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"""
Copyright (c) 2022, salesforce.com, inc.
All rights reserved.
SPDX-License-Identifier: BSD-3-Clause
For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
"""
import datetime
import logging
import time
from collections import defaultdict, deque
import torch
import torch.distributed as dist
from minigpt4_video import dist_utils
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 dist_utils.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,
)
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):
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)
)
)
class AttrDict(dict):
def __init__(self, *args, **kwargs):
super(AttrDict, self).__init__(*args, **kwargs)
self.__dict__ = self
def setup_logger():
logging.basicConfig(
level=logging.INFO if dist_utils.is_main_process() else logging.WARN,
format="%(asctime)s [%(levelname)s] %(message)s",
handlers=[logging.StreamHandler()],
)
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