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