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| # -*- coding: utf-8 -*- | |
| # Author: ximing | |
| # Description: SVGDreamer - optim | |
| # Copyright (c) 2023, XiMing Xing. | |
| # License: MIT License | |
| from functools import partial | |
| import torch | |
| from omegaconf import DictConfig | |
| def get_optimizer(optimizer_name, parameters, lr=None, config: DictConfig = None): | |
| param_dict = {} | |
| if optimizer_name == "adam": | |
| optimizer = partial(torch.optim.Adam, params=parameters) | |
| if lr is not None: | |
| optimizer = partial(torch.optim.Adam, params=parameters, lr=lr) | |
| if config.get('betas'): | |
| param_dict['betas'] = config.betas | |
| if config.get('weight_decay'): | |
| param_dict['weight_decay'] = config.weight_decay | |
| if config.get('eps'): | |
| param_dict['eps'] = config.eps | |
| elif optimizer_name == "adamW": | |
| optimizer = partial(torch.optim.AdamW, params=parameters) | |
| if lr is not None: | |
| optimizer = partial(torch.optim.AdamW, params=parameters, lr=lr) | |
| if config.get('betas'): | |
| param_dict['betas'] = config.betas | |
| if config.get('weight_decay'): | |
| param_dict['weight_decay'] = config.weight_decay | |
| if config.get('eps'): | |
| param_dict['eps'] = config.eps | |
| elif optimizer_name == "radam": | |
| optimizer = partial(torch.optim.RAdam, params=parameters) | |
| if lr is not None: | |
| optimizer = partial(torch.optim.RAdam, params=parameters, lr=lr) | |
| if config.get('betas'): | |
| param_dict['betas'] = config.betas | |
| if config.get('weight_decay'): | |
| param_dict['weight_decay'] = config.weight_decay | |
| elif optimizer_name == "sgd": | |
| optimizer = partial(torch.optim.SGD, params=parameters) | |
| if lr is not None: | |
| optimizer = partial(torch.optim.SGD, params=parameters, lr=lr) | |
| if config.get('momentum'): | |
| param_dict['momentum'] = config.momentum | |
| if config.get('weight_decay'): | |
| param_dict['weight_decay'] = config.weight_decay | |
| if config.get('nesterov'): | |
| param_dict['nesterov'] = config.nesterov | |
| else: | |
| raise NotImplementedError(f"Optimizer {optimizer_name} not implemented.") | |
| if len(param_dict.keys()) > 0: | |
| return optimizer(**param_dict) | |
| else: | |
| return optimizer() | |