logger.py

# A simple torch style logger
# (C) Wei YANG 2017
from __future__ import absolute_import


# import matplotlib.pyplot as plt


__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')