YOLOv3之loss和iou可视化（横坐标和纵坐标与迭代次数完美对齐）

建议大家先去看看其他博客的代码，就能体会到它的魅力了。

Table of Contents

一、extract_log.py

二、visualization_loss.py

三、visualization_iou.py

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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