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| import builtins
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| import datetime
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| import os
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| import time
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| from collections import defaultdict, deque
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| from pathlib import Path
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|
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| import torch
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| import torch.distributed as dist
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| from PIL import ImageFile
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| ImageFile.LOAD_TRUNCATED_IMAGES = True
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| dist_on_itp = False
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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, fmt=None):
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| if fmt is None:
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| fmt = "{median:.4f} ({global_avg:.4f})"
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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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| self.fmt = fmt
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|
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| 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
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|
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| def synchronize_between_processes(self):
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| """
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| Warning: does not synchronize the deque!
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| """
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| if not is_dist_avail_and_initialized():
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| return
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| t = torch.tensor([self.count, self.total], dtype=torch.float64, device='cuda')
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| dist.barrier()
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| dist.all_reduce(t)
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| t = t.tolist()
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| self.count = int(t[0])
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| self.total = t[1]
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|
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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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|
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| @property
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| def avg(self):
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| d = torch.tensor(list(self.deque), dtype=torch.float32)
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| return d.mean().item()
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|
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| @property
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| def global_avg(self):
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| if self.count == 0:
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| return 0
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| else:
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| return self.total / self.count
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|
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| @property
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| def max(self):
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| return max(self.deque)
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|
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| @property
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| def value(self):
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| return self.deque[-1]
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|
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| def __str__(self):
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| return self.fmt.format(
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| median=self.median,
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| avg=self.avg,
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| global_avg=self.global_avg,
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| max=self.max,
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| value=self.value)
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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 v is None:
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| continue
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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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|
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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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| "{}: {}".format(name, str(meter))
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| )
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| return self.delimiter.join(loss_str)
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|
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| def synchronize_between_processes(self):
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| for meter in self.meters.values():
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| meter.synchronize_between_processes()
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| def add_meter(self, name, meter):
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| self.meters[name] = meter
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|
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| def log_every(self, iterable, print_freq, header=None):
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| i = 0
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| if not header:
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| header = ''
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| start_time = time.time()
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| end = time.time()
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| iter_time = SmoothedValue(fmt='{avg:.4f}')
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| data_time = SmoothedValue(fmt='{avg:.4f}')
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| space_fmt = ':' + str(len(str(len(iterable)))) + 'd'
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| log_msg = [
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| header,
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| '[{0' + space_fmt + '}/{1}]',
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| 'eta: {eta}',
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| '{meters}',
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| 'time: {time}',
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| 'data: {data}'
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| ]
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| if torch.cuda.is_available():
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| log_msg.append('max mem: {memory:.0f}')
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| log_msg = self.delimiter.join(log_msg)
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| MB = 1024.0 * 1024.0
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| for obj in iterable:
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| data_time.update(time.time() - end)
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| yield obj
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| iter_time.update(time.time() - end)
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| if i % print_freq == 0 or i == len(iterable) - 1:
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| eta_seconds = iter_time.global_avg * (len(iterable) - i)
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| eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
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| if torch.cuda.is_available():
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| print(log_msg.format(
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| i, len(iterable), eta=eta_string,
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| meters=str(self),
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| time=str(iter_time), data=str(data_time),
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| memory=torch.cuda.max_memory_allocated() / MB))
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| else:
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| print(log_msg.format(
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| i, len(iterable), eta=eta_string,
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| meters=str(self),
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| time=str(iter_time), data=str(data_time)))
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| i += 1
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| end = time.time()
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| total_time = time.time() - start_time
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| total_time_str = str(datetime.timedelta(seconds=int(total_time)))
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| if len(iterable) == 0:
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| print('Total time: {} ({:.4f} s / it)'.format(total_time_str, 0))
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| else:
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| print('{} Total time: {} ({:.4f} s / it)'.format(
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| header, total_time_str, total_time / len(iterable)))
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| def setup_for_distributed(is_master):
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| """
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| This function disables printing when not in master process
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| """
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| builtin_print = builtins.print
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| def print(*args, **kwargs):
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| force = kwargs.pop('force', False)
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| force = force or (get_world_size() > 8)
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| if is_master or force:
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| now = datetime.datetime.now().time()
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| builtin_print('[{}] '.format(now), end='')
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| builtin_print(*args, **kwargs)
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| builtins.print = print
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| def is_dist_avail_and_initialized():
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| if not dist.is_available():
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| return False
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| if not dist.is_initialized():
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| return False
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| return True
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| def get_world_size():
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| if not is_dist_avail_and_initialized():
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| return 1
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| return dist.get_world_size()
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|
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| def get_rank():
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| if not is_dist_avail_and_initialized():
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| return 0
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| return dist.get_rank()
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|
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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):
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| if is_main_process():
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| torch.save(*args, **kwargs)
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| def init_distributed_mode(args):
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| if dist_on_itp:
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| args.rank = int(os.environ['OMPI_COMM_WORLD_RANK'])
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| args.world_size = int(os.environ['OMPI_COMM_WORLD_SIZE'])
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| args.gpu = int(os.environ['OMPI_COMM_WORLD_LOCAL_RANK'])
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| args.dist_url = "tcp://%s:%s" % (os.environ['MASTER_ADDR'], os.environ['MASTER_PORT'])
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| os.environ['LOCAL_RANK'] = str(args.gpu)
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| os.environ['RANK'] = str(args.rank)
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| os.environ['WORLD_SIZE'] = str(args.world_size)
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|
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| elif 'RANK' in os.environ and 'WORLD_SIZE' in os.environ:
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| args.rank = int(os.environ["RANK"])
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| args.world_size = int(os.environ['WORLD_SIZE'])
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| args.gpu = int(os.environ['LOCAL_RANK'])
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| elif 'SLURM_PROCID' in os.environ:
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| args.rank = int(os.environ['SLURM_PROCID'])
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| args.gpu = args.rank % torch.cuda.device_count()
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| else:
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| print('Not using distributed mode')
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| setup_for_distributed(is_master=True)
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| args.distributed = False
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| return
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|
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| args.distributed = True
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|
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| torch.cuda.set_device(args.gpu)
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| args.dist_url = 'env://'
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| args.dist_backend = 'nccl'
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| print('| distributed init (rank {}): {}, gpu {}'.format(
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| args.rank, args.dist_url, args.gpu), flush=True)
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| torch.distributed.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
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| world_size=args.world_size, rank=args.rank)
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| torch.distributed.barrier()
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| setup_for_distributed(args.rank == 0)
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|
|
|
|
| class NativeScalerWithGradNormCount:
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| state_dict_key = "amp_scaler"
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|
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| def __init__(self):
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| self._scaler = torch.cuda.amp.GradScaler()
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|
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| def __call__(self, loss, optimizer, clip_grad=None, parameters=None, create_graph=False, update_grad=True):
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| self._scaler.scale(loss).backward(create_graph=create_graph)
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| if update_grad:
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| if clip_grad is not None:
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| assert parameters is not None
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| self._scaler.unscale_(optimizer)
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| norm = torch.nn.utils.clip_grad_norm_(parameters, clip_grad)
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| else:
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| self._scaler.unscale_(optimizer)
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| norm = get_grad_norm_(parameters)
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| self._scaler.step(optimizer)
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| self._scaler.update()
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| else:
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| norm = None
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| return norm
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|
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| def state_dict(self):
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| return self._scaler.state_dict()
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|
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| def load_state_dict(self, state_dict):
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| self._scaler.load_state_dict(state_dict)
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|
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|
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| def get_grad_norm_(parameters, norm_type: float = 2.0) -> torch.Tensor:
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| if isinstance(parameters, torch.Tensor):
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| parameters = [parameters]
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| parameters = [p for p in parameters if p.grad is not None]
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| norm_type = float(norm_type)
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| if len(parameters) == 0:
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| return torch.tensor(0.)
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| device = parameters[0].grad.device
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| if norm_type == float('inf'):
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| total_norm = max(p.grad.detach().abs().max().to(device) for p in parameters)
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| else:
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| total_norm = torch.norm(torch.stack([torch.norm(p.grad.detach(), norm_type).to(device) for p in parameters]), norm_type)
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| return total_norm
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|
|
|
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| def save_model(args, epoch, model, model_without_ddp, optimizer, loss_scaler):
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| output_dir = Path(args.output_dir)
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| epoch_name = str(epoch)
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| if loss_scaler is not None:
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| checkpoint_paths = [output_dir / ('checkpoint-%s.pth' % epoch_name)]
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| for checkpoint_path in checkpoint_paths:
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| to_save = {
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| 'model': model_without_ddp.state_dict(),
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| 'optimizer': optimizer.state_dict(),
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| 'epoch': epoch,
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| 'scaler': loss_scaler.state_dict(),
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| 'args': args,
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| }
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|
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| save_on_master(to_save, checkpoint_path)
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| else:
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| client_state = {'epoch': epoch}
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| model.save_checkpoint(save_dir=args.output_dir, tag="checkpoint-%s" % epoch_name, client_state=client_state)
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|
|
|
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| def load_model(args, model_without_ddp, optimizer, loss_scaler):
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| if args.resume:
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| if args.resume.startswith('https'):
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| checkpoint = torch.hub.load_state_dict_from_url(
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| args.resume, map_location='cpu', check_hash=True)
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| else:
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| checkpoint = torch.load(args.resume, map_location='cpu')
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| model_without_ddp.load_state_dict(checkpoint['model'])
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| print("Resume checkpoint %s" % args.resume)
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| if 'optimizer' in checkpoint and 'epoch' in checkpoint and not (hasattr(args, 'eval') and args.eval):
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| optimizer.load_state_dict(checkpoint['optimizer'])
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| args.start_epoch = checkpoint['epoch'] + 1
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| if 'scaler' in checkpoint:
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| loss_scaler.load_state_dict(checkpoint['scaler'])
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| print("With optim & sched!")
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|
|
|
|
| def all_reduce_mean(x):
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| world_size = get_world_size()
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| if world_size > 1:
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| x_reduce = torch.tensor(x).cuda()
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| dist.all_reduce(x_reduce)
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| x_reduce /= world_size
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| return x_reduce.item()
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| else:
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| return x |
