X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?p=dagnn.git;a=blobdiff_plain;f=dagnn.lua;h=de9d29b8d3e15c2d0575144f29ee0e1ed1a881de;hp=7fc1018f8dd7f3f88021c914608c5af1f5a710a5;hb=56a476ee19396d0e7f186b238dc7d013000acb59;hpb=063f198047f0202fa921aa09b772369b14ae8be2

diff --git a/dagnn.lua b/dagnn.lua
index 7fc1018..de9d29b 100755
--- a/dagnn.lua
+++ b/dagnn.lua
@@ -29,6 +29,21 @@ function DAG:__init()
    self.node = { }
 end
 
+-- Apply f on t recursively; use the corresponding elements from args
+-- (i.e. same keys) as second parameter to f when available; return
+-- the results from f, organized in a similarly nested table.
+function DAG:nestedApply(f, t, args)
+   if torch.type(t) == 'table' then
+      local result = {}
+      for k, s in pairs(t) do
+         result[k] = self:nestedApply(f, s, args and args[k])
+      end
+      return result
+   else
+      return f(t, args)
+   end
+end
+
 function DAG:createNode(nnm)
    if not self.node[nnm] then
       self:add(nnm) -- Add it to the object as a Container
@@ -40,26 +55,66 @@ function DAG:createNode(nnm)
    end
 end
 
-function DAG:addEdge(nnma, nnmb)
-   self.sorted = nil
-   self:createNode(nnma)
-   self:createNode(nnmb)
-   table.insert(self.node[nnmb].pred, nnma)
-   table.insert(self.node[nnma].succ, nnmb)
+function DAG:putInOrder()
+   if self.sorted then
+      return
+   end
+
+   local distance = {}
+   self:nestedApply(function(m) distance[m] = 1 end, self.inputModules)
+
+   local nc
+   repeat
+      nc = 0
+      for nnma, node in pairs(self.node) do
+         for _, nnmb in pairs(node.succ) do
+            if distance[nnma] and (not distance[nnmb] or distance[nnmb] < distance[nnma] + 1) then
+               distance[nnmb] = distance[nnma] + 1
+               nc = nc + 1
+            end
+         end
+      end
+   until nc == 0
+
+   self.sorted = { }
+   for m, d in pairs(distance) do
+      table.insert(self.sorted, { distance = d, nnm = m })
+   end
+
+   table.sort(self.sorted, function(a, b) return a.distance < b.distance end)
+
+   for i, a in ipairs(self.sorted) do self.sorted[i] = a.nnm end
 end
 
--- Apply f on t recursively; use the corresponding element from args
--- (i.e. same keys) as second parameter to f when available; return
--- the results from f, organized in a similarly nested table.
-function DAG:nestedApply(f, t, args)
-   if torch.type(t) == 'table' then
-      local result = {}
-      for k, s in pairs(t) do
-         result[k] = self:nestedApply(f, s, args and args[k])
+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])
       end
-      return result
-   else
-      return f(t, args)
+   end
+end
+
+----------------------------------------------------------------------
+
+-- Connect a sequence of modules
+function DAG:connect(...)
+   self.sorted = nil
+   local prev
+   for _, nnm in pairs({...}) do
+      self:createNode(nnm)
+      if prev then
+         table.insert(self.node[nnm].pred, prev)
+         table.insert(self.node[prev].succ, nnm)
+      end
+      prev = nnm
    end
 end
 
@@ -95,59 +150,52 @@ function DAG:setOutput(o)
    )
 end
 
-function DAG:putInOrder()
-   if self.sorted then
-      return
+function DAG:print()
+   self:putInOrder()
+
+   for i, d in ipairs(self.sorted) do
+      print('#' .. i .. ' -> ' .. torch.type(d))
    end
+end
 
-   local distance = {}
-   self:nestedApply(function(m) distance[m] = 1 end, self.inputModules)
+----------------------------------------------------------------------
 
-   local nc
-   repeat
-      nc = 0
-      for nnma, node in pairs(self.node) do
-         for _, nnmb in pairs(node.succ) do
-            if distance[nnma] and (not distance[nnmb] or distance[nnmb] < distance[nnma] + 1) then
-               distance[nnmb] = distance[nnma] + 1
-               nc = nc + 1
-            end
-         end
-      end
-   until nc == 0
+function DAG:saveDot(filename)
+   local file = (filename and io.open(filename, 'w')) or io.stdout
 
-   self.sorted = { }
-   for m, d in pairs(distance) do
-      table.insert(self.sorted, { distance = d, nnm = m })
-   end
+   file:write('digraph {\n')
 
-   table.sort(self.sorted, function(a, b) return a.distance < b.distance end)
+   file:write('\n')
 
-   for i, a in ipairs(self.sorted) do self.sorted[i] = a.nnm end
-end
+   for nnmb, node in pairs(self.node) do
+      file:write(
+         '  '
+            .. node.index
+            .. ' [shape=box,label=\"' .. torch.type(nnmb) .. '\"]'
+            .. '\n'
+      )
 
-function DAG:computeGradOutput(gradInputSucc)
-   local gi
-   if #gradInputSucc == 1 then
-      gi = gradInputSucc[1] -- we avoid a clone()
-   elseif #gradInputSucc > 1 then
-      for k = 1, #gradInputSucc do
-         if gi then
-            gi:add(gradInputSucc[k])
-         else
-            gi = gradInputSucc[k]:clone()
+      for i, nnma in pairs(node.pred) do
+         local decoration = ''
+         if #node.pred > 1 then
+            -- decoration = ' [headlabel=\"' .. i .. '\"]'
+            decoration = ' [label=\"' .. i .. '\"]'
          end
+         file:write(
+            '  '
+               .. self.node[nnma].index
+               .. ' -> '
+               .. self.node[nnmb].index
+               .. decoration
+               .. '\n'
+         )
       end
+
+      file:write('\n')
    end
-   return gi
-end
 
-function DAG:print()
-   self:putInOrder()
+   file:write('}\n')
 
-   for i, d in ipairs(self.sorted) do
-      print('#' .. i .. ' -> ' .. torch.type(d))
-   end
 end
 
 ----------------------------------------------------------------------
@@ -158,7 +206,6 @@ function DAG:updateOutput(input)
    self:nestedApply(
       function(nnm, i)
          self.node[nnm].input = i
-         -- nnm:updateOutput(i)
          self:rethrowErrors(nnm, self.node[nnm].index, 'updateOutput', i)
       end,
       self.inputModules,
@@ -178,7 +225,6 @@ function DAG:updateOutput(input)
             end
          end
          node.input = i
-         -- nnm:updateOutput(i)
          self:rethrowErrors(nnm, self.node[nnm].index, 'updateOutput', i)
       end
    end
@@ -192,12 +238,13 @@ function DAG:updateOutput(input)
 end
 
 function DAG:updateGradInput(input, gradOutput)
-   assert(self.sorted, 'there has been a DAG structure change before a DAG:updateGradInput')
+   assert(self.sorted, 'There has been a DAG structure change before a DAG:updateGradInput')
 
    self:nestedApply(
       function(nnm, go)
-         -- nnm:updateGradInput(self.node[nnm].input, go)
-         self:rethrowErrors(nnm, self.node[nnm].index, 'updateGradInput', self.node[nnm].input, go)
+         local node = self.node[nnm]
+         node.gradOutput = go
+         self:rethrowErrors(nnm, node.index, 'updateGradInput', self.node[nnm].input, go)
       end,
       self.outputModules, gradOutput
    )
@@ -214,11 +261,10 @@ function DAG:updateGradInput(input, gradOutput)
    for k = #self.sorted, 1, -1 do
       local nnm = self.sorted[k]
       local node = self.node[nnm]
-      local pred, gradInputSucc = node.pred, node.gradInputSucc
+      local pred = node.pred
 
-      if #gradInputSucc > 0 then
-         node.gradOutput = self:computeGradOutput(gradInputSucc)
-         -- nnm:updateGradInput(node.input, node.gradOutput)
+      if #node.gradInputSucc > 0 then
+         self:updateGradOutput(node)
          self:rethrowErrors(nnm, self.node[nnm].index, 'updateGradInput', node.input, node.gradOutput)
       end
 
@@ -243,14 +289,21 @@ 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')
+   assert(self.sorted, 'There has been a DAG structure change before a DAG:accGradParameters')
+
+   self:nestedApply(
+      function(nnm, go) self.node[nnm].gradOutput = go end,
+      self.outputModules, gradOutput
+   )
+
+   self:nestedApply(
+      function(nnm, i) self.node[nnm].input = i end,
+      self.inputModules, input
+   )
 
    for k = 1, #self.modules do
       local nnm = self.modules[k]
       local node = self.node[nnm]
-      -- nnm:accGradParameters(node.input, node.gradOutput, scale)
-      self:rethrowErrors(nnm, k, 'accGradParameters', node.input, self:computeGradOutput(node.gradInputSucc), scale)
+      self:rethrowErrors(nnm, k, 'accGradParameters', node.input, node.gradOutput, scale)
    end
 end
-
-----------------------------------------------------------------------