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# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the Apache License, Version 2.0
# found in the LICENSE file in the root directory of this source tree.
from collections import defaultdict, deque
import datetime
import json
import logging
import time
import torch
import dinov2.distributed as distributed
logger = logging.getLogger("dinov2")
class MetricLogger(object):
def __init__(self, delimiter="\t", output_file=None):
self.meters = defaultdict(SmoothedValue)
self.delimiter = delimiter
self.output_file = output_file
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 dump_in_output_file(self, iteration, iter_time, data_time):
if self.output_file is None or not distributed.is_main_process():
return
dict_to_dump = dict(
iteration=iteration,
iter_time=iter_time,
data_time=data_time,
)
dict_to_dump.update({k: v.median for k, v in self.meters.items()})
with open(self.output_file, "a") as f:
f.write(json.dumps(dict_to_dump) + "\n")
pass
def log_every(self, iterable, print_freq, header=None, n_iterations=None, start_iteration=0):
i = start_iteration
if not header:
header = ""
start_time = time.time()
end = time.time()
iter_time = SmoothedValue(fmt="{avg:.6f}")
data_time = SmoothedValue(fmt="{avg:.6f}")
if n_iterations is None:
n_iterations = len(iterable)
space_fmt = ":" + str(len(str(n_iterations))) + "d"
log_list = [
header,
"[{0" + space_fmt + "}/{1}]",
"eta: {eta}",
"{meters}",
"time: {time}",
"data: {data}",
]
if torch.cuda.is_available():
log_list += ["max mem: {memory:.0f}"]
log_msg = self.delimiter.join(log_list)
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 == n_iterations - 1:
self.dump_in_output_file(iteration=i, iter_time=iter_time.avg, data_time=data_time.avg)
eta_seconds = iter_time.global_avg * (n_iterations - i)
eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
if torch.cuda.is_available():
logger.info(
log_msg.format(
i,
n_iterations,
eta=eta_string,
meters=str(self),
time=str(iter_time),
data=str(data_time),
memory=torch.cuda.max_memory_allocated() / MB,
)
)
else:
logger.info(
log_msg.format(
i,
n_iterations,
eta=eta_string,
meters=str(self),
time=str(iter_time),
data=str(data_time),
)
)
i += 1
end = time.time()
if i >= n_iterations:
break
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
logger.info("{} Total time: {} ({:.6f} s / it)".format(header, total_time_str, total_time / n_iterations))
class SmoothedValue:
"""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, num=1):
self.deque.append(value)
self.count += num
self.total += value * num
def synchronize_between_processes(self):
"""
Distributed synchronization of the metric
Warning: does not synchronize the deque!
"""
if not distributed.is_enabled():
return
t = torch.tensor([self.count, self.total], dtype=torch.float64, device="cuda")
torch.distributed.barrier()
torch.distributed.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,
)
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