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import matplotlib
matplotlib.use('Agg')
import time
class Timer():
''' Timer for recording training time distribution. '''
def __init__(self):
self.prev_t = time.time()
self.clear()
def set(self):
self.prev_t = time.time()
def cnt(self, mode):
self.time_table[mode] += time.time()-self.prev_t
self.set()
if mode == 'bw':
self.click += 1
def show(self):
total_time = sum(self.time_table.values())
self.time_table['avg'] = total_time/self.click
self.time_table['rd'] = 100*self.time_table['rd']/total_time
self.time_table['fw'] = 100*self.time_table['fw']/total_time
self.time_table['bw'] = 100*self.time_table['bw']/total_time
msg = '{avg:.3f} sec/step (rd {rd:.1f}% | fw {fw:.1f}% | bw {bw:.1f}%)'.format(
**self.time_table)
self.clear()
return msg
def clear(self):
self.time_table = {'rd': 0, 'fw': 0, 'bw': 0}
self.click = 0
# Reference : https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/e2e_asr.py#L168
def human_format(num):
magnitude = 0
while num >= 1000:
magnitude += 1
num /= 1000.0
# add more suffixes if you need them
return '{:3.1f}{}'.format(num, [' ', 'K', 'M', 'G', 'T', 'P'][magnitude])
# provide easy access of attribute from dict, such abc.key
class AttrDict(dict):
def __init__(self, *args, **kwargs):
super(AttrDict, self).__init__(*args, **kwargs)
self.__dict__ = self
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