| import numpy as np
|
| import io
|
| import os
|
| import time
|
| from collections import defaultdict, deque
|
| import datetime
|
|
|
| import torch
|
| import torch.distributed as dist
|
|
|
|
|
| class AttrDict(dict):
|
| def __init__(self, *args, **kwargs):
|
| super(AttrDict, self).__init__(*args, **kwargs)
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| self.__dict__ = self
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|
|
|
|
| def setup_for_distributed(is_master):
|
| """
|
| This function disables printing when not in master process
|
| """
|
| import builtins as __builtin__
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| builtin_print = __builtin__.print
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|
|
| def print(*args, **kwargs):
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| force = kwargs.pop('force', False)
|
| if is_master or force:
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| builtin_print(*args, **kwargs)
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|
|
| __builtin__.print = print
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|
|
|
|
| def is_dist_avail_and_initialized():
|
| if not dist.is_available():
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| return False
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| if not dist.is_initialized():
|
| return False
|
| return True
|
|
|
|
|
| def get_world_size():
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| if not is_dist_avail_and_initialized():
|
| return 1
|
| return dist.get_world_size()
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|
|
|
|
| def get_rank():
|
| if not is_dist_avail_and_initialized():
|
| return 0
|
| return dist.get_rank()
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|
|
|
|
| def is_main_process():
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| return get_rank() == 0
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|
|
|
|
| def save_on_master(*args, **kwargs):
|
| if is_main_process():
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| torch.save(*args, **kwargs)
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|
|
|
|
| def init_distributed_mode(args):
|
| if 'RANK' in os.environ and 'WORLD_SIZE' in os.environ:
|
| args.rank = int(os.environ["RANK"])
|
| args.world_size = int(os.environ['WORLD_SIZE'])
|
| args.gpu = int(os.environ['LOCAL_RANK'])
|
| elif 'SLURM_PROCID' in os.environ:
|
| args.rank = int(os.environ['SLURM_PROCID'])
|
| args.gpu = args.rank % torch.cuda.device_count()
|
| else:
|
| print('Not using distributed mode')
|
| args.distributed = False
|
| return
|
|
|
| args.distributed = True
|
|
|
| torch.cuda.set_device(args.gpu)
|
| args.dist_backend = 'nccl'
|
| print('| distributed init (rank {}): {}'.format(
|
| args.rank, args.dist_url), flush=True)
|
| torch.distributed.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
|
| world_size=args.world_size, rank=args.rank)
|
| torch.distributed.barrier()
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| setup_for_distributed(args.rank == 0)
|
|
|
|
|
| class SmoothedValue(object):
|
| """Track a series of values and provide access to smoothed values over a
|
| window or the global series average.
|
| """
|
|
|
| def __init__(self, window_size=20, fmt=None):
|
| if fmt is None:
|
| fmt = "{median:.4f} ({global_avg:.4f})"
|
| self.deque = deque(maxlen=window_size)
|
| self.total = 0.0
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| self.count = 0
|
| self.fmt = fmt
|
|
|
| def update(self, value, n=1):
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| self.deque.append(value)
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| self.count += n
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| self.total += value * n
|
|
|
| def synchronize_between_processes(self):
|
| """
|
| Warning: does not synchronize the deque!
|
| """
|
| if not is_dist_avail_and_initialized():
|
| return
|
| t = torch.tensor([self.count, self.total],
|
| dtype=torch.float64, device='cuda')
|
| dist.barrier()
|
| dist.all_reduce(t)
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| t = t.tolist()
|
| self.count = int(t[0])
|
| self.total = t[1]
|
|
|
| @property
|
| def median(self):
|
| d = torch.tensor(list(self.deque))
|
| return d.median().item()
|
|
|
| @property
|
| def avg(self):
|
| d = torch.tensor(list(self.deque), dtype=torch.float32)
|
| return d.mean().item()
|
|
|
| @property
|
| def global_avg(self):
|
| return self.total / self.count
|
|
|
| @property
|
| def max(self):
|
| return max(self.deque)
|
|
|
| @property
|
| def value(self):
|
| return self.deque[-1]
|
|
|
| def __str__(self):
|
| return self.fmt.format(
|
| median=self.median,
|
| avg=self.avg,
|
| global_avg=self.global_avg,
|
| max=self.max,
|
| value=self.value)
|
|
|
|
|
| class MetricLogger(object):
|
| def __init__(self, delimiter="\t"):
|
| self.meters = defaultdict(SmoothedValue)
|
| self.delimiter = delimiter
|
|
|
| def update(self, **kwargs):
|
| for k, v in kwargs.items():
|
| if isinstance(v, torch.Tensor):
|
| v = v.item()
|
| assert isinstance(v, (float, int))
|
| self.meters[k].update(v)
|
|
|
| def __getattr__(self, attr):
|
| if attr in self.meters:
|
| return self.meters[attr]
|
| if attr in self.__dict__:
|
| return self.__dict__[attr]
|
| raise AttributeError("'{}' object has no attribute '{}'".format(
|
| type(self).__name__, attr))
|
|
|
| def __str__(self):
|
| loss_str = []
|
| for name, meter in self.meters.items():
|
| loss_str.append(
|
| "{}: {}".format(name, str(meter))
|
| )
|
| return self.delimiter.join(loss_str)
|
|
|
| def global_avg(self):
|
| loss_str = []
|
| for name, meter in self.meters.items():
|
| loss_str.append(
|
| "{}: {:.4f}".format(name, meter.global_avg)
|
| )
|
| return self.delimiter.join(loss_str)
|
|
|
| def synchronize_between_processes(self):
|
| for meter in self.meters.values():
|
| meter.synchronize_between_processes()
|
|
|
| def add_meter(self, name, meter):
|
| self.meters[name] = meter
|
|
|
| def log_every(self, iterable, print_freq, header=None, Eiters=0):
|
| i = 0
|
| if not header:
|
| header = ''
|
| start_time = time.time()
|
| end = time.time()
|
| iter_time = SmoothedValue(fmt='{avg:.4f}')
|
| data_time = SmoothedValue(fmt='{avg:.4f}')
|
| space_fmt = ':' + str(len(str(len(iterable)))) + 'd'
|
| log_msg = [
|
| header,
|
| '[{0' + space_fmt + '}/{1}]',
|
| 'eta: {eta}',
|
| '{meters}',
|
| 'time: {time}',
|
| 'data: {data}'
|
| ]
|
| if torch.cuda.is_available():
|
| log_msg.append('max mem: {memory:.0f}')
|
| log_msg = self.delimiter.join(log_msg)
|
| MB = 1024.0 * 1024.0
|
| for obj in iterable:
|
| data_time.update(time.time() - end)
|
| yield obj
|
| iter_time.update(time.time() - end)
|
| if i % print_freq == 0 or i == len(iterable) - 1:
|
| eta_seconds = iter_time.global_avg * (len(iterable) - i)
|
| eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
|
| if torch.cuda.is_available():
|
| print(log_msg.format(
|
| i, len(iterable), eta=eta_string,
|
| meters=str(self),
|
| time=str(iter_time), data=str(data_time),
|
| memory=torch.cuda.max_memory_allocated() / MB))
|
| else:
|
| print(log_msg.format(
|
| i, len(iterable), eta=eta_string,
|
| meters=str(self),
|
| time=str(iter_time), data=str(data_time)))
|
| i += 1
|
| end = time.time()
|
| total_time = time.time() - start_time
|
| total_time_str = str(datetime.timedelta(seconds=int(total_time)))
|
| print('{} Total time: {} ({:.4f} s / it)'.format(
|
| header, total_time_str, total_time / len(iterable)))
|
|
|
