initial
This commit is contained in:
295
utils.py
Normal file
295
utils.py
Normal file
@@ -0,0 +1,295 @@
|
||||
from collections import defaultdict, deque
|
||||
import datetime
|
||||
import errno
|
||||
import os
|
||||
import time
|
||||
|
||||
import torch
|
||||
import torch.distributed as dist
|
||||
|
||||
|
||||
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
|
||||
self.count = 0
|
||||
self.fmt = fmt
|
||||
|
||||
def update(self, value, n=1):
|
||||
self.deque.append(value)
|
||||
self.count += n
|
||||
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)
|
||||
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)
|
||||
|
||||
|
||||
def all_gather(data):
|
||||
"""
|
||||
Run all_gather on arbitrary picklable data (not necessarily tensors)
|
||||
Args:
|
||||
data: any picklable object
|
||||
Returns:
|
||||
list[data]: list of data gathered from each rank
|
||||
"""
|
||||
world_size = get_world_size()
|
||||
if world_size == 1:
|
||||
return [data]
|
||||
data_list = [None] * world_size
|
||||
dist.all_gather_object(data_list, data)
|
||||
return data_list
|
||||
|
||||
|
||||
def reduce_dict(input_dict, average=True):
|
||||
"""
|
||||
Args:
|
||||
input_dict (dict): all the values will be reduced
|
||||
average (bool): whether to do average or sum
|
||||
Reduce the values in the dictionary from all processes so that all processes
|
||||
have the averaged results. Returns a dict with the same fields as
|
||||
input_dict, after reduction.
|
||||
"""
|
||||
world_size = get_world_size()
|
||||
if world_size < 2:
|
||||
return input_dict
|
||||
with torch.no_grad():
|
||||
names = []
|
||||
values = []
|
||||
# sort the keys so that they are consistent across processes
|
||||
for k in sorted(input_dict.keys()):
|
||||
names.append(k)
|
||||
values.append(input_dict[k])
|
||||
values = torch.stack(values, dim=0)
|
||||
dist.all_reduce(values)
|
||||
if average:
|
||||
values /= world_size
|
||||
reduced_dict = {k: v for k, v in zip(names, values)}
|
||||
return reduced_dict
|
||||
|
||||
|
||||
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 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):
|
||||
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'
|
||||
if torch.cuda.is_available():
|
||||
log_msg = self.delimiter.join([
|
||||
header,
|
||||
'[{0' + space_fmt + '}/{1}]',
|
||||
'eta: {eta}',
|
||||
'{meters}',
|
||||
'time: {time}',
|
||||
'data: {data}',
|
||||
'max mem: {memory:.0f}'
|
||||
])
|
||||
else:
|
||||
log_msg = self.delimiter.join([
|
||||
header,
|
||||
'[{0' + space_fmt + '}/{1}]',
|
||||
'eta: {eta}',
|
||||
'{meters}',
|
||||
'time: {time}',
|
||||
'data: {data}'
|
||||
])
|
||||
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)))
|
||||
|
||||
|
||||
def collate_fn(batch):
|
||||
return tuple(zip(*batch))
|
||||
|
||||
|
||||
def warmup_lr_scheduler(optimizer, warmup_iters, warmup_factor):
|
||||
|
||||
def f(x):
|
||||
if x >= warmup_iters:
|
||||
return 1
|
||||
alpha = float(x) / warmup_iters
|
||||
return warmup_factor * (1 - alpha) + alpha
|
||||
|
||||
return torch.optim.lr_scheduler.LambdaLR(optimizer, f)
|
||||
|
||||
|
||||
def mkdir(path):
|
||||
try:
|
||||
os.makedirs(path)
|
||||
except OSError as e:
|
||||
if e.errno != errno.EEXIST:
|
||||
raise
|
||||
|
||||
|
||||
def setup_for_distributed(is_master):
|
||||
"""
|
||||
This function disables printing when not in master process
|
||||
"""
|
||||
import builtins as __builtin__
|
||||
builtin_print = __builtin__.print
|
||||
|
||||
def print(*args, **kwargs):
|
||||
force = kwargs.pop('force', False)
|
||||
if is_master or force:
|
||||
builtin_print(*args, **kwargs)
|
||||
|
||||
__builtin__.print = print
|
||||
|
||||
|
||||
def is_dist_avail_and_initialized():
|
||||
if not dist.is_available():
|
||||
return False
|
||||
if not dist.is_initialized():
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
def get_world_size():
|
||||
if not is_dist_avail_and_initialized():
|
||||
return 1
|
||||
return dist.get_world_size()
|
||||
|
||||
|
||||
def get_rank():
|
||||
if not is_dist_avail_and_initialized():
|
||||
return 0
|
||||
return dist.get_rank()
|
||||
|
||||
|
||||
def is_main_process():
|
||||
return get_rank() == 0
|
||||
|
||||
|
||||
def save_on_master(*args, **kwargs):
|
||||
if is_main_process():
|
||||
torch.save(*args, **kwargs)
|
||||
|
||||
|
||||
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()
|
||||
setup_for_distributed(args.rank == 0)
|
||||
Reference in New Issue
Block a user