mirror of https://github.com/YifanXu74/MQ-Det.git
131 lines
3.7 KiB
Python
131 lines
3.7 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
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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.series = []
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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.series.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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# haotian added tensorboard support
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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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# timestamp = datetime.fromtimestamp(time.time()).strftime('%Y%m%d-%H:%M')
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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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