可视化中间参数需要用到训练时保存的log文件（命令中的路径根据自己实际修改）：
./darknet detector train pds/fish/cfg/fish.data pds/fish/cfg/yolov3-fish.cfg darknet53.conv.74 2>1 | tee visualization/train_yolov3.log
在使用脚本绘制变化曲线之前，需要先使用extract_log.py脚本，格式化log,用生成的新的log文件供可视化工具绘图，格式化log的extract_log.py脚本如下（和生成的log文件同一目录）：
# coding=utf-8# 该文件用来提取训练log，去除不可解析的log后使log文件格式化，生成新的log文件供可视化工具绘图import inspectimport osimport randomimport sysdef extract_log(log_file,new_log_file,key_word):with open(log_file, 'r') as f:with open(new_log_file, 'w') as train_log:#f = open(log_file)#train_log = open(new_log_file, 'w')for line in f:# 去除多gpu的同步logif 'Syncing' in line:continue# 去除除零错误的logif 'nan' in line:continueif key_word in line:train_log.write(line)f.close()train_log.close()extract_log('train_yolov3.log','train_log_loss.txt','images')extract_log('train_yolov3.log','train_log_iou.txt','IOU')
运行之后，会解析log文件的loss行和iou行得到两个txt文件
使用train_loss_visualization.py脚本可以绘制loss变化曲线
train_loss_visualization.py脚本如下（也是同一目录新建py文件）：
import pandas as pdimport numpy as npimport matplotlib.pyplot as plt#%matplotlib inlinelines =5124 #改为自己生成的train_log_loss.txt中的行数result = pd.read_csv('train_log_loss.txt', skiprows=[x for x in range(lines) if ((x%10!=9) |(x<1000))] ,error_bad_lines=False, names=['loss', 'avg', 'rate', 'seconds', 'images'])result.head()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.head()result.tail()# print(result.head())# print(result.tail())# print(result.dtypes)print(result['loss'])print(result['avg'])print(result['rate'])print(result['seconds'])print(result['images'])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'])result.dtypesfig = plt.figure()ax = fig.add_subplot(1, 1, 1)ax.plot(result['avg'].values,label='avg_loss')# ax.plot(result['loss'].values,label='loss')ax.legend(loc='best') #图列自适应位置ax.set_title('The loss curves')ax.set_xlabel('batches')fig.savefig('avg_loss')# fig.savefig('loss')
修改train_loss_visualization.py中lines为train_log_loss.txt行数，并根据需要修改要跳过的行数：
skiprows=[x for x in range(lines) if ((x%10!=9) |(x<1000))]
运行train_loss_visualization.py会在脚本所在路径生成avg_loss.png。
可以通过分析损失变化曲线，修改cfg中的学习率变化策略。
除了可视化loss，还可以可视化Avg IOU，Avg Recall等参数
可视化’Region Avg IOU’, ‘Class’, ‘Obj’, ‘No Obj’, ‘Avg Recall’,’count’这些参数可以使用脚本train_iou_visualization.py，使用方式和train_loss_visualization.py相同，train_iou_visualization.py脚本如下（#lines根据train_log_iou.txt的行数修改）：
import pandas as pdimport numpy as npimport matplotlib.pyplot as plt#%matplotlib inlinelines = 122956 #根据train_log_iou.txt的行数修改result = pd.read_csv('train_log_iou.txt', skiprows=[x for x in range(lines) if (x%10==0 or x%10==9) ] ,error_bad_lines=False, names=['Region Avg IOU', 'Class', 'Obj', 'No Obj', 'Avg Recall','count'])result.head()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.head()result.tail()# print(result.head())# print(result.tail())# print(result.dtypes)print(result['Region Avg IOU'])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'])result.dtypesfig = plt.figure()ax = fig.add_subplot(1, 1, 1)ax.plot(result['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_title('The Region Avg IOU curves')ax.set_xlabel('batches')# fig.savefig('Avg IOU')fig.savefig('Region Avg IOU')
运行train_iou_visualization.py会在脚本所在路径生成相应的曲线图。
参考：
/yudiemiaomiao/article/details/72469135
/cgt19910923/article/details/80783614
/cgt19910923/article/details/80783614#commentBox
***1113***
评论区的一位做的表格很棒，很值得借鉴学习：
/qq_33614902/article/details/83418441
一、extract_log.py
#!/usr/bin/python#coding=utf-8#该文件用于提取训练log，去除不可解析的log后使log文件格式化，生成新的log文件供可视化工具绘图import inspectimport osimport randomimport sysdef extract_log(log_file, new_log_file, key_word):with open(log_file, 'r') as f:with open(new_log_file, 'w') as train_log:for line in f:#去除多GPU的同步log；去除除零错误的logif ('Syncing' in line) or ('nan' in line):continueif key_word in line:train_log.write(line)f.close()train_log.close()extract_log('./2048/train_log2.txt', './2048/log_loss2.txt', 'images')extract_log('./2048/train_log2.txt', 'log_iou2.txt', 'IOU')
二、visualization_loss.py
#!/usr/bin/python#coding=utf-8import pandas as pdimport numpy as npimport matplotlib.pyplot as plt#根据自己的log_loss.txt中的行数修改lines, 修改训练时的迭代起始次数(start_ite)和结束次数(end_ite)。lines = 4500start_ite = 6000 #log_loss.txt里面的最小迭代次数end_ite = 15000 #log_loss.txt里面的最大迭代次数step = 10 #跳行数，决定画图的稠密程度igore = 0 #当开始的loss较大时，你需要忽略前igore次迭代，注意这里是迭代次数y_ticks = [0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4]#纵坐标的值，可以自己设置。data_path = '2048/log_loss2.txt' #log_loss的路径。result_path = './2048/avg_loss' #保存结果的路径。####-----------------只需要改上面的，下面的可以不改动names = ['loss', 'avg', 'rate', 'seconds', 'images']result = pd.read_csv(data_path, skiprows=[x for x in range(lines) if (x<lines*1.0/((end_ite - start_ite)*1.0)*igore or x%step!=9)], error_bad_lines=\False, names=names)result.head()for name in names:result[name] = result[name].str.split(' ').str.get(1)result.head()result.tail()for name in names:result[name] = pd.to_numeric(result[name])result.dtypesprint(result['avg'].values)fig = plt.figure()ax = fig.add_subplot(1, 1, 1)###-----------设置横坐标的值。x_num = len(result['avg'].values)tmp = (end_ite-start_ite - igore)/(x_num*1.0)x = []for i in range(x_num):x.append(i*tmp + start_ite + igore)#print(x)print('total = %d\n' %x_num)print('start = %d, end = %d\n' %(x[0], x[-1]))###----------ax.plot(x, result['avg'].values, label='avg_loss')#ax.plot(result['loss'].values, label='loss')plt.yticks(y_ticks)#如果不想自己设置纵坐标，可以注释掉。plt.grid()ax.legend(loc = 'best')ax.set_title('The loss curves')ax.set_xlabel('batches')fig.savefig(result_path)#fig.savefig('loss')
三、visualization_iou.py
#!/usr/bin/python#coding=utf-8import pandas as pdimport numpy as npimport matplotlib.pyplot as plt#根据log_iou修改行数lines = 1736397step = 5000start_ite = 0end_ite = 50200igore = 1000data_path = './my_coco3/log_iou.txt' #log_loss的路径。result_path = './my_coco3/Region Avg IOU' #保存结果的路径。names = ['Region Avg IOU', 'Class', 'Obj', 'No Obj', '.5_Recall', '.7_Recall', 'count']#result = pd.read_csv('log_iou.txt', skiprows=[x for x in range(lines) if (x%10==0 or x%10==9)]\result = pd.read_csv(data_path, skiprows=[x for x in range(lines) if (x<lines*1.0/((end_ite - start_ite)*1.0)*igore or x%step!=0)]\, error_bad_lines=False, names=names)result.head()for name in names:result[name] = result[name].str.split(': ').str.get(1)result.head()result.tail()for name in names:result[name] = pd.to_numeric(result[name])result.dtypes####--------------x_num = len(result['Region Avg IOU'].values)tmp = (end_ite-start_ite - igore)/(x_num*1.0)x = []for i in range(x_num):x.append(i*tmp + start_ite + igore)#print(x)print('total = %d\n' %x_num)print('start = %d, end = %d\n' %(x[0], x[-1]))####-------------fig = plt.figure()ax = fig.add_subplot(1,1,1)ax.plot(x, result['Region Avg IOU'].values, label='Region Avg IOU')#ax.plot(result['Avg Recall'].values, label='Avg Recall')plt.grid()ax.legend(loc='best')ax.set_title('The Region Avg IOU curves')ax.set_xlabel('batches')fig.savefig(result_path)
