optimizer.py

# --------------------------------------------------------
# RepMLPNet: Hierarchical Vision MLP with Re-parameterized Locality (https://arxiv.org/abs/2112.11081)
# CVPR 2022
# Github source: https://github.com/DingXiaoH/RepMLP
# 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 == '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