File size: 4,398 Bytes
5f58fbe | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 | # A simple torch style logger
# (C) Wei YANG 2017
from __future__ import absolute_import
import matplotlib.pyplot as plt
import os
import sys
import numpy as np
__all__ = ['Logger', 'LoggerMonitor', 'savefig']
def savefig(fname, dpi=None):
dpi = 150 if dpi == None else dpi
plt.savefig(fname, dpi=dpi)
def plot_overlap(logger, names=None):
names = logger.names if names == None else names
numbers = logger.numbers
for _, name in enumerate(names):
x = np.arange(len(numbers[name]))
plt.plot(x, np.asarray(numbers[name]))
return [logger.title + '(' + name + ')' for name in names]
class Logger(object):
'''Save training process to log file with simple plot function.'''
def __init__(self, fpath, title=None, resume=False):
self.file = None
self.resume = resume
self.title = '' if title == None else title
if fpath is not None:
if resume:
self.file = open(fpath, 'r')
name = self.file.readline()
self.names = name.rstrip().split('\t')
self.numbers = {}
for _, name in enumerate(self.names):
self.numbers[name] = []
for numbers in self.file:
numbers = numbers.rstrip().split('\t')
for i in range(0, len(numbers)):
self.numbers[self.names[i]].append(numbers[i])
self.file.close()
self.file = open(fpath, 'a')
else:
self.file = open(fpath, 'w')
def set_names(self, names):
if self.resume:
pass
# initialize numbers as empty list
self.numbers = {}
self.names = names
for _, name in enumerate(self.names):
self.file.write(name)
self.file.write('\t')
self.numbers[name] = []
self.file.write('\n')
self.file.flush()
def append(self, numbers):
assert len(self.names) == len(numbers), 'Numbers do not match names'
for index, num in enumerate(numbers):
self.file.write("{0:.6f}".format(num))
self.file.write('\t')
self.numbers[self.names[index]].append(num)
self.file.write('\n')
self.file.flush()
def plot(self, names=None):
names = self.names if names == None else names
numbers = self.numbers
for _, name in enumerate(names):
x = np.arange(len(numbers[name]))
plt.plot(x, np.asarray(numbers[name]))
plt.legend([self.title + '(' + name + ')' for name in names])
plt.grid(True)
def close(self):
if self.file is not None:
self.file.close()
class LoggerMonitor(object):
'''Load and visualize multiple logs.'''
def __init__ (self, paths):
'''paths is a distionary with {name:filepath} pair'''
self.loggers = []
for title, path in paths.items():
logger = Logger(path, title=title, resume=True)
self.loggers.append(logger)
def plot(self, names=None):
plt.figure()
plt.subplot(121)
legend_text = []
for logger in self.loggers:
legend_text += plot_overlap(logger, names)
plt.legend(legend_text, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
plt.grid(True)
if __name__ == '__main__':
# # Example
# logger = Logger('test.txt')
# logger.set_names(['Train loss', 'Valid loss','Test loss'])
# length = 100
# t = np.arange(length)
# train_loss = np.exp(-t / 10.0) + np.random.rand(length) * 0.1
# valid_loss = np.exp(-t / 10.0) + np.random.rand(length) * 0.1
# test_loss = np.exp(-t / 10.0) + np.random.rand(length) * 0.1
# for i in range(0, length):
# logger.append([train_loss[i], valid_loss[i], test_loss[i]])
# logger.plot()
# Example: logger monitor
paths = {
'resadvnet20':'/home/wyang/code/pytorch-classification/checkpoint/cifar10/resadvnet20/log.txt',
'resadvnet32':'/home/wyang/code/pytorch-classification/checkpoint/cifar10/resadvnet32/log.txt',
'resadvnet44':'/home/wyang/code/pytorch-classification/checkpoint/cifar10/resadvnet44/log.txt',
}
field = ['Valid Acc.']
monitor = LoggerMonitor(paths)
monitor.plot(names=field)
savefig('test.eps') |