Exponential moving averages of model parameters #321
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RaulPPelaez
reviewed
May 14, 2024
| @@ -17,3 +17,5 @@ dependencies: | |||
| - pytest | |||
| - psutil | |||
| - gxx<12 | |||
| - pip: | |||
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AFAIK this is a dangerous idea. Note that this project you link to is a short and single MIT-licensed class: https://github.com/fadel/pytorch_ema/blob/master/torch_ema/ema.py
I would consider just including it in the repo with the appropriate copyright notice.
RaulPPelaez
reviewed
May 14, 2024
| @@ -73,6 +74,11 @@ def __init__(self, hparams, prior_model=None, mean=None, std=None): | |||
| else: | |||
| self.model = create_model(self.hparams, prior_model, mean, std) | |||
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| self.ema_prmtrs = None | |||
| if self.hparams.ema_prmtrs_decay is not None: | |||
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Tests fail because of this. You have to first make sure the element exists, see how its done with the charge above:
if "charge" not in hparams:Alternatively you can deal with hparams as a dict before the call to save_hyperparameters.
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Enhance model training by enabling Exponential Moving Average (EMA) on the model parameters. To activate this feature, simply specify the
ema_prmtrs_decayarg in the configuration file. If not set, its default value is None, indicating that EMA is not going to be applied.