blob: 5012c2af239cf09e5a9c302984e1ea525190ca2c (
plain)
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
66
67
68
69
70
71
|
local MarginRankingCriterion, parent = torch.class('nn.MarginRankingCriterion', 'nn.Criterion')
function MarginRankingCriterion:__init(margin)
parent.__init(self)
margin=margin or 1
self.margin = margin
self.gradInput = {torch.Tensor(1), torch.Tensor(1)}
end
function MarginRankingCriterion:updateOutput(input,y)
if type(input[1]) == "number" then
self.output=math.max(0, -y*(input[1]-input[2]) + self.margin )
else
if type(self.output) == "number" then
self.output = input[1]:clone()
end
self.output = self.output or input[1]:clone()
self.output:resizeAs(input[1])
self.output:copy(input[1])
self.output:add(-1, input[2])
self.output:mul(-y)
self.output:add(self.margin)
self.mask = self.mask or self.output:clone()
self.mask:resizeAs(self.output)
self.mask:copy(self.output)
self.mask:ge(self.output, 0.0)
self.output:cmul(self.mask)
end
return self.output
end
function MarginRankingCriterion:updateGradInput(input, y)
if type(input[1]) == "number" then
local dist = -y*(input[1][1]-input[2][1]) + self.margin
if dist < 0 then
self.gradInput[1][1]=0;
self.gradInput[2][1]=0;
else
self.gradInput[1][1]=-y
self.gradInput[2][1]=y
end
else
self.dist = self.dist or input[1].new()
self.dist = self.dist:resizeAs(input[1]):copy(input[1])
local dist = self.dist
dist:add(-1, input[2])
dist:mul(-y)
dist:add(self.margin)
self.mask = self.mask or input[1].new()
self.mask = self.mask:resizeAs(input[1]):copy(dist)
local mask = self.mask
mask:ge(dist, 0)
self.gradInput[1]:resize(dist:size())
self.gradInput[2]:resize(dist:size())
self.gradInput[1]:copy(mask)
self.gradInput[1]:mul(-y)
self.gradInput[2]:copy(mask)
self.gradInput[2]:mul(y)
end
return self.gradInput
end
|
