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import io
import os
import math
import time
import json
import glob
from collections import defaultdict, deque, OrderedDict
import datetime
import numpy as np
from pathlib import Path
import argparse
import torch
from torch import optim as optim
import torch.distributed as dist
from tensorboardX import SummaryWriter
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 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 init_distributed_mode(args):
if int(os.getenv('OMPI_COMM_WORLD_SIZE', '0')) > 0:
rank = int(os.environ['OMPI_COMM_WORLD_RANK'])
local_rank = int(os.environ['OMPI_COMM_WORLD_LOCAL_RANK'])
world_size = int(os.environ['OMPI_COMM_WORLD_SIZE'])
os.environ["LOCAL_RANK"] = os.environ['OMPI_COMM_WORLD_LOCAL_RANK']
os.environ["RANK"] = os.environ['OMPI_COMM_WORLD_RANK']
os.environ["WORLD_SIZE"] = os.environ['OMPI_COMM_WORLD_SIZE']
args.rank = int(os.environ["RANK"])
args.world_size = int(os.environ["WORLD_SIZE"])
args.gpu = int(os.environ["LOCAL_RANK"])
elif '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'])
else:
print('Not using distributed mode')
args.distributed = False
return
args.distributed = True
args.dist_backend = 'nccl'
args.dist_url = "env://"
print('| distributed init (rank {}): {}, gpu {}'.format(
args.rank, args.dist_url, args.gpu), flush=True)
def cosine_scheduler(base_value, final_value, epochs, niter_per_ep, warmup_epochs=0,
start_warmup_value=0, warmup_steps=-1):
warmup_schedule = np.array([])
warmup_iters = warmup_epochs * niter_per_ep
if warmup_steps > 0:
warmup_iters = warmup_steps
print("Set warmup steps = %d" % warmup_iters)
if warmup_epochs > 0:
warmup_schedule = np.linspace(start_warmup_value, base_value, warmup_iters)
iters = np.arange(epochs * niter_per_ep - warmup_iters)
schedule = np.array(
[final_value + 0.5 * (base_value - final_value) * (1 + math.cos(math.pi * i / (len(iters)))) for i in iters])
schedule = np.concatenate((warmup_schedule, schedule))
assert len(schedule) == epochs * niter_per_ep
return schedule
def constant_scheduler(base_value, epochs, niter_per_ep, warmup_epochs=0,
start_warmup_value=1e-6, warmup_steps=-1):
warmup_schedule = np.array([])
warmup_iters = warmup_epochs * niter_per_ep
if warmup_steps > 0:
warmup_iters = warmup_steps
print("Set warmup steps = %d" % warmup_iters)
if warmup_iters > 0:
warmup_schedule = np.linspace(start_warmup_value, base_value, warmup_iters)
iters = epochs * niter_per_ep - warmup_iters
schedule = np.array([base_value] * iters)
schedule = np.concatenate((warmup_schedule, schedule))
assert len(schedule) == epochs * niter_per_ep
return schedule
def get_parameter_groups(model, weight_decay=1e-5, base_lr=1e-4, skip_list=(), get_num_layer=None, get_layer_scale=None, **kwargs):
parameter_group_names = {}
parameter_group_vars = {}
for name, param in model.named_parameters():
if not param.requires_grad:
continue # frozen weights
if len(kwargs.get('filter_name', [])) > 0:
flag = False
for filter_n in kwargs.get('filter_name', []):
if filter_n in name:
print(f"filter {name} because of the pattern {filter_n}")
flag = True
if flag:
continue
default_scale=1.
if param.ndim <= 1 or name.endswith(".bias") or name in skip_list: # param.ndim <= 1 len(param.shape) == 1
group_name = "no_decay"
this_weight_decay = 0.
else:
group_name = "decay"
this_weight_decay = weight_decay
if get_num_layer is not None:
layer_id = get_num_layer(name)
group_name = "layer_%d_%s" % (layer_id, group_name)
else:
layer_id = None
if group_name not in parameter_group_names:
if get_layer_scale is not None:
scale = get_layer_scale(layer_id)
else:
scale = default_scale
parameter_group_names[group_name] = {
"weight_decay": this_weight_decay,
"params": [],
"lr": base_lr,
"lr_scale": scale,
}
parameter_group_vars[group_name] = {
"weight_decay": this_weight_decay,
"params": [],
"lr": base_lr,
"lr_scale": scale,
}
parameter_group_vars[group_name]["params"].append(param)
parameter_group_names[group_name]["params"].append(name)
print("Param groups = %s" % json.dumps(parameter_group_names, indent=2))
return list(parameter_group_vars.values())
def create_optimizer(args, model, get_num_layer=None, get_layer_scale=None, filter_bias_and_bn=True, skip_list=None, **kwargs):
opt_lower = args.opt.lower()
weight_decay = args.weight_decay
skip = {}
if skip_list is not None:
skip = skip_list
elif hasattr(model, 'no_weight_decay'):
skip = model.no_weight_decay()
print(f"Skip weight decay name marked in model: {skip}")
parameters = get_parameter_groups(model, weight_decay, args.lr, skip, get_num_layer, get_layer_scale, **kwargs)
weight_decay = 0.
if 'fused' in opt_lower:
assert has_apex and torch.cuda.is_available(), 'APEX and CUDA required for fused optimizers'
opt_args = dict(lr=args.lr, weight_decay=weight_decay)
if hasattr(args, 'opt_eps') and args.opt_eps is not None:
opt_args['eps'] = args.opt_eps
if hasattr(args, 'opt_beta1') and args.opt_beta1 is not None:
opt_args['betas'] = (args.opt_beta1, args.opt_beta2)
print('Optimizer config:', opt_args)
opt_split = opt_lower.split('_')
opt_lower = opt_split[-1]
if opt_lower == 'sgd' or opt_lower == 'nesterov':
opt_args.pop('eps', None)
optimizer = optim.SGD(parameters, momentum=args.momentum, nesterov=True, **opt_args)
elif opt_lower == 'momentum':
opt_args.pop('eps', None)
optimizer = optim.SGD(parameters, momentum=args.momentum, nesterov=False, **opt_args)
elif opt_lower == 'adam':
optimizer = optim.Adam(parameters, **opt_args)
elif opt_lower == 'adamw':
optimizer = optim.AdamW(parameters, **opt_args)
elif opt_lower == 'adadelta':
optimizer = optim.Adadelta(parameters, **opt_args)
elif opt_lower == 'rmsprop':
optimizer = optim.RMSprop(parameters, alpha=0.9, momentum=args.momentum, **opt_args)
else:
assert False and "Invalid optimizer"
raise ValueError
return optimizer
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 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():
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)))