support EMA for fp32/fp64 #60
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guolinke
reviewed
Jul 1, 2024
| else: | ||
| with torch.no_grad(): | ||
| for n, p in new_param: | ||
| if n in self.name2param: |
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what if n is not in "n in self.name2param:" ?
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I guess parameters not in self.name2param are those with requires_grad=False
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I can also implement this logic in the trainer, by filtering the model.named_parameters(). Which implementation is better?
guolinke
approved these changes
Jul 17, 2024
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