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from collections import defaultdict |
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from collections import deque |
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import torch |
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import time |
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from datetime import datetime |
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from .comm import is_main_process |
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class SmoothedValue(object): |
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"""Track a series of values and provide access to smoothed values over a |
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window or the global series average. |
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""" |
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def __init__(self, window_size=20): |
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self.deque = deque(maxlen=window_size) |
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self.total = 0.0 |
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self.count = 0 |
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def update(self, value): |
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self.deque.append(value) |
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self.count += 1 |
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if value != value: |
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value = 0 |
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self.total += value |
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@property |
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def median(self): |
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d = torch.tensor(list(self.deque)) |
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return d.median().item() |
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@property |
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def avg(self): |
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d = torch.tensor(list(self.deque)) |
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return d.mean().item() |
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@property |
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def global_avg(self): |
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return self.total / self.count |
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class AverageMeter(object): |
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"""Computes and stores the average and current value""" |
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def __init__(self): |
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self.reset() |
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def reset(self): |
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self.val = 0 |
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self.avg = 0 |
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self.sum = 0 |
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self.count = 0 |
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def update(self, val, n=1): |
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self.val = val |
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self.sum += val * n |
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self.count += n |
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self.avg = self.sum / self.count |
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class MetricLogger(object): |
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def __init__(self, delimiter="\t"): |
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self.meters = defaultdict(SmoothedValue) |
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self.delimiter = delimiter |
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def update(self, **kwargs): |
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for k, v in kwargs.items(): |
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if isinstance(v, torch.Tensor): |
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v = v.item() |
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assert isinstance(v, (float, int)) |
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self.meters[k].update(v) |
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def __getattr__(self, attr): |
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if attr in self.meters: |
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return self.meters[attr] |
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if attr in self.__dict__: |
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return self.__dict__[attr] |
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raise AttributeError("'{}' object has no attribute '{}'".format( |
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type(self).__name__, attr)) |
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def __str__(self): |
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loss_str = [] |
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for name, meter in self.meters.items(): |
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loss_str.append( |
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"{}: {:.4f} ({:.4f})".format(name, meter.median, meter.global_avg) |
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) |
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return self.delimiter.join(loss_str) |
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class TensorboardLogger(MetricLogger): |
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def __init__(self, |
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log_dir, |
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start_iter=0, |
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delimiter='\t' |
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): |
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super(TensorboardLogger, self).__init__(delimiter) |
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self.iteration = start_iter |
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self.writer = self._get_tensorboard_writer(log_dir) |
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@staticmethod |
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def _get_tensorboard_writer(log_dir): |
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try: |
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from tensorboardX import SummaryWriter |
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except ImportError: |
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raise ImportError( |
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'To use tensorboard please install tensorboardX ' |
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'[ pip install tensorflow tensorboardX ].' |
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) |
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if is_main_process(): |
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tb_logger = SummaryWriter('{}'.format(log_dir)) |
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return tb_logger |
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else: |
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return None |
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def update(self, **kwargs): |
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super(TensorboardLogger, self).update(**kwargs) |
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if self.writer: |
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for k, v in kwargs.items(): |
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if isinstance(v, torch.Tensor): |
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v = v.item() |
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assert isinstance(v, (float, int)) |
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self.writer.add_scalar(k, v, self.iteration) |
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self.iteration += 1 |
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