(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(, , , , , , , ))
net:add(nn.SpatialBatchNormalization())
net:add(nn.ReLU())
net:add(nn.SpatialMaxPooling(, , , , , )) net:add(nn.Inception{
inputSize = ,
kernelSize = {, },
kernelStride = {, },
outputSize = {, },
reduceSize = {, , , },
pool = nn.SpatialMaxPooling(, , , , , ),
batchNorm = true
}) net:add(nn.Inception{
inputSize = ,
kernelSize = {, },
kernelStride = {, },
outputSize = {, },
reduceSize = {, , , },
pool = nn.SpatialLPPooling(, , , , , ),
batchNorm = false
}) net:add(nn.SpatialAveragePooling(, ))
net:add(nn.View())
net:add(nn.Linear(, ))
net:add(nn.Normalize()) return net
end torch.setdefaulttensortype('torch.FloatTensor') local model = createModel() --print(model)
tt =
model:apply(function(module)
tt = tt +
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函数的更多相关文章
- R语言中apply函数
前言 刚开始接触R语言时,会听到各种的R语言使用技巧,其中最重要的一条就是不要用循环,效率特别低,要用向量计算代替循环计算. 那么,这是为什么呢?原因在于R的循环操作for和while,都是基于R语言 ...
- js中bind、call、apply函数的用法
最近一直在用 js 写游戏服务器,我也接触 js 时间不长,大学的时候用 js 做过一个 H3C 的 web的项目,然后在腾讯实习的时候用 js 写过一些奇怪的程序,自己也用 js 写过几个的网站.但 ...
- 关于call和apply函数的区别及用法
call和apply函数是function函数的基本属性,都可以用于更改函数对象和传递参数,是前端工程师常用的函数.具体使用方法请参考以下案列: 例如: 申明函数: var fn = function ...
- Javascript中bind、call、apply函数用法
js 里函数调用有 4 种模式:方法调用.正常函数调用.构造器函数调用.apply/call 调用. 同时,无论哪种函数调用除了你声明时定义的形参外,还会自动添加 2 个形参,分别是 this 和ar ...
- (2)apply函数及其源码
本文原创,转载请注明出处,本人Q1273314690(交流学习) 总结: 就是MARGIN决定了你的FUN调用几次,每次传递给你的是什么维度的内容,而...是传递给FUN的(每次调用的时候都会被传 ...
- Javascript中call函数和apply函数的使用
Javascript 中call函数和apply的使用: Javascript中的call函数和apply函数是对执行上下文进行切换,是将一个函数从当前执行的上下文切换到另一个对象中执行,例如: so ...
- 博文推荐】Javascript中bind、call、apply函数用法
[博文推荐]Javascript中bind.call.apply函数用法 2015-03-02 09:22 菜鸟浮出水 51CTO博客 字号:T | T 最近一直在用 js 写游戏服务器,我也接触 j ...
- call与apply函数
call与apply函数 1.为什么需要call与apply函数 Javascript中,每一个函数内部都有一个特殊的关键词this,其随着所处环境的不同其指向也是不同的. 函数的内部其this也是指 ...
- JavaScript Function.apply() 函数详解
apply()函数用于调用当前函数functionObject,并可同时使用指定对象thisObj作为本次函数执行时函数内部的this指针引用. 该函数属于Function对象,所有主流浏览器均支持该 ...
随机推荐
- 浮点数比较问题(float x 与 '零值'比较)
今天在牛客网上看到一道面试题,看完之后着实吃了一惊,自己平常都没有在意,看似简单的问题,实则考验了语言的基本功. 据说这是腾讯的面试题: float x 与“零值”比较的if语句为? if (x == ...
- ioctl函数,可以获取ip地址,修改ip地址,网卡地址等
部分转自http://www.cnblogs.com/zht-blog/p/4025903.html #include <sys/types.h>#include <sys/sock ...
- ANDROID 自动生成动态表格for
简单的栗子去了解这个自动生成的动态的控件(自动生成表格) /cs-Layout/res/layout/activity_main.xml <LinearLayout xmlns:android= ...
- 转:15 Best Responsive Web Design Testing Tools
Responsive Web Design is regarded as being the approach which suggests that web design and developme ...
- jQuery 中的事件绑定与取消绑定
1:在jQuery中使用bind方法进行事件的绑定,bind方法有两个参数,第一个参数是事件的类型例如click,change,keyup,keydown,blur,focus等.第二个参数是一个回调 ...
- C# and android
http://www.cnblogs.com/GoodHelper/archive/2011/07/08/android_socket_chart.html http://blog.csdn.net/ ...
- 【转】BT5无法找到软件安装包(提供可用的BT更新源)
像我这种一般想起才玩玩BT5的人,跟不上讯息的变化. 偶尔想尝试某个实验,然后去安装,发现提示无法找到软件安装包. 这个时候,应该注意到,现在已经从BT5到kali了,假如想我这种人,肯定比较少留意软 ...
- HDU-2059龟兔赛跑(基础方程DP-遍历之前的所有状态)
Problem Description 据说在很久很久以前,可怜的兔子经历了人生中最大的打击——赛跑输给乌龟后,心中郁闷,发誓要报仇雪恨,于是躲进了杭州下沙某农业园卧薪尝胆潜心修炼,终于练成了绝技,能 ...
- BZOJ 3514 (动态树)
这两天终于基本理解了Link-Cut Tree这种神一般的东西.然后就来做这道题了. 原题是CodeChef上的.CodeChef上没有强制在线,且时限更宽松,所以似乎用莫队一样的算法把询问分组就能水 ...
- 转:史上最全最强SpringMVC详细示例实战教程
一.SpringMVC基础入门,创建一个HelloWorld程序 1.首先,导入SpringMVC需要的jar包. 2.添加Web.xml配置文件中关于SpringMVC的配置 <!--conf ...