1.输出重定向
做训练的时候输出重定向得到训练日志文件
./darknet detector train cfg/voc.data cfg/yolov3-voc.cfg darknet53.conv.74 -gpus 0,1 2>1 | tee train_yolov3.log
2.解析日志文件
extract_log.py
# 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')
解析loss行和iou行得到两个txt文件
3.loss曲线可视化
train_loss_visualization.py
根据train_log_loss.txt行数修改lines行
import pandas as pd import numpy as np import matplotlib.pyplot as plt #%matplotlib inline #lines =9873 lines=25100 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())import pandas as pd import numpy as np import matplotlib.pyplot as plt #%matplotlib inline lines =9873 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') ax.set_title('The loss curves') ax.set_xlabel('batches') fig.savefig('avg_loss') #fig.savefig('loss') # 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') ax.set_title('The loss curves') ax.set_xlabel('batches') fig.savefig('avg_loss') #fig.savefig('loss')
4.iou曲线可视化
train_iou_visualization.py
根据train_log_iou.txt行数修改一下lines行,这个数值比较大。
import pandas as pd import numpy as np import matplotlib.pyplot as plt #%matplotlib inline lines =1994726 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')