由於要寫論文需要畫loss曲線，查詢網上的loss曲線視覺化的方法發現大多數是基於Imagenat的一些方法，在運用到Faster-Rcnn上時沒法用，本人不怎麼會編寫程式碼，所以想到能否用python直接寫一個程式碼，讀取txt然後畫出來，參考大神們的部落格，然後總和總算一下午時間，搞出來了，大牛們不要見笑。

        首先，在訓練Faster-Rcnn時會自己生成log檔案，大概在/py-faster-rcnn/experiments/logs檔案下，把他直接拿出來,放在任意位置即可，因為是txt格式，可以直接用，如果嫌麻煩重新命名1.txt.接下來就是編寫程式了

一下為log基本的格式

I0530 08:54:19.183091 10143 solver.cpp:229] Iteration 22000, loss = 0.173712
I0530 08:54:19.183137 10143 solver.cpp:245]     Train net output #0: rpn_cls_loss = 0.101713 (* 1 = 0.101713 loss)
I0530 08:54:19.183145 10143 solver.cpp:245]     Train net output #1: rpn_loss_bbox = 0.071999 (* 1 = 0.071999 loss)
I0530 08:54:19.183148 10143 sgd_solver.cpp:106] Iteration 22000, lr = 0.001

通過發現，我們只需獲得 Iteration 和loss就行

#!/usr/bin/env python
import os
import sys
import numpy as np
import matplotlib.pyplot as plt
import math
import re
import pylab
from pylab import figure, show, legend
from mpl_toolkits.axes_grid1 import host_subplot

# read the log file
fp = open('2.txt', 'r')

train_iterations = []
train_loss = []
test_iterations = []
#test_accuracy = []

for ln in fp:
  # get train_iterations and train_loss
  if '] Iteration ' in ln and 'loss = ' in ln:
    arr = re.findall(r'ion \b\d+\b,',ln)
    train_iterations.append(int(arr[0].strip(',')[4:]))
    train_loss.append(float(ln.strip().split(' = ')[-1]))
    
fp.close()

host = host_subplot(111)
plt.subplots_adjust(right=0.8) # ajust the right boundary of the plot window
#par1 = host.twinx()
# set labels
host.set_xlabel("iterations")
host.set_ylabel("RPN loss")
#par1.set_ylabel("validation accuracy")

# plot curves
p1, = host.plot(train_iterations, train_loss, label="train RPN loss")
#p2, = par1.plot(test_iterations, test_accuracy, label="validation accuracy")

# set location of the legend, 
# 1->rightup corner, 2->leftup corner, 3->leftdown corner
# 4->rightdown corner, 5->rightmid ...
host.legend(loc=1)

# set label color
host.axis["left"].label.set_color(p1.get_color())
#par1.axis["right"].label.set_color(p2.get_color())
# set the range of x axis of host and y axis of par1
host.set_xlim([-1500,60000])
host.set_ylim([0., 1.6])

plt.draw()
plt.show()