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（原）torch的apply函数
转载请注明出处：
http://www.cnblogs.com/darkknightzh/p/6221633.html
torch中的apply函数通过可以不断遍历model的各个模块。实际上其使用的是深度优先算法。
其具体代码如下所示（代码见torch/install/share/lua/5.1/nn/Module.lua）：
-- Run a callback (called with the module as an argument) in preorder over this -- module and its children. -- function Module:apply(callback) callback(self) if self.modules then for _, module in ipairs(self.modules) do module:apply(callback) end end end
可见，apply递归调用自身，直到不存在模块为止（这样说不太合理）。
如下所示的测试代码：
require "dpnn" function createModel() local net = nn.Sequential() net:add(nn.SpatialConvolutionMM(3, 64, 7, 7, 2, 2, 3, 3)) net:add(nn.SpatialBatchNormalization(64)) net:add(nn.ReLU()) net:add(nn.SpatialMaxPooling(3, 3, 2, 2, 1, 1)) net:add(nn.Inception{ inputSize = 192, kernelSize = {3, 5}, kernelStride = {1, 1}, outputSize = {128, 32}, reduceSize = {96, 16, 32, 64}, pool = nn.SpatialMaxPooling(3, 3, 1, 1, 1, 1), batchNorm = true }) net:add(nn.Inception{ inputSize = 256, kernelSize = {3, 5}, kernelStride = {1, 1}, outputSize = {128, 64}, reduceSize = {96, 32, 64, 64}, pool = nn.SpatialLPPooling(256, 2, 3, 3, 1, 1), batchNorm = false }) net:add(nn.SpatialAveragePooling(7, 7)) net:add(nn.View(320)) net:add(nn.Linear(320, 128)) net:add(nn.Normalize(2)) return net end torch.setdefaulttensortype(‘torch.FloatTensor‘) local model = createModel() --print(model) tt = 0 model:apply(function(module) tt = tt + 1 print(tt, module) end)
其输出结果为：
1 nn.Sequential {
[input -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> (7) -> (8) -> (9) -> (10) -> output]
(1): nn.SpatialConvolutionMM(3 -> 64, 7x7, 2,2, 3,3)
(2): nn.SpatialBatchNormalization
(3): nn.ReLU
(4): nn.SpatialMaxPooling(3x3, 2,2, 1,1)
(5): nn.Inception @ nn.DepthConcat {
input
|`-> (1): nn.Sequential {
| [input -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> output]
| (1): nn.SpatialConvolution(192 -> 96, 1x1)
| (2): nn.SpatialBatchNormalization
| (3): nn.ReLU
| (4): nn.SpatialConvolution(96 -> 128, 3x3, 1,1, 1,1)
| (5): nn.SpatialBatchNormalization
| (6): nn.ReLU
| }
|`-> (2): nn.Sequential {
| [input -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> output]
| (1): nn.SpatialConvolution(192 -> 16, 1x1)
| (2): nn.SpatialBatchNormalization
| (3): nn.ReLU
| (4): nn.SpatialConvolution(16 -> 32, 5x5, 1,1, 2,2)
| (5): nn.SpatialBatchNormalization
| (6): nn.ReLU
| }
|`-> (3): nn.Sequential {
| [input -> (1) -> (2) -> (3) -> (4) -> output]
| (1): nn.SpatialMaxPooling(3x3, 1,1, 1,1)
| (2): nn.SpatialConvolution(192 -> 32, 1x1)
| (3): nn.SpatialBatchNormalization
| (4): nn.ReLU
| }
|`-> (4): nn.Sequential {
[input -> (1) -> (2) -> (3) -> output]
(1): nn.SpatialConvolution(192 -> 64, 1x1)
(2): nn.SpatialBatchNormalization
(3): nn.ReLU
}
... -> output
}
(6): nn.Inception @ nn.DepthConcat {
input
|`-> (1): nn.Sequential {
| [input -> (1) -> (2) -> (3) -> (4) -> output]
| (1): nn.SpatialConvolution(256 -> 96, 1x1)
| (2): nn.ReLU
| (3): nn.SpatialConvolution(96 -> 128, 3x3, 1,1, 1,1)
| (4): nn.ReLU
| }
|`-> (2): nn.Sequential {
| [input -> (1) -> (2) -> (3) -> (4) -> output]
| (1): nn.SpatialConvolution(256 -> 32, 1x1)
| (2): nn.ReLU
| (3): nn.SpatialConvolution(32 -> 64, 5x5, 1,1, 2,2)
| (4): nn.ReLU
| }
|`-> (3): nn.Sequential {
| [input -> (1) -> (2) -> (3) -> output]
| (1): nn.Sequential {
| [input -> (1) -> (2) -> (3) -> (4) -> output]
| (1): nn.Square
| (2): nn.SpatialAveragePooling(3x3, 1,1)
| (3): nn.MulConstant
| (4): nn.Sqrt
| }
| (2): nn.SpatialConvolution(256 -> 64, 1x1)
| (3): nn.ReLU
| }
|`-> (4): nn.Sequential {
[input -> (1) -> (2) -> output]
(1): nn.SpatialConvolution(256 -> 64, 1x1)
(2): nn.ReLU
}
... -> output
}
(7): nn.SpatialAveragePooling(7x7, 1,1)
(8): nn.View(320)
(9): nn.Linear(320 -> 128)
(10): nn.Normalize(2)
}
2 nn.SpatialConvolutionMM(3 -> 64, 7x7, 2,2, 3,3)
3 nn.SpatialBatchNormalization
4 nn.ReLU
5 nn.SpatialMaxPooling(3x3, 2,2, 1,1)
6 nn.Inception @ nn.DepthConcat {
input
|`-> (1): nn.Sequential {
| [input -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> output]
| (1): nn.SpatialConvolution(192 -> 96, 1x1)
| (2): nn.SpatialBatchNormalization
| (3): nn.ReLU
| (4): nn.SpatialConvolution(96 -> 128, 3x3, 1,1, 1,1)
| (5): nn.SpatialBatchNormalization
| (6): nn.ReLU
| }
|`-> (2): nn.Sequential {
| [input -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> output]
| (1): nn.SpatialConvolution(192 -> 16, 1x1)
| (2): nn.SpatialBatchNormalization
| (3): nn.ReLU
| (4): nn.SpatialConvolution(16 -> 32, 5x5, 1,1, 2,2)
| (5): nn.SpatialBatchNormalization
| (6): nn.ReLU
| }
|`-> (3): nn.Sequential {
| [input -> (1) -> (2) -> (3) -> (4) -> output]
| (1): nn.SpatialMaxPooling(3x3, 1,1, 1,1)
| (2): nn.SpatialConvolution(192 -> 32, 1x1)
| (3): nn.SpatialBatchNormalization
| (4): nn.ReLU
| }
|`-> (4): nn.Sequential {
[input -> (1) -> (2) -> (3) -> output]
(1): nn.SpatialConvolution(192 -> 64, 1x1)
(2): nn.SpatialBatchNormalization
(3): nn.ReLU
}
... -> output
}
7 nn.DepthConcat {
input
|`-> (1): nn.Sequential {
| [input -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> output]
| (1): nn.SpatialConvolution(192 -> 96, 1x1)
| (2): nn.SpatialBatchNormalization
| (3): nn.ReLU
| (4): nn.SpatialConvolution(96 -> 128, 3x3, 1,1, 1,1)
| (5): nn.SpatialBatchNormalization
| (6): nn.ReLU
| }
|`-> (2): nn.Sequential {
| [input -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> output]
| (1): nn.SpatialConvolution(192 -> 16, 1x1)
| (2): nn.SpatialBatchNormalization
| (3): nn.ReLU
| (4): nn.SpatialConvolution(16 -> 32, 5x5, 1,1, 2,2)
| (5): nn.SpatialBatchNormalization
| (6): nn.ReLU
| }
|`-> (3): nn.Sequential {
| [input -> (1) -> (2) -> (3) -> (4) -> output]
| (1): nn.SpatialMaxPooling(3x3, 1,1, 1,1)
| (2): nn.SpatialConvolution(192 -> 32, 1x1)
| (3): nn.SpatialBatchNormalization
| (4): nn.ReLU
| }
|`-> (4): nn.Sequential {
[input -> (1) -> (2) -> (3) -> output]
(1): nn.SpatialConvolution(192 -> 64, 1x1)
(2): nn.SpatialBatchNormalization
(3): nn.ReLU
}
... -> output
}
8 nn.Sequential {
[input -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> output]
(1): nn.SpatialConvolution(192 -> 96, 1x1)
(2): nn.SpatialBatchNormalization
(3): nn.ReLU
(4): nn.SpatialConvolution(96 -> 128, 3x3, 1,1, 1,1)
(5): nn.SpatialBatchNormalization
(6): nn.ReLU
}
9 nn.SpatialConvolution(192 -> 96, 1x1)
10 nn.SpatialBatchNormalization
11 nn.ReLU
12 nn.SpatialConvolution(96 -> 128, 3x3, 1,1, 1,1)
13 nn.SpatialBatchNormalization
14 nn.ReLU
15 nn.Sequential {
[input -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> output]
(1): nn.SpatialConvolution(192 -> 16, 1x1)
(2): nn.SpatialBatchNormalization
(3): nn.ReLU
(4): nn.SpatialConvolution(16 -> 32, 5x5, 1,1, 2,2)
(5): nn.SpatialBatchNormalization
(6): nn.ReLU
}
16 nn.SpatialConvolution(192 -> 16, 1x1)
17 nn.SpatialBatchNormalization
18 nn.ReLU
19 nn.SpatialConvolution(16 -> 32, 5x5, 1,1, 2,2)
20 nn.SpatialBatchNormalization
21 nn.ReLU
22 nn.Sequential {
[input -> (1) -> (2) -> (3) -> (4) -> output]
(1): nn.SpatialMaxPooling(3x3, 1,1, 1,1)
(2): nn.SpatialConvolution(192 -> 32, 1x1)
(3): nn.SpatialBatchNormalization
(4): nn.ReLU
}
23 nn.SpatialMaxPooling(3x3, 1,1, 1,1)
24 nn.SpatialConvolution(192 -> 32, 1x1)
25 nn.SpatialBatchNormalization
26 nn.ReLU
27 nn.Sequential {
[input -> (1) -> (2) -> (3) -> output]
(1): nn.SpatialConvolution(192 -> 64, 1x1)
(2): nn.SpatialBatchNormalization
(3): nn.ReLU
}
28 nn.SpatialConvolution(192 -> 64, 1x1)
29 nn.SpatialBatchNormalization
30 nn.ReLU
31 nn.Inception @ nn.DepthConcat {
input
|`-> (1): nn.Sequential {
| [input -> (1) -> (2) -> (3) -> (4) -> output]
| (1): nn.SpatialConvolution(256 -> 96, 1x1)
| (2): nn.ReLU
| (3): nn.SpatialConvolution(96 -> 128, 3x3, 1,1, 1,1)
| (4): nn.ReLU
| }
|`-> (2): nn.Sequential {
| [input -> (1) -> (2) -> (3) -> (4) -> output]
| (1): nn.SpatialConvolution(256 -> 32, 1x1)
| (2): nn.ReLU
| (3): nn.SpatialConvolution(32 -> 64, 5x5, 1,1, 2,2)
| (4): nn.ReLU
| }
|`-> (3): nn.Sequential {
| [input -> (1) -> (2) -> (3) -> output]
| (1): nn.Sequential {
| [input -> (1) -> (2) -> (3) -> (4) -> output]
| (1): nn.Square
| (2): nn.SpatialAveragePooling(3x3, 1,1)
| (3): nn.MulConstant
| (4): nn.Sqrt
| }
| (2): nn.SpatialConvolution(256 -> 64, 1x1)
| (3): nn.ReLU
| }
|`-> (4): nn.Sequential {
[input -> (1) -> (2) -> output]
(1): nn.SpatialConvolution(256 -> 64, 1x1)
(2): nn.ReLU
}
... -> output
}
32 nn.DepthConcat {
input
|`-> (1): nn.Sequential {
| [input -> (1) -> (2) -> (3) -> (4) -> output]
| (1): nn.SpatialConvolution(256 -> 96, 1x1)
| (2): nn.ReLU
| (3): nn.SpatialConvolution(96 -> 128, 3x3, 1,1, 1,1)
| (4): nn.ReLU
| }
|`-> (2): nn.Sequential {
| [input -> (1) -> (2) -> (3) -> (4) -> output]
| (1): nn.SpatialConvolution(256 -> 32, 1x1)
| (2): nn.ReLU
| (3): nn.SpatialConvolution(32 -> 64, 5x5, 1,1, 2,2)
| (4): nn.ReLU
| }
|`-> (3): nn.Sequential {
| [input -> (1) -> (2) -> (3) -> output]
| (1): nn.Sequential {
| [input -> (1) -> (2) -> (3) -> (4) -> output]
| (1): nn.Square
| (2): nn.SpatialAveragePooling(3x3, 1,1)
| (3): nn.MulConstant
| (4): nn.Sqrt
| }
| (2): nn.SpatialConvolution(256 -> 64, 1x1)
| (3): nn.ReLU
| }
|`-> (4): nn.Sequential {
[input -> (1) -> (2) -> output]
(1): nn.SpatialConvolution(256 -> 64, 1x1)
(2): nn.ReLU
}
... -> output
}
33 nn.Sequential {
[input -> (1) -> (2) -> (3) -> (4) -> output]
(1): nn.SpatialConvolution(256 -> 96, 1x1)
(2): nn.ReLU
(3): nn.SpatialConvolution(96 -> 128, 3x3, 1,1, 1,1)
(4): nn.ReLU
}
34 nn.SpatialConvolution(256 -> 96, 1x1)
35 nn.ReLU
36 nn.SpatialConvolution(96 -> 128, 3x3, 1,1, 1,1)
37 nn.ReLU
38 nn.Sequential {
[input -> (1) -> (2) -> (3) -> (4) -> output]
(1): nn.SpatialConvolution(256 -> 32, 1x1)
(2): nn.ReLU
(3): nn.SpatialConvolution(32 -> 64, 5x5, 1,1, 2,2)
(4): nn.ReLU
}
39 nn.SpatialConvolution(256 -> 32, 1x1)
40 nn.ReLU
41 nn.SpatialConvolution(32 -> 64, 5x5, 1,1, 2,2)
42 nn.ReLU
43 nn.Sequential {
[input -> (1) -> (2) -> (3) -> output]
(1): nn.Sequential {
[input -> (1) -> (2) -> (3) -> (4) -> output]
(1): nn.Square
(2): nn.SpatialAveragePooling(3x3, 1,1)
(3): nn.MulConstant
(4): nn.Sqrt
}
(2): nn.SpatialConvolution(256 -> 64, 1x1)
(3): nn.ReLU
}
44 nn.Sequential {
[input -> (1) -> (2) -> (3) -> (4) -> output]
(1): nn.Square
(2): nn.SpatialAveragePooling(3x3, 1,1)
(3): nn.MulConstant
(4): nn.Sqrt
}
45 nn.Square
46 nn.SpatialAveragePooling(3x3, 1,1)
47 nn.MulConstant
48 nn.Sqrt
49 nn.SpatialConvolution(256 -> 64, 1x1)
50 nn.ReLU
51 nn.Sequential {
[input -> (1) -> (2) -> output]
(1): nn.SpatialConvolution(256 -> 64, 1x1)
(2): nn.ReLU
}
52 nn.SpatialConvolution(256 -> 64, 1x1)
53 nn.ReLU
54 nn.SpatialAveragePooling(7x7, 1,1)
55 nn.View(320)
56 nn.Linear(320 -> 128)
57 nn.Normalize(2)
由上述结果可以看出，使用apply后，第1次输出整个模型，此处为最顶层的。
第2-5次输出：
2 nn.SpatialConvolutionMM(3 -> 64, 7x7, 2,2, 3,3)
3 nn.SpatialBatchNormalization
4 nn.ReLU
5 nn.SpatialMaxPooling(3x3, 2,2, 1,1)
为Inception之前的几个层。
第6次为nn.Inception @ nn.DepthConcat，第7次为nn.DepthConcat。此处是第一个Inceptioin层。
第8次为Inception的第一个nn.Sequential，第9-14次为该层的具体层。此时已经到了第一个最底层。
第15次为Inception的第二个nn.Sequential，第16-21次为该层的具体层。此时已经到了第二个最底层。
第22次为Inception的第三个nn.Sequential，第23-26次为该层的具体层。此时已经到了第三个最底层。
第27次为Inception的第四个nn.Sequential，第28-30次为该层的具体层。此时已经到了第四个最底层。
至此，第一个Inception层通过深度优先的方式遍历完毕。
第31次为nn.Inception @ nn.DepthConcat，第32次为nn.DepthConcat。此处是第二个Inceptioin层（注意，为了区分第一个Inception和第二个Inception层，这两个层具体结构不完全一样）。
第33次为Inception的第一个nn.Sequential，第34-37次为该层的具体层。此时已经到了第一个最底层。
第38次为Inception的第二个nn.Sequential，第39-42次为该层的具体层。此时已经到了第二个最底层。
第43次为Inception的第三个nn.Sequential。
第44次为第三个nn.Sequential的第一个小module（也是一个nn.Sequential）。第45-48依次遍历此nn.Sequential。到了最底层后遍历完毕。
第49-50为第三个nn.Sequential的最后两层。
第51次为Inception的第四个nn.Sequential，第52-53次为该层的具体层。此时已经到了第四个最底层。
至此，第二个Inception层通过深度优先的方式遍历完毕。
第54-57为最后的两个层。
由上面可以看出，apply采用的是深度优先的方式进行遍历。
（原）torch的apply函数
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