File size: 7,355 Bytes
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from collections import defaultdict, deque
import logging
import torch.distributed as dist
import time
import datetime
from tensorboardX import SummaryWriter
from .distributed import is_dist_avail_and_initialized
class SmoothedValue(object):
"""Track a series of values and provide access to smoothed values over a
window or the global series average.xxxxxxxxxxx
"""
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)
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 v is None:
continue
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'
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():
logging.info(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:
logging.info(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)))
logging.info('{} Total time: {} ({:.4f} s / it)'.format(
header, total_time_str, total_time / len(iterable)))
class TensorboardLogger(object):
def __init__(self, log_dir):
self.writer = SummaryWriter(logdir=log_dir)
self.step = 0
def set_step(self, step=None):
if step is not None:
self.step = step
else:
self.step += 1
def update(self, head='scalar', step=None, **kwargs):
for k, v in kwargs.items():
if v is None:
continue
if isinstance(v, torch.Tensor):
v = v.item()
assert isinstance(v, (float, int))
self.writer.add_scalar(head + "/" + k, v, self.step if step is None else step)
def flush(self):
self.writer.flush()
class WandbLogger(object):
def __init__(self, args):
self.args = args
try:
import wandb
self._wandb = wandb
except ImportError:
raise ImportError(
"To use the Weights and Biases Logger please install wandb."
"Run `pip install wandb` to install it."
)
# Initialize a W&B run
if self._wandb.run is None:
self._wandb.init(
project=args.project,
config=args
)
def log_epoch_metrics(self, metrics, commit=True):
"""
Log train/test metrics onto W&B.
"""
# Log number of model parameters as W&B summary
self._wandb.summary['n_parameters'] = metrics.get('n_parameters', None)
metrics.pop('n_parameters', None)
# Log current epoch
self._wandb.log({'epoch': metrics.get('epoch')}, commit=False)
metrics.pop('epoch')
for k, v in metrics.items():
if 'train' in k:
self._wandb.log({f'Global Train/{k}': v}, commit=False)
elif 'test' in k:
self._wandb.log({f'Global Test/{k}': v}, commit=False)
self._wandb.log({})
def log_checkpoints(self):
output_dir = self.args.output_dir
model_artifact = self._wandb.Artifact(
self._wandb.run.id + "_model", type="model"
)
model_artifact.add_dir(output_dir)
self._wandb.log_artifact(model_artifact, aliases=["latest", "best"])
def set_steps(self):
# Set global training step
self._wandb.define_metric('Rank-0 Batch Wise/*', step_metric='Rank-0 Batch Wise/global_train_step')
# Set epoch-wise step
self._wandb.define_metric('Global Train/*', step_metric='epoch')
self._wandb.define_metric('Global Test/*', step_metric='epoch')
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