optimizer.py

# -*- encoding: utf-8 -*-
# ----------------------------------------------
# filename        :optimizer.py
# description     :NomMer: Nominate Synergistic Context in Vision Transformer for Visual Recognition
# date            :2021/12/28 17:44:27
# author          :clark
# version number  :1.0
# ----------------------------------------------


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()
    parameters = set_weight_decay(model, skip, skip_keywords)

    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,
        )
    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=()):
    has_decay = []
    no_decay = []

    for name, param in model.named_parameters():
        if not param.requires_grad:
            continue  # frozen weights
        if (
            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)
            # print(f"{name} has no weight decay")
        else:
            has_decay.append(param)
    return [{'params': has_decay}, {'params': no_decay, 'weight_decay': 0.0}]


def check_keywords_in_name(name, keywords=()):
    isin = False
    for keyword in keywords:
        if keyword in name:
            isin = True
    return isin