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# --------------------------------------------------------
# RepVGG: Making VGG-style ConvNets Great Again (https://openaccess.thecvf.com/content/CVPR2021/papers/Ding_RepVGG_Making_VGG-Style_ConvNets_Great_Again_CVPR_2021_paper.pdf)
# Github source: https://github.com/DingXiaoH/RepVGG
# Licensed under The MIT License [see LICENSE for details]
# The training script is based on the code of Swin Transformer (https://github.com/microsoft/Swin-Transformer)
# --------------------------------------------------------
from torch import optim as optim
def build_optimizer(config, model):
"""
Build optimizer, set weight decay of normalization to 0 by default.
"""
skip = {}
skip_keywords = {}
if hasattr(model, 'no_weight_decay'):
skip = model.no_weight_decay()
if hasattr(model, 'no_weight_decay_keywords'):
skip_keywords = model.no_weight_decay_keywords()
echo = (config.LOCAL_RANK==0)
parameters = set_weight_decay(model, skip, skip_keywords, echo=echo)
opt_lower = config.TRAIN.OPTIMIZER.NAME.lower()
optimizer = None
if opt_lower == 'sgd':
optimizer = optim.SGD(parameters, momentum=config.TRAIN.OPTIMIZER.MOMENTUM, nesterov=True,
lr=config.TRAIN.BASE_LR, weight_decay=config.TRAIN.WEIGHT_DECAY)
if echo:
print('================================== SGD nest, momentum = {}, wd = {}'.format(config.TRAIN.OPTIMIZER.MOMENTUM, config.TRAIN.WEIGHT_DECAY))
elif opt_lower == 'adam':
print('adam')
optimizer = optim.Adam(parameters, eps=config.TRAIN.OPTIMIZER.EPS, betas=config.TRAIN.OPTIMIZER.BETAS,
lr=config.TRAIN.BASE_LR, weight_decay=config.TRAIN.WEIGHT_DECAY)
elif opt_lower == 'adamw':
optimizer = optim.AdamW(parameters, eps=config.TRAIN.OPTIMIZER.EPS, betas=config.TRAIN.OPTIMIZER.BETAS,
lr=config.TRAIN.BASE_LR, weight_decay=config.TRAIN.WEIGHT_DECAY)
return optimizer
def set_weight_decay(model, skip_list=(), skip_keywords=(), echo=False):
has_decay = []
no_decay = []
for name, param in model.named_parameters():
if not param.requires_grad:
continue # frozen weights
if 'identity.weight' in name:
has_decay.append(param)
if echo:
print(f"{name} USE weight decay")
elif len(param.shape) == 1 or name.endswith(".bias") or (name in skip_list) or \
check_keywords_in_name(name, skip_keywords):
no_decay.append(param)
if echo:
print(f"{name} has no weight decay")
else:
has_decay.append(param)
if echo:
print(f"{name} USE weight decay")
return [{'params': has_decay},
{'params': no_decay, 'weight_decay': 0.}]
def check_keywords_in_name(name, keywords=()):
isin = False
for keyword in keywords:
if keyword in name:
isin = True
return isin
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