# In the name of GOD the most compassionate the most merciful

# Last Updated : 4/9/2017 , updated the regex for the latest caffe (rc5) logs

# Just added the search for current directory so that users dont have to use command prompts anymore!

# and also shows the top 4 accuracies achieved so far, and displaying the highest in the plot title

# Coded By: Seyyed Hossein Hasan Pour (Coderx7@gmail.com)

# -------How to Use ---------------

# 1.Just place your caffe's traning/test log file (with .log extension) next to this script

# and then run the script.If you have multiple logs placed next to the script, it will plot all of them

# you may also copy this script to your working directory, where you generate/keep your train/test logs

# and easily execute the script and see the curve plotted.

# this script is standalone.

# 2. you can use command line arguments as well, just feed the script with different log files separated by space

# and you are good to go.

#----------------------------------

import numpy as np

import re

import click

import glob, os

from matplotlib import pylab as plt

import operator

import ntpath

@click.command()

@click.argument('files', nargs=-1, type=click.Path(exists=True))

def main(files):

plt.style.use('ggplot')

fig, ax1 = plt.subplots()

ax2 = ax1.twinx()

ax1.set_xlabel('iteration')

ax1.set_ylabel('loss')

ax2.set_ylabel('accuracy %')

if not files:

print 'no args found'

print '\n\rloading all files with .log extension from current directory'

os.chdir(".")

files = glob.glob("*.log")

for i, log_file in enumerate(files):

loss_iterations, losses, accuracy_iterations, accuracies, accuracies_iteration_checkpoints_ind, fileName = parse_log(log_file)

disp_results(fig, ax1, ax2, loss_iterations, losses, accuracy_iterations, accuracies, accuracies_iteration_checkpoints_ind, fileName, color_ind=i)

loss_iterations, losses, accuracy_iterations, accuracies, accuracies_iteration_checkpoints_ind, fileName = parse_training_log(log_file)

disp_results(fig, ax1, ax2, loss_iterations, losses, accuracy_iterations, accuracies, accuracies_iteration_checkpoints_ind, fileName, color_ind=i+1)

plt.show()

def parse_training_log(log_file):

with open(log_file, 'r') as log_file2:

log = log_file2.read()

losses = []

loss_iterations = []

loss_accuracy_pattern = r"Iteration (?P<iter_num>\d+) (.?)*, loss = ((\d*.\d*)+)\n.*( *)Train net output #0: accuracy_training = ((\d*.\d*)+)"

accuracies = []

accuracy_iterations = []

accuracies_iteration_checkpoints_ind = []

fileName= 'train_'+os.path.basename(log_file)

if re.search(loss_accuracy_pattern,log) != None:

for r in re.findall(loss_accuracy_pattern, log):

#print '\n'

#print (r)

iteration = int(r[0])

loss_iterations.append(iteration)

losses.append(float(r[2]))

accuracy = float(r[5]) * 100

if iteration % 10000 == 0 and iteration > 0:

accuracies_iteration_checkpoints_ind.append(len(accuracy_iterations))

accuracy_iterations.append(iteration)

accuracies.append(accuracy)

loss_iterations = np.array(loss_iterations)

losses = np.array(losses)

accuracy_iterations = np.array(accuracy_iterations)

accuracies = np.array(accuracies)

return loss_iterations, losses, accuracy_iterations, accuracies, accuracies_iteration_checkpoints_ind, fileName

def parse_log(log_file):

with open(log_file, 'r') as log_file2:

log = log_file2.read()

losses = []

loss_iterations = []

loss_accuracy_pattern = r"Iteration (?P<iter_num>\d+), Testing net \(#0\)((.*?)(\n)*)* accuracy = ((\d*.\d*)+)((.*?)(\n)*)*(.?)*loss = ((\d*.\d*)+) \("

accuracies = []

accuracy_iterations = []

accuracies_iteration_checkpoints_ind = []

fileName= 'test_'+os.path.basename(log_file)

if re.search(loss_accuracy_pattern,log) != None:

for r in re.findall(loss_accuracy_pattern, log):

iteration = int(r[0])

loss_iterations.append(iteration)

losses.append(float(r[10]))

#print '\n'

#print (r)

accuracy = float(r[4]) * 100

if iteration % 10000 == 0 and iteration > 0:

accuracies_iteration_checkpoints_ind.append(len(accuracy_iterations))

accuracy_iterations.append(iteration)

accuracies.append(accuracy)

loss_iterations = np.array(loss_iterations)

losses = np.array(losses)

accuracy_iterations = np.array(accuracy_iterations)

accuracies = np.array(accuracies)

return loss_iterations, losses, accuracy_iterations, accuracies, accuracies_iteration_checkpoints_ind, fileName

def disp_results(fig, ax1, ax2, loss_iterations, losses, accuracy_iterations, accuracies, accuracies_iteration_checkpoints_ind, fileName, color_ind=0):

modula = len(plt.rcParams['axes.color_cycle'])

acrIterations =[]

top_acrs={}

if accuracies.size:

if accuracies.size>4:

top_n = 4

else:

top_n = accuracies.size -1

temp = np.argpartition(-accuracies, top_n)

result_indexces = temp[:top_n]

temp = np.partition(-accuracies, top_n)

result = -temp[:top_n]

for acr in result_indexces:

acrIterations.append(accuracy_iterations[acr])

top_acrs[str(accuracy_iterations[acr])]=str(accuracies[acr])

sorted_top4 = sorted(top_acrs.items(), key=operator.itemgetter(1))

maxAcc = np.amax(accuracies, axis=0)

iterIndx = np.argmax(accuracies)

maxAccIter = accuracy_iterations[iterIndx]

maxIter = accuracy_iterations[-1]

consoleInfo = format('\n[%s]:maximum accuracy [from 0 to %s ] = [Iteration %s]: %s ' %(fileName,maxIter,maxAccIter ,maxAcc))

plotTitle = format('max accuracy(%s) [Iteration %s]: %s ' % (fileName,maxAccIter, maxAcc))

print (consoleInfo)

#print (str(result))

#print(acrIterations)

# print 'Top 4 accuracies:'

print ('Top 4 accuracies:'+str(sorted_top4))

plt.title(plotTitle)

ax1.plot(loss_iterations, losses, color=plt.rcParams['axes.color_cycle'][(color_ind * 2 + 0) % modula])

ax2.plot(accuracy_iterations, accuracies, plt.rcParams['axes.color_cycle'][(color_ind * 2 + 1) % modula], label=str(fileName))

ax2.plot(accuracy_iterations[accuracies_iteration_checkpoints_ind], accuracies[accuracies_iteration_checkpoints_ind], 'o', color=plt.rcParams['axes.color_cycle'][(color_ind * 2 + 1) % modula])

plt.legend(loc='lower right')

if __name__ == '__main__':

main()