深度学习（七十）darknet 实现编写mobilenet源码

最新推荐文章于 2024-09-14 09:17:59 发布

hjimce

最新推荐文章于 2024-09-14 09:17:59 发布

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分类专栏：深度学习深度学习

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本文介绍如何在Darknet框架中实现深度可分离卷积，并以MobileNet为例详细展示了网络配置文件的编写过程及训练实验效果。

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一、添加一个新的网络层

(1)parse.c文件中函数string_to_layer_type，添加网络层类型解析：

if (strcmp(type, "[depthwise_convolutional]") == 0) return DEPTHWISE_CONVOLUTIONAL;

(2)darknet.h文件中枚举类型LAYER_TYPE，添加网络层枚举类型：

DEPTHWISE_CONVOLUTIONAL；

(3)parse.c文件中函数parse_network_cfg添加网络层解析后进行构建：

        LAYER_TYPE lt = string_to_layer_type(s->type);
if (lt == DEPTHWISE_CONVOLUTIONAL) {
l = parse_depthwise_convolutional(options, params);//自己编写的函数，主要调用了make layer
}
        else if(lt == CONVOLUTIONAL){
            l = parse_convolutional(options, params);

(4)parse.c 添加参数读取网络层加载文件

A、添加网络层读取参数函数

void load_weights_upto(network *net, char *filename, int start, int cutoff)
load depthwise weights；

B、把参数读取到内存上:

void load_depthwise_convolutional_weights(layer l, FILE *fp)；

C、以及修改depthwise_convolutional_kenel.cu把读取后的cpu参数拷贝到显存上：

void push_depthwise_convolutional_layer(depthwise_convolutional_layer layer)；

(5)parse.c添加参数保存功能：

void save_weights_upto(network net, char *filename, int cutoff)：
void save_depthwise_convolutional_weights(layer l, FILE *fp)；
void pull_depthwise_convolutional_layer(depthwise_convolutional_layer layer)；

(6)添加network.c中网络层:

int resize_network(network *net, int w, int h)的resize：
if (l.type==DEPTHWISE_CONVOLUTIONAL)
{
resize_depthwise_convolutional_layer(&l, w, h);
}

(7)另外在多卡异步训练的时候，network_kernels.cu的好几个函数也要添加depth_convolutional参数相关的更新设置。

总结为一句话：直接搜索项目中调用：CONVOLUTIONAL的关键子

有调用到卷积层枚举类型的地方，可分离卷积层也要添加相对应的功能。

(8)darknet使用须知：darknet的网络配置文件中的学习率、batch并不是我们平时所说的学习率、batch_size。网络更新所用的学习率为：learning_rate/batch_size,所以学习率不能太小，比如如果学习率设置为0.01，batch=128，那么实际计算的学习率就是0.000078，非常小的一个数值，基本上就是更新不了

二、编写网络层代码:depthwise_convolutional_kernels.cu、depthwise_convolutional_layer.c、depthwise_convolutional_layer.h

三、编写mobilenet网络结构文件：

[net]
batch=32
subdivisions=1
height=224
width=224
channels=3
momentum=0.9
decay=0.000
max_crop=320

learning_rate=0.1
policy=poly
power=3
max_batches=1600000

#conv1 
[convolutional]
batch_normalize=1
filters=32
size=3
stride=2
pad=1
activation=relu

#conv2_1/dw
[depthwise_convolutional]
batch_normalize=1
size=3
stride=1
pad=1
activation=relu

#conv2_1/sep
[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=0
activation=relu


#conv2_2/dw
[depthwise_convolutional]
batch_normalize=1
size=3
stride=2
pad=1
activation=relu

#conv2_2/sep
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=0
activation=relu

#conv3_1/dw
[depthwise_convolutional]
batch_normalize=1
size=3
stride=1
pad=1
activation=relu


#conv3_1/sep
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=0
activation=relu

#conv3_2/dw
[depthwise_convolutional]
batch_normalize=1
size=3
stride=2
pad=1
activation=relu

#conv3_2/sep
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=0
activation=relu

#conv4_1/dw
[depthwise_convolutional]
batch_normalize=1
size=3
stride=1
pad=1
activation=relu

#conv4_1/sep
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=0
activation=relu

#conv4_2/dw
[depthwise_convolutional]
batch_normalize=1
size=3
stride=2
pad=1
activation=relu

#conv4_2/sep
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=0
activation=relu

#conv5_1/dw
[depthwise_convolutional]
batch_normalize=1
size=3
stride=1
pad=1
activation=relu

#conv5_1/sep
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=0
activation=relu

#conv5_2/dw
[depthwise_convolutional]
batch_normalize=1
size=3
stride=1
pad=1
activation=relu

#conv5_2/sep
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=0
activation=relu

#conv5_3/dw
[depthwise_convolutional]
batch_normalize=1
size=3
stride=1
pad=1
activation=relu

#conv5_3/sep
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=0
activation=relu

#conv5_4/dw
[depthwise_convolutional]
batch_normalize=1
size=3
stride=1
pad=1
activation=relu

#conv5_4/sep
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=0
activation=relu

#conv5_5/dw
[depthwise_convolutional]
batch_normalize=1
size=3
stride=1
pad=1
activation=relu

#conv5_5/sep
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=0
activation=relu

#conv5_6/dw
[depthwise_convolutional]
batch_normalize=1
size=3
stride=2
pad=1
activation=relu

#conv5_6/sep
[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=0
activation=relu

#conv6/dw
[depthwise_convolutional]
batch_normalize=1
size=3
stride=1
pad=1
activation=relu

#conv6/sep
[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=0
activation=relu

#pool6
[avgpool]

#fc7
[convolutional]
filters=1000
size=1
stride=1
pad=0
activation=leaky

[softmax]
groups=1

[cost]

四、imagenet训练实验

1、训练一天后,经过两轮多的epoch后,精度: