How to use _report_stats method in autotest

Best Python code snippet using autotest_python Github


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1""" Report manager utility """2from __future__ import print_function3import time4from datetime import datetime5import onmt6from onmt.utils.logging import logger7def build_report_manager(opt, gpu_rank):8 if opt.tensorboard and gpu_rank == 0:9 from torch.utils.tensorboard import SummaryWriter10 tensorboard_log_dir = opt.tensorboard_log_dir11 if not opt.train_from:12 tensorboard_log_dir +="/%b-%d_%H-%M-%S")13 writer = SummaryWriter(tensorboard_log_dir, comment="Unmt")14 else:15 writer = None16 report_mgr = ReportMgr(opt.report_every, start_time=-1,17 tensorboard_writer=writer)18 return report_mgr19class ReportMgrBase(object):20 """21 Report Manager Base class22 Inherited classes should override:23 * `_report_training`24 * `_report_step`25 """26 def __init__(self, report_every, start_time=-1.):27 """28 Args:29 report_every(int): Report status every this many sentences30 start_time(float): manually set report start time. Negative values31 means that you will need to set it later or use `start()`32 """33 self.report_every = report_every34 self.start_time = start_time35 def start(self):36 self.start_time = time.time()37 def log(self, *args, **kwargs):38*args, **kwargs)39 def report_training(self, step, num_steps, learning_rate,40 report_stats, multigpu=False):41 """42 This is the user-defined batch-level traing progress43 report function.44 Args:45 step(int): current step count.46 num_steps(int): total number of batches.47 learning_rate(float): current learning rate.48 report_stats(Statistics): old Statistics instance.49 Returns:50 report_stats(Statistics): updated Statistics instance.51 """52 if self.start_time < 0:53 raise ValueError("""ReportMgr needs to be started54 (set 'start_time' or use 'start()'""")55 if step % self.report_every == 0:56 if multigpu:57 report_stats = \58 onmt.utils.Statistics.all_gather_stats(report_stats)59 self._report_training(60 step, num_steps, learning_rate, report_stats)61 return [onmt.utils.Statistics( for x in report_stats]62 else:63 return report_stats64 def _report_training(self, *args, **kwargs):65 """ To be overridden """66 raise NotImplementedError()67 def report_step(self, lr, step, train_stats=None, valid_stats=None):68 """69 Report stats of a step70 Args:71 train_stats(Statistics): training stats72 valid_stats(Statistics): validation stats73 lr(float): current learning rate74 """75 self._report_step(76 lr, step, train_stats=train_stats, valid_stats=valid_stats)77 def _report_step(self, *args, **kwargs):78 raise NotImplementedError()79class ReportMgr(ReportMgrBase):80 def __init__(self, report_every, start_time=-1., tensorboard_writer=None):81 """82 A report manager that writes statistics on standard output as well as83 (optionally) TensorBoard84 Args:85 report_every(int): Report status every this many sentences86 tensorboard_writer(:obj:`tensorboard.SummaryWriter`):87 The TensorBoard Summary writer to use or None88 """89 super(ReportMgr, self).__init__(report_every, start_time)90 self.tensorboard_writer = tensorboard_writer91 def maybe_log_tensorboard(self, stats, prefix, learning_rate, step):92 if self.tensorboard_writer is not None:93 stats.log_tensorboard(94 prefix, self.tensorboard_writer, learning_rate, step)95 def _report_training(self, step, num_steps, learning_rate,96 report_stats):97 """98 See base class method `ReportMgrBase.report_training`.99 """100 output_report_stats = []101 for _report_stats in report_stats:102 if _report_stats.n_words == 0:103 continue104 _report_stats.output(step, num_steps,105 learning_rate, self.start_time)106 # Log the progress using the number of batches on the x-axis.107 self.maybe_log_tensorboard(_report_stats,108 "progress_",109 learning_rate,110 step)111 _report_stats = onmt.utils.Statistics( output_report_stats.append(_report_stats)113 return output_report_stats114 def _report_step(self, lr, step, train_stats=None, valid_stats=None):115 """116 See base class method `ReportMgrBase.report_step`.117 """118 if train_stats:119 for _train_stats in train_stats:120 self.log('Train name: %s' % self.log('Train perplexity: %g' % _train_stats.ppl())122 self.log('Train accuracy: %g' % _train_stats.accuracy())123 self.maybe_log_tensorboard(_train_stats,124 "train_",125 lr,126 step)127 if valid_stats:128 for _valid_stats in valid_stats:129 if _valid_stats.n_words == 0:130 continue131 self.log('Validation name: %s' % self.log('Validation perplexity: %g' % _valid_stats.ppl())133 self.log('Validation accuracy: %g' % _valid_stats.accuracy())134 self.maybe_log_tensorboard(_valid_stats,135 "valid_",136 lr,...

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...42 return r.ok43def run_reporter(stats_key):44 stats = StatHat(stats_key, 'localtunnel.')45"starting metrics reporter with {0}".format(stats_key))46 def _report_stats():47 dump = {}48 for m in dump_metrics():49 dump[m['name']] = m['value']50 for metric in monitored_metrics:51 value = dump.get(metric)52 if value:53 if metric.startswith('collect:'):54 # metrics starting with "collect:" are55 # counters that will be reset once reported56 stats.count(metric.split(':')[-1], value)57 metric_name = metric.split('_count')[0]58 counter(metric_name).clear()59 else:60 stats.value(metric, value)...

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