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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license 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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