+--[[
+
+ Copyright (c) 2016 Idiap Research Institute, http://www.idiap.ch/
+ Written by Francois Fleuret <francois.fleuret@idiap.ch>
+
+ This file is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License version 3 as
+ published by the Free Software Foundation.
+
+ It is distributed in the hope that it will be useful, but WITHOUT
+ ANY WARRANTY; without even the implied warranty of MERCHANTABILITY
+ or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public
+ License for more details.
+
+ You should have received a copy of the GNU General Public License
+ along with this file. If not, see <http://www.gnu.org/licenses/>.
+
+]]--
+
require 'torch'
require 'nn'
function DAG:__init()
parent.__init(self)
- -- Nodes are indexed by the module they encompass
+ -- Nodes are indexed by the module they contain
self.node = { }
end
function DAG:createNode(nnm)
if not self.node[nnm] then
self:add(nnm) -- Add it to the object as a Container
- self.node[nnm] = {}
- self.node[nnm].succ = {}
- self.node[nnm].pred = {}
+ local node = {}
+ node.succ = {}
+ node.pred = {}
+ node.index = #self.modules
+ self.node[nnm] = node
end
end
table.insert(self.node[nnma].succ, nnmb)
end
--- 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:nestApply(f, t, a1, a2)
+-- 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:nestApply(f, s, a1 and a1[k], a2 and a2[k])
+ result[k] = self:nestedApply(f, s, args and args[k])
end
return result
else
- return f(t, a1, a2)
+ return f(t, args)
end
end
function DAG:setInput(i)
self.sorted = nil
self.inputModules = i
- self:nestApply(
+ self:nestedApply(
function(nnm)
if #self.node[nnm].succ == 0 then
error('Input modules must have outgoing edges.')
function DAG:setOutput(o)
self.sorted = nil
self.outputModules = o
- self:nestApply(
+ self:nestedApply(
function(nnm)
if #self.node[nnm].pred == 0 then
error('Output module must have incoming edges.')
return
end
- -- First, we sort the nodes according to the DAG order
-
local distance = {}
-
- self:nestApply(function(m) distance[m] = 1 end, self.inputModules)
+ self:nestedApply(function(m) distance[m] = 1 end, self.inputModules)
local nc
-
repeat
nc = 0
for nnma, node in pairs(self.node) do
for i, a in ipairs(self.sorted) do self.sorted[i] = a.nnm end
end
+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()
+ end
+ end
+ end
+ return gi
+end
+
function DAG:print()
self:putInOrder()
end
end
+----------------------------------------------------------------------
+
function DAG:updateOutput(input)
self:putInOrder()
- self:nestApply(
+ self:nestedApply(
function(nnm, i)
self.node[nnm].input = i
- nnm:updateOutput(i)
+ -- nnm:updateOutput(i)
+ self:rethrowErrors(nnm, self.node[nnm].index, 'updateOutput', i)
end,
self.inputModules,
input
end
end
node.input = i
- nnm:updateOutput(i)
+ -- nnm:updateOutput(i)
+ self:rethrowErrors(nnm, self.node[nnm].index, 'updateOutput', i)
end
end
- self.output = self:nestApply(function(m) return m.output end, self.outputModules)
+ self.output = self:nestedApply(
+ function(m) return m.output end,
+ self.outputModules
+ )
return self.output
end
function DAG:updateGradInput(input, gradOutput)
- self:putInOrder()
+ assert(self.sorted, 'there has been a DAG structure change before a DAG:updateGradInput')
- self:nestApply(
- function(nnm, go) nnm:updateGradInput(self.node[nnm].input, go) end,
+ 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)
+ end,
self.outputModules, gradOutput
)
+ self:nestedApply(
+ function(nnm, i) self.node[nnm].input = i end,
+ self.inputModules, input
+ )
+
for _, node in pairs(self.node) do
node.gradInputSucc = {}
end
for k = #self.sorted, 1, -1 do
local nnm = self.sorted[k]
local node = self.node[nnm]
- local pred, succ, gradInputSucc = node.pred, node.succ, node.gradInputSucc
+ local pred, gradInputSucc = node.pred, node.gradInputSucc
if #gradInputSucc > 0 then
- -- We update nnm:gradInput
- 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()
- end
- end
- end
- nnm:updateGradInput(node.input, gi)
+ node.gradOutput = self:computeGradOutput(gradInputSucc)
+ -- nnm:updateGradInput(node.input, node.gradOutput)
+ self:rethrowErrors(nnm, self.node[nnm].index, 'updateGradInput', node.input, node.gradOutput)
end
-- We fill the gradInputSucc of our predecessors
end
end
- self.gradInput = self:nestApply(function(m) return m.gradInput end, self.inputModules)
+ self.gradInput = self:nestedApply(function(m) return m.gradInput end, self.inputModules)
return self.gradInput
end
-return DAG
+function DAG:accGradParameters(input, gradOutput, scale)
+ scale = scale or 1
+
+ assert(self.sorted, 'there has been a DAG structure change before a DAG:accGradParameters')
+
+ 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)
+ end
+end
+
+----------------------------------------------------------------------
projects / dagnn.git / blobdiff