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Now deals when a module whose input is a table has multiple successors.
author
Francois Fleuret
<francois@fleuret.org>
Fri, 13 Jan 2017 21:54:37 +0000
(22:54 +0100)
committer
Francois Fleuret
<francois@fleuret.org>
Fri, 13 Jan 2017 21:54:37 +0000
(22:54 +0100)
dagnn.lua
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diff --git
a/dagnn.lua
b/dagnn.lua
index
de9d29b
..
0e7e8b0
100755
(executable)
--- a/
dagnn.lua
+++ b/
dagnn.lua
@@
-26,7
+26,7
@@
local DAG, parent = torch.class('nn.DAG', 'nn.Container')
function DAG:__init()
parent.__init(self)
-- Nodes are indexed by the module they contain
function DAG:__init()
parent.__init(self)
-- Nodes are indexed by the module they contain
- self.node = {
}
+ self.node = {}
end
-- Apply f on t recursively; use the corresponding elements from args
end
-- Apply f on t recursively; use the corresponding elements from args
@@
-76,7
+76,7
@@
function DAG:putInOrder()
end
until nc == 0
end
until nc == 0
- self.sorted = {
}
+ self.sorted = {}
for m, d in pairs(distance) do
table.insert(self.sorted, { distance = d, nnm = m })
end
for m, d in pairs(distance) do
table.insert(self.sorted, { distance = d, nnm = m })
end
@@
-86,18
+86,35
@@
function DAG:putInOrder()
for i, a in ipairs(self.sorted) do self.sorted[i] = a.nnm end
end
for i, a in ipairs(self.sorted) do self.sorted[i] = a.nnm end
end
+-- This accumulate x in a where they are both nested tables of
+-- tensors. If first is true, set a = x.
+function DAG:nestedAccTensor(a, x, first)
+ if torch.type(x) == 'table' then
+ a = a or {}
+ for i in pairs(x) do
+ a[i] = self:nestedAccTensor(a[i], x[i], first)
+ end
+ else
+ if first then
+ if a then
+ a:resizeAs(x):copy(x)
+ else
+ a = x:clone()
+ end
+ else
+ a:add(x)
+ end
+ end
+ return a
+end
+
function DAG:updateGradOutput(node)
local gradInputSucc = node.gradInputSucc
if #gradInputSucc == 1 then
node.gradOutput = gradInputSucc[1]
elseif #gradInputSucc > 1 then
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])
+ for k = 1, #gradInputSucc do
+ node.gradOutput = self:nestedAccTensor(node.gradOutput, gradInputSucc[k], k == 1)
end
end
end
end
end
end
@@
-307,3
+324,13
@@
function DAG:accGradParameters(input, gradOutput, scale)
self:rethrowErrors(nnm, k, 'accGradParameters', node.input, node.gradOutput, scale)
end
end
self:rethrowErrors(nnm, k, 'accGradParameters', node.input, node.gradOutput, scale)
end
end
+
+function DAG:clearState()
+ self.sorted = nil
+ for _, node in pairs(self.node) do
+ node.gradInputSucc = nil
+ node.input = nil
+ node.gradOutput = nil
+ end
+ return parent.clearState(self)
+end