151 lines
4.8 KiB
Python
151 lines
4.8 KiB
Python
from collections import defaultdict, deque
|
|
import datetime
|
|
import logging
|
|
import random
|
|
import time
|
|
|
|
import numpy as np
|
|
import torch
|
|
import torch.distributed as dist
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
def random_seed(seed=0):
|
|
random.seed(seed)
|
|
torch.random.manual_seed(seed)
|
|
np.random.seed(seed)
|
|
|
|
|
|
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=1000, fmt=None):
|
|
if fmt is None:
|
|
fmt = "{avg:.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!
|
|
"""
|
|
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", window_size=1000, fmt=None):
|
|
self.meters = defaultdict(lambda: SmoothedValue(window_size, fmt))
|
|
self.delimiter = delimiter
|
|
|
|
def update(self, **kwargs):
|
|
for k, v in kwargs.items():
|
|
if v is None:
|
|
continue
|
|
elif isinstance(v, (torch.Tensor, float, int)):
|
|
self.meters[k].update(v.item() if isinstance(v, torch.Tensor) else v)
|
|
elif isinstance(v, list):
|
|
for i, sub_v in enumerate(v):
|
|
self.meters[f"{k}_{i}"].update(sub_v.item() if isinstance(sub_v, torch.Tensor) else sub_v)
|
|
elif isinstance(v, dict):
|
|
for sub_key, sub_v in v.items():
|
|
self.meters[f"{k}_{sub_key}"].update(sub_v.item() if isinstance(sub_v, torch.Tensor) else sub_v)
|
|
else:
|
|
raise TypeError(f"Unsupported type {type(v)} for metric {k}")
|
|
|
|
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, start_iter=0, samples_per_iter=None):
|
|
i = start_iter
|
|
if not header:
|
|
header = ""
|
|
start_time = time.time()
|
|
end = time.time()
|
|
iter_time = SmoothedValue(fmt="{avg:.4f}")
|
|
data_time = SmoothedValue(fmt="{avg:.4f}")
|
|
log_msg = [header, "[{0" + "}/{1}]", "{meters}", "time: {time}", "data: {data}"]
|
|
if samples_per_iter is not None:
|
|
log_msg.append("samples/sec: {samples_per_sec:.2f}")
|
|
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:
|
|
try:
|
|
total_len = len(iterable)
|
|
except:
|
|
total_len = "unknown"
|
|
|
|
msg_kwargs = {
|
|
"meters": str(self),
|
|
"time": str(iter_time),
|
|
"data": str(data_time),
|
|
}
|
|
if samples_per_iter is not None:
|
|
msg_kwargs["samples_per_sec"] = samples_per_iter / iter_time.avg
|
|
if torch.cuda.is_available():
|
|
msg_kwargs["memory"] = torch.cuda.max_memory_allocated() / MB
|
|
|
|
logger.info(log_msg.format(i, total_len, **msg_kwargs))
|
|
i += 1
|
|
end = time.time()
|
|
total_time = time.time() - start_time
|
|
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
|
|
logger.info("{} Total time: {}".format(header, total_time_str))
|