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