（转）YOLO-V3可视化训练过程中的参数，绘制loss、IOU、avg Recall等的曲线图

https://blog.csdn.net/qq_34806812/article/details/81459982

看了好几个博客，发现了些问题，有些博客是有bug的，此博客亲测无误。

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 inspect
import os
import random
import sys
def 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log
if 'Syncing' in line:
continue
# 去除除零错误的log
if 'nan' in line:
continue
if 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 pd
import numpy as np
import matplotlib.pyplot as plt
#%matplotlib inline

lines = 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.dtypes


fig = 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 pd
import numpy as np
import matplotlib.pyplot as plt
#%matplotlib inline

lines = 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.dtypes

fig = 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：

https://blog.csdn.net/yudiemiaomiao/article/details/72469135

https://blog.csdn.net/cgt19910923/article/details/80783614

https://blog.csdn.net/cgt19910923/article/details/80783614#commentBox

***20181113***

评论区的一位做的表格很棒，很值得借鉴学习：

https://blog.csdn.net/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 inspect
import os
import random
import sys
def 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log
if ( 'Syncing' in line) or ( 'nan' in line):
continue
if 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-8

import pandas as pd
import numpy as np
import 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 = 4500
start_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.dtypes
print(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-8

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

#根据log_iou修改行数
lines = 1736397
step = 5000
start_ite = 0
end_ite = 50200
igore = 1000
data_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)