Track a series of values and provide access to smoothed values over a window or the global series average.
| 18 | |
| 19 | |
| 20 | class SmoothedValue(object): |
| 21 | """Track a series of values and provide access to smoothed values over a |
| 22 | window or the global series average. |
| 23 | """ |
| 24 | |
| 25 | def __init__(self, window_size=20, fmt=None): |
| 26 | if fmt is None: |
| 27 | fmt = "{median:.4f} ({global_avg:.4f})" |
| 28 | self.deque = deque(maxlen=window_size) |
| 29 | self.total = 0.0 |
| 30 | self.count = 0 |
| 31 | self.fmt = fmt |
| 32 | |
| 33 | def update(self, value, n=1): |
| 34 | self.deque.append(value) |
| 35 | self.count += n |
| 36 | self.total += value * n |
| 37 | |
| 38 | def synchronize_between_processes(self): |
| 39 | """ |
| 40 | Warning: does not synchronize the deque! |
| 41 | """ |
| 42 | if not is_dist_avail_and_initialized(): |
| 43 | return |
| 44 | t = torch.tensor([self.count, self.total], dtype=torch.float64, device='cuda') |
| 45 | dist.barrier() |
| 46 | dist.all_reduce(t) |
| 47 | t = t.tolist() |
| 48 | self.count = int(t[0]) |
| 49 | self.total = t[1] |
| 50 | |
| 51 | @property |
| 52 | def median(self): |
| 53 | d = torch.tensor(list(self.deque)) |
| 54 | return d.median().item() |
| 55 | |
| 56 | @property |
| 57 | def avg(self): |
| 58 | d = torch.tensor(list(self.deque), dtype=torch.float32) |
| 59 | return d.mean().item() |
| 60 | |
| 61 | @property |
| 62 | def global_avg(self): |
| 63 | return self.total / self.count |
| 64 | |
| 65 | @property |
| 66 | def max(self): |
| 67 | return max(self.deque) |
| 68 | |
| 69 | @property |
| 70 | def value(self): |
| 71 | return self.deque[-1] |
| 72 | |
| 73 | def __str__(self): |
| 74 | return self.fmt.format( |
| 75 | median=self.median, |
| 76 | avg=self.avg, |
| 77 | global_avg=self.global_avg, |