Update. The input/output can now be nested tables.

diff --git a/dagnn.lua b/dagnn.lua
index 4841843..65a30e2 100755 (executable)
--- a/dagnn.lua
+++ b/dagnn.lua
@@ -25,32 +25,26 @@ function DAG:addEdge(a, b)
    succ[a][#succ[a] + 1] = b
 end
 
-function DAG:setInput(i)
-   self.sorted = nil
-   if torch.type(i) == 'table' then
-      self.inputModules = i
-      for _, m in ipairs(i) do
-         if not self.pred[m] and not self.succ[m] then
-            self:add(m)
-         end
+function DAG:applyOnModules(f, t1, t2)
+   if torch.type(t1) == 'table' then
+      local result = {}
+      for k, s in pairs(t1) do
+         result[k] = self:applyOnModules(f, s, t2 and t2[k])
       end
+      return result
    else
-      self:setInput({ i })
+      return f(t1, t2)
    end
 end
 
+function DAG:setInput(i)
+   self.sorted = nil
+   self.inputModules = i
+end
+
 function DAG:setOutput(o)
    self.sorted = nil
-   if torch.type(o) == 'table' then
-      self.outputModules = o
-      for _, m in ipairs(o) do
-         if not self.pred[m] and not self.succ[m] then
-            self:add(m)
-         end
-      end
-   else
-      self:setOutput({ o })
-   end
+   self.outputModules = o
 end
 
 function DAG:sort()
@@ -60,9 +54,7 @@ function DAG:sort()
 
    local distance = {}
 
-   for _, a in pairs(self.inputModules) do
-      distance[a] = 1
-   end
+   self:applyOnModules(function(m) distance[m] = 1 end, self.inputModules)
 
    local nc
 
@@ -98,13 +90,7 @@ end
 function DAG:updateOutput(input)
    self:sort()
 
-   if #self.inputModules == 1 then
-      self.inputModules[1]:updateOutput(input)
-   else
-      for i, d in ipairs(self.inputModules) do
-         d:updateOutput(input[i])
-      end
-   end
+   self:applyOnModules(function(m, i) m:updateOutput(i) end, self.inputModules, input)
 
    for _, d in ipairs(self.sorted) do
       if self.pred[d] then
@@ -120,14 +106,7 @@ function DAG:updateOutput(input)
       end
    end
 
-   if #self.outputModules == 1 then
-      self.output = self.outputModules[1].output
-   else
-      self.output = { }
-      for i, d in ipairs(self.outputModules) do
-         self.output[i] = d.output
-      end
-   end
+   self.output = self:applyOnModules(function(m) return m.output end, self.outputModules)
 
    return self.output
 end

diff --git a/test-dagnn.lua b/test-dagnn.lua
index 262ea6f..a45d636 100755 (executable)
--- a/test-dagnn.lua
+++ b/test-dagnn.lua
@@ -5,6 +5,10 @@ require 'nn'
 
 require 'dagnn'
 
+-- torch.setnumthreads(params.nbThreads)
+torch.setdefaulttensortype('torch.DoubleTensor')
+torch.manualSeed(2)
+
 a = nn.Linear(10, 10)
 b = nn.ReLU()
 c = nn.Linear(10, 3)
@@ -24,14 +28,16 @@ f = nn.Linear(3, 2)
 g = nn.DAG:new()
 
 g:setInput(a)
-g:setOutput({ e, f })
+g:setOutput({ e })
 
 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:print()
 
@@ -39,5 +45,12 @@ input = torch.Tensor(3, 10):uniform()
 
 output = g:updateOutput(input)
 
-print(output[1])
-print(output[2])
+if torch.type(output) == 'table' then
+   for i, t in pairs(output) do
+      print(tostring(i) .. ' -> ' .. tostring(t))
+   end
+else
+   print(tostring(output))
+end
+
+torch.save('dag.t7', g)