深度学习（七十）darknet 实现编写mobilenet源码

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=32subdivisions=1height=224width=224channels=3momentum=0.9decay=0.000max_crop=320learning_rate=0.1policy=polypower=3max_batches=1600000#conv1 [convolutional]batch_normalize=1filters=32size=3stride=2pad=1activation=relu#conv2_1/dw[depthwise_convolutional]batch_normalize=1size=3stride=1pad=1activation=relu#conv2_1/sep[convolutional]batch_normalize=1filters=64size=1stride=1pad=0activation=relu#conv2_2/dw[depthwise_convolutional]batch_normalize=1size=3stride=2pad=1activation=relu#conv2_2/sep[convolutional]batch_normalize=1filters=128size=1stride=1pad=0activation=relu#conv3_1/dw[depthwise_convolutional]batch_normalize=1size=3stride=1pad=1activation=relu#conv3_1/sep[convolutional]batch_normalize=1filters=128size=1stride=1pad=0activation=relu#conv3_2/dw[depthwise_convolutional]batch_normalize=1size=3stride=2pad=1activation=relu#conv3_2/sep[convolutional]batch_normalize=1filters=256size=1stride=1pad=0activation=relu#conv4_1/dw[depthwise_convolutional]batch_normalize=1size=3stride=1pad=1activation=relu#conv4_1/sep[convolutional]batch_normalize=1filters=256size=1stride=1pad=0activation=relu#conv4_2/dw[depthwise_convolutional]batch_normalize=1size=3stride=2pad=1activation=relu#conv4_2/sep[convolutional]batch_normalize=1filters=512size=1stride=1pad=0activation=relu#conv5_1/dw[depthwise_convolutional]batch_normalize=1size=3stride=1pad=1activation=relu#conv5_1/sep[convolutional]batch_normalize=1filters=512size=1stride=1pad=0activation=relu#conv5_2/dw[depthwise_convolutional]batch_normalize=1size=3stride=1pad=1activation=relu#conv5_2/sep[convolutional]batch_normalize=1filters=512size=1stride=1pad=0activation=relu#conv5_3/dw[depthwise_convolutional]batch_normalize=1size=3stride=1pad=1activation=relu#conv5_3/sep[convolutional]batch_normalize=1filters=512size=1stride=1pad=0activation=relu#conv5_4/dw[depthwise_convolutional]batch_normalize=1size=3stride=1pad=1activation=relu#conv5_4/sep[convolutional]batch_normalize=1filters=512size=1stride=1pad=0activation=relu#conv5_5/dw[depthwise_convolutional]batch_normalize=1size=3stride=1pad=1activation=relu#conv5_5/sep[convolutional]batch_normalize=1filters=512size=1stride=1pad=0activation=relu#conv5_6/dw[depthwise_convolutional]batch_normalize=1size=3stride=2pad=1activation=relu#conv5_6/sep[convolutional]batch_normalize=1filters=1024size=1stride=1pad=0activation=relu#conv6/dw[depthwise_convolutional]batch_normalize=1size=3stride=1pad=1activation=relu#conv6/sep[convolutional]batch_normalize=1filters=1024size=1stride=1pad=0activation=relu#pool6[avgpool]#fc7[convolutional]filters=1000size=1stride=1pad=0activation=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:

2、训练两天后,迭代第二天结果:

3\又训练了一天多: