From: Francois Fleuret <francois@fleuret.org>
Date: Wed, 11 Jan 2017 08:30:33 +0000 (+0100)
Subject: Update.
X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?p=dagnn.git;a=commitdiff_plain;h=da3a60ffa7e1a39e4d01b405c2d80d84c3722c2c

Update.
---

diff --git a/dagnn.lua b/dagnn.lua
index 65a30e2..a6414b3 100755
--- a/dagnn.lua
+++ b/dagnn.lua
@@ -25,26 +25,46 @@ function DAG:addEdge(a, b)
    succ[a][#succ[a] + 1] = b
 end
 
-function DAG:applyOnModules(f, t1, t2)
-   if torch.type(t1) == 'table' then
+-- Apply f on t recursively; use the corresponding a1 and a2 elements
+-- (i.e. same keys) as second and third parameters to f when
+-- available; return the results from f, organized in a similarly
+-- nested table.
+function DAG:applyOnModules(f, t, a1, a2)
+   if torch.type(t) == 'table' then
       local result = {}
-      for k, s in pairs(t1) do
-         result[k] = self:applyOnModules(f, s, t2 and t2[k])
+      for k, s in pairs(t) do
+         result[k] = self:applyOnModules(f, s, a1 and a1[k], a2 and a2[k])
       end
       return result
    else
-      return f(t1, t2)
+      return f(t, a1, a2)
    end
 end
 
 function DAG:setInput(i)
    self.sorted = nil
    self.inputModules = i
+   self:applyOnModules(
+      function(m)
+         if (not self.succ[m] or #self.succ[m] == 0) or (self.pred[m] and #self.pred[m] > 0) then
+            error('Invalid input edges.')
+         end
+      end,
+      self.inputModules
+   )
 end
 
 function DAG:setOutput(o)
    self.sorted = nil
    self.outputModules = o
+   self:applyOnModules(
+      function(m)
+         if (not self.pred[m] or #self.pred[m] == 0) or (self.succ[m] and #self.succ[m] > 0) then
+            error('Invalid output edges.')
+         end
+      end,
+      self.outputModules
+   )
 end
 
 function DAG:sort()
@@ -113,6 +133,31 @@ end
 
 function DAG:updateGradInput(input, gradOutput)
    self:sort()
+
+   self:applyOnModules(function(m, i, go) m:updateGradInput(i, go) end, self.outputModules, input, gradOutput)
+
+   for k = self.sorted, 1, -1 do
+      local m = sorted[k]
+      if self.succ[d] then
+         if #self.succ[d] == 1 then
+            d:updateGradInput(self.succ[d][1].gradInput)
+         elseif #self.succ[d] > 1 then
+            local sum
+            for k = 1, #self.succ[d] do
+               if sum then
+                  sum:add(self.succ[d][k].gradInput)
+               else
+                  sum = self.succ[d][k].gradInput:clone()
+               end
+            end
+            d:updateGradInput(sum)
+         end
+      end
+   end
+
+   self.gradInput = self:applyOnModules(function(m) return m.gradInput end, self.inputModules)
+
+   return self.gradInput
 end
 
 return DAG
diff --git a/test-dagnn.lua b/test-dagnn.lua
index a45d636..6c09f95 100755
--- a/test-dagnn.lua
+++ b/test-dagnn.lua
@@ -5,6 +5,17 @@ require 'nn'
 
 require 'dagnn'
 
+function printTensorTable(t)
+   if torch.type(t) == 'table' then
+      for i, t in pairs(t) do
+         print('-- ELEMENT [' .. i .. '] --')
+         printTensorTable(t)
+      end
+   else
+      print(tostring(t))
+   end
+end
+
 -- torch.setnumthreads(params.nbThreads)
 torch.setdefaulttensortype('torch.DoubleTensor')
 torch.manualSeed(2)
@@ -16,41 +27,32 @@ d = nn.Linear(10, 3)
 e = nn.CMulTable()
 f = nn.Linear(3, 2)
 
---[[
-
-   a -----> b ---> c ----> e ---
-             \           /
-              \--> d ---/
-                    \
-                     \---> f ---
-]]--
+--   a -----> b ---> c ----> e ---
+--             \           /
+--              \--> d ---/
+--                    \
+--                     \---> f ---
 
-g = nn.DAG:new()
-
-g:setInput(a)
-g:setOutput({ e })
+g = nn.DAG()
 
 g:addEdge(c, e)
 g:addEdge(a, b)
 g:addEdge(d, e)
 g:addEdge(b, c)
 g:addEdge(b, d)
--- g:addEdge(d, f)
+g:addEdge(d, f)
 
--- g = torch.load('dag.t7')
+g:setInput({a})
+g:setOutput({e,f})
 
 g:print()
 
 input = torch.Tensor(3, 10):uniform()
 
-output = g:updateOutput(input)
+output = g:updateOutput({input})
 
-if torch.type(output) == 'table' then
-   for i, t in pairs(output) do
-      print(tostring(i) .. ' -> ' .. tostring(t))
-   end
-else
-   print(tostring(output))
-end
+printTensorTable(output)
+
+----------------------------------------------------------------------
 
-torch.save('dag.t7', g)
+-- gradInput = g:updateGradInput({ input }, output)