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’)
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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’)
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。
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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）：
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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%100 or x%109) ] ,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’)
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#!/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_num1.0)
x = []
for i in range(x_num):
x.append(itmp + 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)
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imprt aplotlib.pyplot as plt
#根据lo
igore = 10
data_path = './
