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		| @@ -1,7 +1,6 @@ | ||||
| ##################################################### | ||||
| # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 # | ||||
| ##################################################### | ||||
| import copy | ||||
| import torch | ||||
|  | ||||
| import torch.nn.functional as F | ||||
| @@ -294,7 +293,9 @@ class LFNA_Meta(super_core.SuperModule): | ||||
|                 best_loss = init_info["loss"] | ||||
|                 new_param.data.copy_(init_info["param"].data) | ||||
|             else: | ||||
|                 best_new_param, best_loss = None, 1e9 | ||||
|                 best_loss = 1e9 | ||||
|             with torch.no_grad(): | ||||
|                 best_new_param = new_param.detach().clone() | ||||
|             for iepoch in range(epochs): | ||||
|                 optimizer.zero_grad() | ||||
|                 _, [_], time_embed = self(timestamp.view(1, 1), None, True) | ||||
| @@ -310,7 +311,7 @@ class LFNA_Meta(super_core.SuperModule): | ||||
|                 if meta_loss.item() < best_loss: | ||||
|                     with torch.no_grad(): | ||||
|                         best_loss = meta_loss.item() | ||||
|                         best_new_param = new_param.detach() | ||||
|                         best_new_param = new_param.detach().clone() | ||||
|         with torch.no_grad(): | ||||
|             self.replace_append_learnt(None, None) | ||||
|             self.append_fixed(timestamp, best_new_param) | ||||
|   | ||||
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