Update.

[dagnn.git] / graphnn.lua

diff --git a/graphnn.lua b/graphnn.lua
deleted file mode 100755 (executable)
index 1ec9b4e..0000000
--- a/graphnn.lua
+++ /dev/null
@@ -1,167 +0,0 @@
-#!/usr/bin/env luajit
-
-require 'torch'
-require 'nn'
-require 'image'
-require 'optim'
-
-----------------------------------------------------------------------
-
-local Graph, parent = torch.class('nn.Graph', 'nn.Container')
-
-function Graph:__init()
-   parent.__init(self)
-   self.pred = {}
-   self.succ = {}
-end
-
-function Graph:addEdge(a, b)
-   local pred, succ = self.pred, self.succ
-   if not pred[a] and not succ[a] then
-      self:add(a)
-   end
-   if not pred[b] and not succ[b] then
-      self:add(b)
-   end
-   pred[b] = pred[b] or {}
-   pred[b][#pred[b] + 1] = a
-   succ[a] = succ[a] or {}
-   succ[a][#succ[a] + 1] = b
-end
-
-function Graph:setInput(i)
-   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
-      end
-   else
-      self:setInput({ i })
-   end
-end
-
-function Graph:setOutput(o)
-   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
-end
-
-function Graph:order()
-   local distance = {}
-
-   for _, a in pairs(self.inputModules) do
-      distance[a] = 1
-   end
-
-   local nc
-
-   repeat
-      nc = 0
-      for i, isucc in pairs(self.succ) do
-         for _, j in pairs(isucc) do
-            if distance[i] and (not distance[j] or distance[j] < distance[i] + 1) then
-               distance[j] = distance[i] + 1
-               nc = nc + 1
-            end
-         end
-      end
-   until nc == 0
-
-   self.sorted = { }
-   for i, d in pairs(distance) do
-      table.insert(self.sorted, { d, i })
-   end
-
-   table.sort(self.sorted, function(a, b) return a[1] < b[1] end)
-   for i, a in ipairs(self.sorted) do self.sorted[i] = a[2] end
-end
-
-function Graph:print()
-   for i, d in ipairs(self.sorted) do
-      print('#' .. i .. ' -> ' .. torch.type(d))
-   end
-end
-
-function Graph:updateOutput(input)
-   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
-
-   for _, d in ipairs(self.sorted) do
-      if self.pred[d] then
-         if #self.pred[d] == 1 then
-            d:updateOutput(self.pred[d][1].output)
-         elseif #self.pred[d] > 1 then
-            local c = {}
-            for k = 1, #self.pred[d] do
-               c[k] = self.pred[d][k].output
-            end
-            d:updateOutput(c)
-         end
-      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
-
-   return self.output
-end
-
-----------------------------------------------------------------------
-
-a = nn.Linear(10, 10)
-b = nn.ReLU()
-c = nn.Linear(10, 3)
-d = nn.Linear(10, 3)
-e = nn.CMulTable()
-f = nn.Linear(3, 2)
-
---[[
-
-   a -----> b ---> c ----> e ---
-             \           /
-              \--> d ---/
-                    \
-                     \---> f ---
-]]--
-
-g = Graph:new()
-
-g:setInput(a)
-g:setOutput({ e, f })
-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:order()
-
-g:print(graph)
-
-input = torch.Tensor(3, 10):uniform()
-
-output = g:updateOutput(input)
-
-print(output[1])
-print(output[2])