
       self.inputModules
@@ -205,8 +224,9 @@ function DAG:updateOutput(input)
 
    self:nestedApply(
       function(nnm, i)
-         self.node[nnm].input = i
-         self:rethrowErrors(nnm, self.node[nnm].index, 'updateOutput', i)
+         local node = self.node[nnm]
+         node.input = i
+         self:rethrowErrors(nnm, node.index, 'updateOutput', i)
       end,
       self.inputModules,
       input
@@ -225,7 +245,7 @@ function DAG:updateOutput(input)
             end
          end
          node.input = i
-         self:rethrowErrors(nnm, self.node[nnm].index, 'updateOutput', i)
+         self:rethrowErrors(nnm, node.index, 'updateOutput', i)
       end
    end
 
@@ -244,7 +264,7 @@ function DAG:updateGradInput(input, gradOutput)
       function(nnm, go)
          local node = self.node[nnm]
          node.gradOutput = go
-         self:rethrowErrors(nnm, node.index, 'updateGradInput', self.node[nnm].input, go)
+         self:rethrowErrors(nnm, node.index, 'updateGradInput', node.input, go)
       end,
       self.outputModules, gradOutput
    )
@@ -265,7 +285,7 @@ function DAG:updateGradInput(input, gradOutput)
 
       if #node.gradInputSucc > 0 then
          self:updateGradOutput(node)
-         self:rethrowErrors(nnm, self.node[nnm].index, 'updateGradInput', node.input, node.gradOutput)
+         self:rethrowErrors(nnm, node.index, 'updateGradInput', node.input, node.gradOutput)
       end
 
       -- We fill the gradInputSucc of our predecessors
@@ -287,8 +307,6 @@ function DAG:updateGradInput(input, gradOutput)
 end
 
 function DAG:accGradParameters(input, gradOutput, scale)
-   scale = scale or 1
-
    assert(self.sorted, 'There has been a DAG structure change before a DAG:accGradParameters')
 
    self:nestedApply(
@@ -307,3 +325,13 @@ function DAG:accGradParameters(input, gradOutput, scale)
       self:rethrowErrors(nnm, k, 'accGradParameters', node.input, node.gradOutput, scale)
    end
 end
+
+function DAG:clearState()
+   self.sorted = nil
+   for _, node in pairs(self.node) do
+      node.gradInputSucc = nil
+      node.input = nil
+      node.gradOutput = nil
+   end
+   return parent.clearState(self)
+end