android开发分享Ubuntu16.04下实现darknet-yolov3训练自己的数据（含loss图、mAP计算）

记录一下本地编译darknet并用自己的数据集来训练yolov3的过程，最后补充了mAP的计算方法。

1.环境配置

首先CUDA和Cudnn是必备的，安装有很多教程就不多写了，opencv安装比较麻烦可以不用装

2.本地编译darknet

从github获取darknet

 git clone https://github.com/pjreddie/darknet cd darknet

修改Makefile文件

 GPU=1 #如果使用GPU设置为1，CPU设置为0 CUDNN=1  #如果使用CUDNN设置为1，否则为0 OPENCV=0 #如果调用摄像头，还需要设置OPENCV为1，否则为0 OPENMP=0  #如果使用OPENMP设置为1，否则为0 DEBUG=0  #如果使用DEBUG设置为1，否则为0

在darknet文件夹下编译

 make

下载yolov3的预训练模型

 wget https://pjreddie.com/media/files/yolov3.weights

测试是否编译成功

 ./darknet detector test cfg/coco.data cfg/yolov3.cfg yolov3.weights data/dog.jpg

3.准备数据集

一般是先准备VOC格式的数据集，然后通过一些脚android开发分享Ubuntu16.04下实现darknet-yolov3训练自己的数据（含loss图、mAP计算）件转化成可用于训练的版本

在darknet文件夹下创建dateset文件夹，内部结构为：

 dataset   ---JPEGImages#存放原图像（免费精选名字大全只要和xml对应就行，不用规范化）     ---Annotations#存放图像对应的xml文件     ---ImageSets/Main # 存放训练/验证图像的txt文件（脚本生成）

将图像数据放入JPEGImages，xml的标注文件放入Annotations，然后新建一个py文件，随便命名（如：maketxt.py）

 import os import random  trainval_percent = 0.1 train_percent = 0.9 xmlfilepath = 'Annotations' txtsavepath = 'ImageSetsMain' total_xml = os.listdir(xmlfilepath) num = len(total_xml) list = range(num) tv = int(num * trainval_percent) tr = int(tv * train_percent) trainval = random.sample(list, tv) train = random.sample(trainval, tr)  ftrainval = open('ImageSets/Main/trainval.txt', 'w') ftest = open('ImageSets/Main/test.txt', 'w') ftrain = open('ImageSets/Main/train.txt', 'w') fval = open('ImageSets/Main/val.txt', 'w')  for i in list:     name = total_xml[i][:-4] + 'n'     if i in trainval:         ftrainval.write(name)         if i in train:             ftest.write(name)         else:             fval.write(name)     else:         ftrain.write(name)  ftrainval.close() ftrain.close() fval.close() ftest.close()

运行后会在ImageSets/Main路径下生成train.txt,val.txt,test.txt和trainval.txt四个必备的txt文件

转换VOC格式的数据集为darknet的格式（点坐标进行归一化https://blog.csdn.net/hesongzefairy/article/details/104443573）

在darknet文件夹中创建一个py文件，随便命名，需要修改的地方看注释（修改自darknet/scripts/voc_label.py）

 import xml.etree.ElementTree as ET import pickle import os from os import listdir, getcwd from os.path import join   #源代码sets=[('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test')] sets=[('dataset', 'train')]  # 改成自己建立的文件夹免费精选名字大全，若生成测试的train改成test   classes = ["person", "car"] # 改成自己的类别，和fastrcnn系不同，不用+1   def convert(size, box):     dw = 1./(size[0])     dh = 1./(size[1])     x = (box[0] + box[1])/2.0 - 1     y = (box[2] + box[3])/2.0 - 1     w = box[1] - box[0]     h = box[3] - box[2]     x = x*dw     w = w*dw     y = y*dh     h = h*dh     return (x,y,w,h)   def convert_annotation(year, image_id):     in_file = open('myData/Annotations/%s.xml'%(image_id))  VOCdevkit/VOC%s/Annotations/%s.xml     out_file = open('myData/labels/%s.txt'%(image_id), 'w')  VOCdevkit/VOC%s/labels/%s.txt     tree=ET.parse(in_file)     root = tree.getroot()     size = root.find('size')     w = int(size.find('width').text)     h = int(size.find('height').text)       for obj in root.iter('object'):         difficult = obj.find('difficult').text         cls = obj.find('name').text         if cls not in classes or int(difficult)==1:             continue         cls_id = classes.index(cls)         xmlbox = obj.find('bndbox')         b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text), float(xmlbox.find('ymax').text))         bb = convert((w,h), b)         out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + 'n')   # wd = getcwd()   # for year, image_set in sets: #    if not os.path.exists('/labels/'):  # 改成自己建立的myData #       os.makedirs('myData/labels/') #    image_ids = open('myData/ImageSets/Main/%s.txt'%(image_set)).read().strip().split() #    list_file = open('myData/%s_%s.txt'%(year, image_set), 'w') #    for image_id in image_ids: #        list_file.write('%s/myData/JPEGImages/%s.jpgn'%(wd, image_id)) #        convert_annotation(year, image_id) #    list_file.close()

运行之后，会在dataset文件夹中生成dataset_train.txt（记录了训练数据的绝对路径）

4.修改配置文件

修改cfg/voc.data

 classes= 2 #改为自己的分类个数  train  = /home/XXX/darknet/dataset/dataset_train.txt # 记录绝对路径的txt names = /home/XXX/darknet/dataset/myData.names       # 自己创建，记录类别名称 backup = /home/XXX/darknet/dataset/weights           # 权重保存路径

修改cfg/yolov3-voc.cfg

通过vim打开，用/yolo搜索

对每个yolo搜索点上的conv层，修改filter为3*（5+len（classes）），yolo层修改classes即可

同时文件最上部分可修改一些训练参数

 # Testing            ### 测试模式                                           # batch=1 # subdivisions=1 # Training           ### 训练模式，每次前向的图片数目 = batch/subdivisions  batch=64 subdivisions=16 width=416            ### 网络的输入宽、高、通道数 height=416 channels=3 momentum=0.9         ### 动量  decay=0.0005         ### 权重衰减 angle=0 saturation = 1.5     ### 饱和度 exposure = 1.5       ### 曝光度  hue=.1               ### 色调 learning_rate=0.001  ### 学习率  burn_in=1000         ### 学习率控制的参数 max_batches = 50200  ### 迭代次数                                           policy=steps         ### 学习率策略  steps=40000,45000    ### 学习率变动步长

5.开始训练

准备darknet权重

 wget https://pjreddie.com/media/files/darknet53.conv.74

启动训练（指定gpu）

 ./darknet detector train cfg/voc.data cfg/yolov3-voc.cfg darknet53.conv.74 -gpu 1

启动训练（保存训练log，用于后续画图）

 ./darknet detector train cfg/voc.data cfg/yolov3-voc.cfg darknet53.conv.74 -gpu 1 2>&1 | tee logs/train_yolov3.log

从停止处重新训练

 ./darknet detector train cfg/voc.data cfg/yolov3-voc.cfg darknet53.conv.74 -gups 0 myData/weights/my_yolov3.backup

6.训练完成后加载log文件画loss图和iou图

 # coding=utf-8 import os import pandas as pd import numpy as np import matplotlib.pyplot as plt import logging   logging.basicConfig(     level=logging.DEBUG,     format='%(asctime)s %(levelname)s: %(message)s',     datefmt='%Y-%m-%d %H:%M:%S' ) logger = logging.getLogger(__name__)     class Yolov3LogVisualization:       def __init__(self, log_path, result_dir):           self.log_path = log_path         self.result_dir = result_dir       def extract_log(self, save_log_path, key_word):         with open(self.log_path, 'r') as f:             with open(save_log_path, 'w') as train_log:                 next_skip = False                 for line in f:                     if next_skip:                         next_skip = False                         continue                     # 去除多gpu的同步log                     if 'Syncing' in line:                         continue                     # 去除除零错误的log                     if 'nan' in line:                         continue                     if 'Saving weights to' in line:                         next_skip = True                         continue                     if key_word in line:                         train_log.write(line)         f.close()         train_log.close()       def parse_loss_log(self, log_path, line_num=2000):         # 用于设置忽略前多少步，上千几百的太大了，所以从几一下开始。          result = pd.read_csv(log_path,skiprows=[x for x in range(line_num) if (x<1500)],                              error_bad_lines=False, names=['loss', 'avg', 'rate', 'seconds', 'images'])         result['loss'] = result['loss'].str.split(' ').str.get(1)         result['avg'] = result['avg'].str.split(' ').str.get(1)         result['rate'] = result['rate'].str.split(' ').str.get(1)         result['seconds'] = result['seconds'].str.split(' ').str.get(1)         result['images'] = result['images'].str.split(' ').str.get(1)           result['loss'] = pd.to_numeric(result['loss'])         result['avg'] = pd.to_numeric(result['avg'])         result['rate'] = pd.to_numeric(result['rate'])         result['seconds'] = pd.to_numeric(result['seconds'])         result['images'] = pd.to_numeric(result['images'])         return result       def gene_loss_pic(self, pd_loss):         fig = plt.figure()         ax = fig.add_subplot(1, 1, 1)         ax.plot(pd_loss['avg'].values, label='avg_loss')         ax.legend(loc='best')         ax.set_title('The loss curves')         ax.set_xlabel('batches')         fig.savefig(self.result_dir + '/avg_loss')         logger.info('save iou loss done')       def loss_pic(self):         train_log_loss_path = os.path.join(self.result_dir, 'train_log_loss.txt')         self.extract_log(train_log_loss_path, 'images')         pd_loss = self.parse_loss_log(train_log_loss_path)         self.gene_loss_pic(pd_loss)       def parse_iou_log(self, log_path, line_num=2000):         result = pd.read_csv(log_path, skiprows=[x for x in range(line_num) if (x % 10 == 0 or x % 10 == 9)],                              error_bad_lines=False,                              names=['Region Avg IOU', 'Class', 'Obj', 'No Obj', 'Avg Recall', 'count'])         result['Region Avg IOU'] = result['Region Avg IOU'].str.split(': ').str.get(1)         result['Class'] = result['Class'].str.split(': ').str.get(1)         result['Obj'] = result['Obj'].str.split(': ').str.get(1)         result['No Obj'] = result['No Obj'].str.split(': ').str.get(1)         result['Avg Recall'] = result['Avg Recall'].str.split(': ').str.get(1)         result['count'] = result['count'].str.split(': ').str.get(1)           result['Region Avg IOU'] = pd.to_numeric(result['Region Avg IOU'])         result['Class'] = pd.to_numeric(result['Class'])         result['Obj'] = pd.to_numeric(result['Obj'])         result['No Obj'] = pd.to_numeric(result['No Obj'])         result['Avg Recall'] = pd.to_numeric(result['Avg Recall'])         result['count'] = pd.to_numeric(result['count'])         return result       def gene_iou_pic(self, pd_loss):         fig = plt.figure()         ax = fig.add_subplot(1, 1, 1)         ax.plot(pd_loss['Region Avg IOU'].values, label='Region Avg IOU')         # ax.plot(result['Class'].values,label='Class')         # ax.plot(result['Obj'].values,label='Obj')         # ax.plot(result['No Obj'].values,label='No Obj')         # ax.plot(result['Avg Recall'].values,label='Avg Recall')         # ax.plot(result['count'].values,label='count')         ax.legend(loc='best')         ax.set_title('The Region Avg IOU curves')         ax.set_xlabel('batches')         fig.savefig(self.result_dir + '/region_avg_iou')         logger.info('save iou pic done')       def iou_pic(self):         train_log_loss_path = os.path.join(self.result_dir, 'train_log_iou.txt')         self.extract_log(train_log_loss_path, 'IOU')         pd_loss = self.parse_iou_log(train_log_loss_path)         self.gene_iou_pic(pd_loss)     if __name__ == '__main__':     log_path = '/home/studieren/论文/darknet/log_analysis/train_yolov3.log'     result_dir = '/home/studieren/论文/darknet/log_analysis'     logVis = Yolov3LogVisualization(log_path, result_dir)     logVis.loss_pic()     logVis.iou_pic()

7.mAP计算

参考方法https://blog.csdn.net/Gentleman_Qin/article/details/84800188

这个代码运行需要python2.7，但是现在都是python3了，谁还用2

需要修改的地方：

1. print的用法，注释掉或者修改成python3格式都可以

2. 读写文件的方法，open仅用r或者w会报错，改成rb和wb（readline的位置不要改）

具体的根据报错来修改一下就行了，最终会输出AP值