Note

You are reading the documentation for MMSelfSup 0.x, which will soon be deprecated by the end of 2022. We recommend you upgrade to MMSelfSup 1.0.0rc versions to enjoy fruitful new features and better performance brought by OpenMMLab 2.0. Check out the changelog, code and documentation of MMSelfSup 1.0.0rc for more details.

Source code for mmselfsup.core.optimizer.builder

# Copyright (c) OpenMMLab. All rights reserved.
import copy

from mmcv.runner.optimizer.builder import build_optimizer_constructor


def build_optimizer(model, optimizer_cfg):
    """Build optimizer from configs.

    Args:
        model (:obj:`nn.Module`): The model with parameters to be optimized.
        optimizer_cfg (dict): The config dict of the optimizer.
            Positional fields are:
                - type: class name of the optimizer.
                - lr: base learning rate.
            Optional fields are:
                - any arguments of the corresponding optimizer type, e.g.,
                  weight_decay, momentum, etc.
                - paramwise_options: a dict with regular expression as keys
                  to match parameter names and a dict containing options as
                  values. Options include 6 fields: lr, lr_mult, momentum,
                  momentum_mult, weight_decay, weight_decay_mult.

    Returns:
        torch.optim.Optimizer: The initialized optimizer.

    Example:
        >>> model = torch.nn.modules.Conv1d(1, 1, 1)
        >>> paramwise_options = {
        >>>     '(bn|gn)(\\d+)?.(weight|bias)': dict(weight_decay_mult=0.1),
        >>>     '\\Ahead.': dict(lr_mult=10, momentum=0)}
        >>> optimizer_cfg = dict(type='SGD', lr=0.01, momentum=0.9,
        >>>                      weight_decay=0.0001,
        >>>                      paramwise_options=paramwise_options)
        >>> optimizer = build_optimizer(model, optimizer_cfg)
    """
    optimizer_cfg = copy.deepcopy(optimizer_cfg)
    constructor_type = optimizer_cfg.pop('constructor',
                                         'DefaultOptimizerConstructor')
    paramwise_cfg = optimizer_cfg.pop('paramwise_options', None)
    optim_constructor = build_optimizer_constructor(
        dict(
            type=constructor_type,
            optimizer_cfg=optimizer_cfg,
            paramwise_cfg=paramwise_cfg))
    optimizer = optim_constructor(model)
    return optimizer