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

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

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