127 lines
4.3 KiB
Python
127 lines
4.3 KiB
Python
# A simple torch style logger
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# (C) Wei YANG 2017
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from __future__ import absolute_import
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import matplotlib.pyplot as plt
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import os
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import sys
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import numpy as np
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__all__ = ['Logger', 'LoggerMonitor', 'savefig']
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def savefig(fname, dpi=None):
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dpi = 150 if dpi == None else dpi
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plt.savefig(fname, dpi=dpi)
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def plot_overlap(logger, names=None):
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names = logger.names if names == None else names
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numbers = logger.numbers
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for _, name in enumerate(names):
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x = np.arange(len(numbers[name]))
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plt.plot(x, np.asarray(numbers[name]))
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return [logger.title + '(' + name + ')' for name in names]
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class Logger(object):
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'''Save training process to log file with simple plot function.'''
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def __init__(self, fpath, title=None, resume=False):
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self.file = None
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self.resume = resume
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self.title = '' if title == None else title
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if fpath is not None:
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if resume:
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self.file = open(fpath, 'r')
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name = self.file.readline()
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self.names = name.rstrip().split('\t')
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self.numbers = {}
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for _, name in enumerate(self.names):
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self.numbers[name] = []
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for numbers in self.file:
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numbers = numbers.rstrip().split('\t')
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for i in range(0, len(numbers)):
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self.numbers[self.names[i]].append(numbers[i])
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self.file.close()
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self.file = open(fpath, 'a')
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else:
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self.file = open(fpath, 'w')
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def set_names(self, names):
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if self.resume:
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pass
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# initialize numbers as empty list
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self.numbers = {}
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self.names = names
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for _, name in enumerate(self.names):
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self.file.write(name)
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self.file.write('\t')
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self.numbers[name] = []
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self.file.write('\n')
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self.file.flush()
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def append(self, numbers):
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assert len(self.names) == len(numbers), 'Numbers do not match names'
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for index, num in enumerate(numbers):
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self.file.write("{0:.6f}".format(num))
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self.file.write('\t')
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self.numbers[self.names[index]].append(num)
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self.file.write('\n')
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self.file.flush()
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def plot(self, names=None):
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names = self.names if names == None else names
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numbers = self.numbers
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for _, name in enumerate(names):
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x = np.arange(len(numbers[name]))
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plt.plot(x, np.asarray(numbers[name]))
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plt.legend([self.title + '(' + name + ')' for name in names])
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plt.grid(True)
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def close(self):
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if self.file is not None:
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self.file.close()
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class LoggerMonitor(object):
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'''Load and visualize multiple logs.'''
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def __init__ (self, paths):
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'''paths is a distionary with {name:filepath} pair'''
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self.loggers = []
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for title, path in paths.items():
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logger = Logger(path, title=title, resume=True)
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self.loggers.append(logger)
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def plot(self, names=None):
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plt.figure()
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plt.subplot(121)
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legend_text = []
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for logger in self.loggers:
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legend_text += plot_overlap(logger, names)
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plt.legend(legend_text, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
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plt.grid(True)
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if __name__ == '__main__':
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# # Example
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# logger = Logger('test.txt')
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# logger.set_names(['Train loss', 'Valid loss','Test loss'])
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# length = 100
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# t = np.arange(length)
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# train_loss = np.exp(-t / 10.0) + np.random.rand(length) * 0.1
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# valid_loss = np.exp(-t / 10.0) + np.random.rand(length) * 0.1
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# test_loss = np.exp(-t / 10.0) + np.random.rand(length) * 0.1
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# for i in range(0, length):
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# logger.append([train_loss[i], valid_loss[i], test_loss[i]])
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# logger.plot()
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# Example: logger monitor
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paths = {
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'resadvnet20':'/home/wyang/code/pytorch-classification/checkpoint/cifar10/resadvnet20/log.txt',
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'resadvnet32':'/home/wyang/code/pytorch-classification/checkpoint/cifar10/resadvnet32/log.txt',
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'resadvnet44':'/home/wyang/code/pytorch-classification/checkpoint/cifar10/resadvnet44/log.txt',
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}
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field = ['Valid Acc.']
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monitor = LoggerMonitor(paths)
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monitor.plot(names=field)
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savefig('test.eps') |