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@ -112,9 +112,15 @@ class ModelEmaV2(nn.Module):
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if self.device is not None:
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self.module.to(device=device)
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def update(self, model):
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def _update(self, model, update_fn):
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with torch.no_grad():
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for ema_v, model_v in zip(self.module.state_dict().values(), model.state_dict().values()):
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if self.device is not None:
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model_v = model_v.to(device=self.device)
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ema_v.copy_(ema_v * self.decay + (1. - self.decay) * model_v)
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ema_v.copy_(update_fn(ema_v, model_v))
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def update(self, model):
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self._update(model, update_fn=lambda e, m: self.decay * e + (1. - self.decay) * m)
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def set(self, model):
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self._update(model, update_fn=lambda e, m: m)
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