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# Copyright (C) 2022-present Naver Corporation. All rights reserved.
# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
#
# --------------------------------------------------------
# utilitary functions for CroCo
# --------------------------------------------------------
# References:
# MAE: https://github.com/facebookresearch/mae
# DeiT: https://github.com/facebookresearch/deit
# BEiT: https://github.com/microsoft/unilm/tree/master/beit
# --------------------------------------------------------
import builtins
import datetime
import os
import time
import math
import json
from collections import defaultdict, deque
from pathlib import Path
import numpy as np
import torch
import torch.distributed as dist
from torch import inf
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, max_iter=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}')
len_iterable = min(len(iterable), max_iter) if max_iter else len(iterable)
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 it,obj in enumerate(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()
if max_iter and it >= max_iter:
break
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))
def setup_for_distributed(is_master):
"""
This function disables printing when not in master process
"""
builtin_print = builtins.print
def print(*args, **kwargs):
force = kwargs.pop('force', False)
force = force or (get_world_size() > 8)
if is_master or force:
now = datetime.datetime.now().time()
builtin_print('[{}] '.format(now), end='') # print with time stamp
builtin_print(*args, **kwargs)
builtins.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):
#### Hengyi: WORLD_SIZE is the number of GPUs used in the training
# RANK is a unique identifier for each process in the distributed training
# Local_rank is the GPU index of the current process
# e.g. Consider a distributed training setup with 2 nodes, each having 4 GPUs (total WORLD_SIZE of 8):
# Node 1:
# Process (GPU) 0: RANK 0, LOCAL_RANK 0
# Process (GPU) 1: RANK 1, LOCAL_RANK 1
# Process (GPU) 2: RANK 2, LOCAL_RANK 2
# Process (GPU) 3: RANK 3, LOCAL_RANK 3
# Node 2:
# Process (GPU) 0: RANK 4, LOCAL_RANK 0
# Process (GPU) 1: RANK 5, LOCAL_RANK 1
# Process (GPU) 2: RANK 6, LOCAL_RANK 2
# Process (GPU) 3: RANK 7, LOCAL_RANK 3
nodist = args.nodist if hasattr(args,'nodist') else False
if 'RANK' in os.environ and 'WORLD_SIZE' in os.environ and not nodist:
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')
setup_for_distributed(is_master=True) # hack
args.distributed = False
return
args.distributed = True
torch.cuda.set_device(args.gpu)
args.dist_backend = 'nccl'
print('| distributed init (rank {}): {}, gpu {}'.format(
args.rank, args.dist_url, args.gpu), 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)
class NativeScalerWithGradNormCount:
state_dict_key = "amp_scaler"
def __init__(self, enabled=True):
self._scaler = torch.cuda.amp.GradScaler(enabled=enabled)
def __call__(self, loss, optimizer, clip_grad=None, parameters=None, create_graph=False, update_grad=True):
self._scaler.scale(loss).backward(create_graph=create_graph)
if update_grad:
if clip_grad is not None:
assert parameters is not None
self._scaler.unscale_(optimizer) # unscale the gradients of optimizer's assigned params in-place
norm = torch.nn.utils.clip_grad_norm_(parameters, clip_grad)
else:
self._scaler.unscale_(optimizer)
norm = get_grad_norm_(parameters)
self._scaler.step(optimizer)
self._scaler.update()
else:
norm = None
return norm
def state_dict(self):
return self._scaler.state_dict()
def load_state_dict(self, state_dict):
self._scaler.load_state_dict(state_dict)
def get_grad_norm_(parameters, norm_type: float = 2.0) -> torch.Tensor:
if isinstance(parameters, torch.Tensor):
parameters = [parameters]
parameters = [p for p in parameters if p.grad is not None]
norm_type = float(norm_type)
if len(parameters) == 0:
return torch.tensor(0.)
device = parameters[0].grad.device
if norm_type == inf:
total_norm = max(p.grad.detach().abs().max().to(device) for p in parameters)
else:
total_norm = torch.norm(torch.stack([torch.norm(p.grad.detach(), norm_type).to(device) for p in parameters]), norm_type)
return total_norm
def save_model(args, epoch, model_without_ddp, optimizer, loss_scaler, fname=None, best_so_far=None):
output_dir = Path(args.output_dir)
if fname is None: fname = str(epoch)
checkpoint_path = output_dir / ('checkpoint-%s.pth' % fname)
to_save = {
'model': model_without_ddp.state_dict(),
'optimizer': optimizer.state_dict(),
'scaler': loss_scaler.state_dict(),
'args': args,
'epoch': epoch,
}
if best_so_far is not None: to_save['best_so_far'] = best_so_far
print(f'>> Saving model to {checkpoint_path} ...')
save_on_master(to_save, checkpoint_path)
def load_model(args, model_without_ddp, optimizer, loss_scaler):
args.start_epoch = 0
best_so_far = None
if args.resume is not None:
if args.resume.startswith('https'):
checkpoint = torch.hub.load_state_dict_from_url(
args.resume, map_location='cpu', check_hash=True)
else:
checkpoint = torch.load(args.resume, map_location='cpu')
print("Resume checkpoint %s" % args.resume)
model_without_ddp.load_state_dict(checkpoint['model'], strict=False)
args.start_epoch = checkpoint['epoch'] + 1
optimizer.load_state_dict(checkpoint['optimizer'])
if 'scaler' in checkpoint:
loss_scaler.load_state_dict(checkpoint['scaler'])
if 'best_so_far' in checkpoint:
best_so_far = checkpoint['best_so_far']
print(" & best_so_far={:g}".format(best_so_far))
else:
print("")
print("With optim & sched! start_epoch={:d}".format(args.start_epoch), end='')
return best_so_far
def all_reduce_mean(x):
world_size = get_world_size()
if world_size > 1:
x_reduce = torch.tensor(x).cuda()
dist.all_reduce(x_reduce)
x_reduce /= world_size
return x_reduce.item()
else:
return x
def _replace(text, src, tgt, rm=''):
""" Advanced string replacement.
Given a text:
- replace all elements in src by the corresponding element in tgt
- remove all elements in rm
"""
if len(tgt) == 1:
tgt = tgt * len(src)
assert len(src) == len(tgt), f"'{src}' and '{tgt}' should have the same len"
for s,t in zip(src, tgt):
text = text.replace(s,t)
for c in rm:
text = text.replace(c,'')
return text
def filename( obj ):
""" transform a python obj or cmd into a proper filename.
- \1 gets replaced by slash '/'
- \2 gets replaced by comma ','
"""
if not isinstance(obj, str):
obj = repr(obj)
obj = str(obj).replace('()','')
obj = _replace(obj, '_,(*/\1\2','-__x%/,', rm=' )\'"')
assert all(len(s) < 256 for s in obj.split(os.sep)), 'filename too long (>256 characters):\n'+obj
return obj
def _get_num_layer_for_vit(var_name, enc_depth, dec_depth):
if var_name in ("cls_token", "mask_token", "pos_embed", "global_tokens"):
return 0
elif var_name.startswith("patch_embed"):
return 0
elif var_name.startswith("enc_blocks"):
layer_id = int(var_name.split('.')[1])
return layer_id + 1
elif var_name.startswith('decoder_embed') or var_name.startswith('enc_norm'): # part of the last black
return enc_depth
elif var_name.startswith('dec_blocks'):
layer_id = int(var_name.split('.')[1])
return enc_depth + layer_id + 1
elif var_name.startswith('dec_norm'): # part of the last block
return enc_depth + dec_depth
elif any(var_name.startswith(k) for k in ['head','prediction_head']):
return enc_depth + dec_depth + 1
else:
raise NotImplementedError(var_name)
def get_parameter_groups(model, weight_decay, layer_decay=1.0, skip_list=(), no_lr_scale_list=[]):
parameter_group_names = {}
parameter_group_vars = {}
enc_depth, dec_depth = None, None
# prepare layer decay values
assert layer_decay==1.0 or 0.<layer_decay<1.
if layer_decay<1.:
enc_depth = model.enc_depth
dec_depth = model.dec_depth if hasattr(model, 'dec_blocks') else 0
num_layers = enc_depth+dec_depth
layer_decay_values = list(layer_decay ** (num_layers + 1 - i) for i in range(num_layers + 2))
for name, param in model.named_parameters():
if not param.requires_grad:
continue # frozen weights
# Assign weight decay values
if len(param.shape) == 1 or name.endswith(".bias") or name in skip_list:
group_name = "no_decay"
this_weight_decay = 0.
else:
group_name = "decay"
this_weight_decay = weight_decay
# Assign layer ID for LR scaling
if layer_decay<1.:
skip_scale = False
layer_id = _get_num_layer_for_vit(name, enc_depth, dec_depth)
group_name = "layer_%d_%s" % (layer_id, group_name)
if name in no_lr_scale_list:
skip_scale = True
group_name = f'{group_name}_no_lr_scale'
else:
layer_id = 0
skip_scale = True
if group_name not in parameter_group_names:
if not skip_scale:
scale = layer_decay_values[layer_id]
else:
scale = 1.
parameter_group_names[group_name] = {
"weight_decay": this_weight_decay,
"params": [],
"lr_scale": scale
}
parameter_group_vars[group_name] = {
"weight_decay": this_weight_decay,
"params": [],
"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 adjust_learning_rate(optimizer, epoch, args):
"""Decay the learning rate with half-cycle cosine after warmup"""
if epoch < args.warmup_epochs:
lr = args.lr * epoch / args.warmup_epochs
else:
lr = args.min_lr + (args.lr - args.min_lr) * 0.5 * \
(1. + math.cos(math.pi * (epoch - args.warmup_epochs) / (args.epochs - args.warmup_epochs)))
for param_group in optimizer.param_groups:
if "lr_scale" in param_group:
param_group["lr"] = lr * param_group["lr_scale"]
else:
param_group["lr"] = lr
return lr