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Simplify EMA to use Pytorch's update_parameters #5469

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merged 4 commits into from
Feb 27, 2022
Merged

Simplify EMA to use Pytorch's update_parameters #5469

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f17c0f8
Simplify EMA to use Pytorch's update_parameters
xiaohu2015 Feb 24, 2022
b9de5fe
Merge branch 'pytorch:main' into patch-1
xiaohu2015 Feb 25, 2022
7a7cb10
Merge branch 'main' into patch-1
xiaohu2015 Feb 26, 2022
31b0e04
Merge branch 'main' into patch-1
datumbox Feb 27, 2022
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12 changes: 1 addition & 11 deletions references/classification/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -166,17 +166,7 @@ def __init__(self, model, decay, device="cpu"):
def ema_avg(avg_model_param, model_param, num_averaged):
return decay * avg_model_param + (1 - decay) * model_param

super().__init__(model, device, ema_avg)

def update_parameters(self, model):
for p_swa, p_model in zip(self.module.state_dict().values(), model.state_dict().values()):
device = p_swa.device
p_model_ = p_model.detach().to(device)
if self.n_averaged == 0:
p_swa.detach().copy_(p_model_)
else:
p_swa.detach().copy_(self.avg_fn(p_swa.detach(), p_model_, self.n_averaged.to(device)))
self.n_averaged += 1
super().__init__(model, device, ema_avg, use_buffers=True)


def accuracy(output, target, topk=(1,)):
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