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# Licensed under the Apache License, Version 2.0 (the "License");
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import torch.nn as nn
from . import callback_register, callback_config_register
from typing import Dict, List
import os
from pytorch_lightning.callbacks import StochasticWeightAveraging
[docs]@callback_config_register('weight_average')
class StochasticWeightAveragingCallbackConfig(object):
"""Config for StochasticWeightAveragingCallback
Config Example:
>>> { //weight_average default
>>> "_name": "weight_average",
>>> "config": {
>>> "swa_epoch_start": 0.8, // swa start epoch
>>> "swa_lrs": null,
>>> //None. Use the current learning rate of the optimizer at the time the SWA procedure starts.
>>> //float. Use this value for all parameter groups of the optimizer.
>>> //List[float]. A list values for each parameter group of the optimizer.
>>> "annealing_epochs": 10,
>>> "annealing_strategy": 'cos',
>>> "device": null, // save device, null for auto detach, if the gpu is oom, you should change this to 'cpu'
>>> }
>>> }
"""
def __init__(self, config):
super(StochasticWeightAveragingCallbackConfig, self).__init__()
config = config['config']
self.swa_epoch_start = config['swa_epoch_start']
self.swa_lrs = config["swa_lrs"]
self.annealing_epochs = config["annealing_epochs"]
self.annealing_strategy = config["annealing_strategy"]
self.device = config["device"]
[docs]@callback_register('weight_average')
class StochasticWeightAveragingCallback(object):
"""Average weight by config
"""
def __init__(self, config: StochasticWeightAveragingCallbackConfig):
super().__init__()
self.config = config
def __call__(self, rt_config: Dict)->StochasticWeightAveraging:
"""return StochasticWeightAveraging object
Args:
rt_config: runtime config, include save_dir, and the checkpoint path name
Returns:
StochasticWeightAveraging object
"""
return StochasticWeightAveraging(**self.config.__dict__)