1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
|
--[[
This file implements Dynamic K Max Pooling as described in the paper:
"A Convolutional Neural Network for Modelling Sentences"
by Nal Kalchbrenner, Edward Grefenstette, Phil Blunsom
The operation is simply selecting the k highest values out of a sequence.
k can be a calculated value or pre-defined
The value of k can be calulated as in the paper by using:
k_top as minK
(L-l)/L as factor
Where:
k_top is the desired sequence length at the end of the convolution part,
L is the total number of layers,
l is this layers number
]]
local TemporalDynamicKMaxPooling, parent = torch.class('nn.TemporalDynamicKMaxPooling', 'nn.Module')
function TemporalDynamicKMaxPooling:__init(minK, factor)
parent.__init(self)
self.minK = minK
self.factor = factor or 0
end
function TemporalDynamicKMaxPooling:updateOutput(input)
assert(input:dim() == 2 or input:dim() == 3, 'Only 2D or 3D(batch mode) accepted')
local seqDim = input:dim()-1
local k = math.max(self.minK, math.ceil(self.factor*input:size(seqDim)))
assert(input:size(seqDim) >= self.minK, 'Input sequence length (' .. input:size(seqDim) .. ') too small for desired k value (' .. k .. ')')
-- Sort input in descending order
local sorted, allIndices = input:sort(seqDim,true)
-- Reduce the indices to only include the top-k and return to original order by sorting
self.indices = allIndices:narrow(seqDim, 1, k):sort(seqDim)
self.output = input:gather(seqDim, self.indices)
return self.output
end
function TemporalDynamicKMaxPooling:updateGradInput(input, gradOutput)
if self.gradInput then
local seqDim = input:dim()-1
self.gradInput:resizeAs(input)
self.gradInput:zero()
-- Using the previously stored indices, add the gradOutputs to their respective
-- input indices in the self.gradInput buffer
local updateValues = self.gradInput:gather(seqDim, self.indices)
updateValues:add(gradOutput)
self.gradInput:scatter(seqDim, self.indices, updateValues)
return self.gradInput
end
end
function TemporalDynamicKMaxPooling:clearState()
nn.utils.clear(self, 'indices')
return parent.clearState(self)
end
|
