|
import math |
|
def cosine_lr_schedule(optimizer, epoch, max_epoch, init_lr, min_lr): |
|
"""Decay the learning rate""" |
|
lr = (init_lr - min_lr) * 0.5 * (1. + math.cos(math.pi * epoch / max_epoch)) + min_lr |
|
for param_group in optimizer.param_groups: |
|
param_group['lr'] = lr |
|
|
|
def warmup_lr_schedule(optimizer, step, max_step, init_lr, max_lr): |
|
"""Warmup the learning rate""" |
|
lr = min(max_lr, init_lr + (max_lr - init_lr) * step / max_step) |
|
for param_group in optimizer.param_groups: |
|
param_group['lr'] = lr |
|
|
|
def step_lr_schedule(optimizer, epoch, init_lr, min_lr, decay_rate): |
|
"""Decay the learning rate""" |
|
lr = max(min_lr, init_lr * (decay_rate**epoch)) |
|
for param_group in optimizer.param_groups: |
|
param_group['lr'] = lr |
|
|
|
import numpy as np |
|
import io |
|
import os |
|
import time |
|
from collections import defaultdict, deque |
|
import datetime |
|
|
|
import torch |
|
import torch.distributed as dist |
|
|
|
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 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 compute_acc(logits, label, reduction='mean'): |
|
ret = (torch.argmax(logits, dim=1) == label).float() |
|
if reduction == 'none': |
|
return ret.detach() |
|
elif reduction == 'mean': |
|
return ret.mean().item() |
|
|
|
def compute_n_params(model, return_str=True): |
|
tot = 0 |
|
for p in model.parameters(): |
|
w = 1 |
|
for x in p.shape: |
|
w *= x |
|
tot += w |
|
if return_str: |
|
if tot >= 1e6: |
|
return '{:.1f}M'.format(tot / 1e6) |
|
else: |
|
return '{:.1f}K'.format(tot / 1e3) |
|
else: |
|
return tot |
|
|
|
def setup_for_distributed(is_master): |
|
""" |
|
This function disables printing when not in master process |
|
""" |
|
import builtins as __builtin__ |
|
builtin_print = __builtin__.print |
|
|
|
def print(*args, **kwargs): |
|
force = kwargs.pop('force', False) |
|
if is_master or force: |
|
builtin_print(*args, **kwargs) |
|
|
|
__builtin__.print = print |
|
|
|
|
|
def is_dist_avail_and_initialized(): |
|
if not dist.is_available(): |
|
return False |
|
if not dist.is_initialized(): |
|
return False |
|
return True |
|
|
|
|
|
def get_world_size(): |
|
if not is_dist_avail_and_initialized(): |
|
return 1 |
|
return dist.get_world_size() |
|
|
|
|
|
def get_rank(): |
|
if not is_dist_avail_and_initialized(): |
|
return 0 |
|
return dist.get_rank() |
|
|
|
|
|
def is_main_process(): |
|
return get_rank() == 0 |
|
|
|
|
|
def save_on_master(*args, **kwargs): |
|
if is_main_process(): |
|
torch.save(*args, **kwargs) |
|
|
|
|
|
def init_distributed_mode(args): |
|
if 'RANK' in os.environ and 'WORLD_SIZE' in os.environ: |
|
args.rank = int(os.environ["RANK"]) |
|
args.world_size = int(os.environ['WORLD_SIZE']) |
|
args.gpu = int(os.environ['LOCAL_RANK']) |
|
elif 'SLURM_PROCID' in os.environ: |
|
args.rank = int(os.environ['SLURM_PROCID']) |
|
args.gpu = args.rank % torch.cuda.device_count() |
|
else: |
|
print('Not using distributed mode') |
|
args.distributed = False |
|
return |
|
|
|
args.distributed = True |
|
|
|
torch.cuda.set_device(args.gpu) |
|
args.dist_backend = 'nccl' |
|
print('| distributed init (rank {}, word {}): {}'.format( |
|
args.rank, args.world_size, args.dist_url), flush=True) |
|
torch.distributed.init_process_group(backend=args.dist_backend, init_method=args.dist_url, |
|
world_size=args.world_size, rank=args.rank) |
|
torch.distributed.barrier() |
|
setup_for_distributed(args.rank == 0) |
|
|
|
|
