Upgrade LFNA
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@ -1,7 +1,7 @@
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#####################################################
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#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
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#####################################################
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#####################################################
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# python exps/LFNA/lfna.py --env_version v1
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# python exps/LFNA/lfna.py --env_version v1 --device cuda
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#####################################################
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#####################################################
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import sys, time, copy, torch, random, argparse
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import sys, time, copy, torch, random, argparse
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from tqdm import tqdm
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from tqdm import tqdm
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@ -55,6 +55,7 @@ class LFNA_Meta(super_core.SuperModule):
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order=super_core.LayerOrder.PostNorm,
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order=super_core.LayerOrder.PostNorm,
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)
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)
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)
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)
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layers.append(super_core.SuperLinear(time_embedding, time_embedding))
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self.meta_corrector = super_core.SuperSequential(*layers)
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self.meta_corrector = super_core.SuperSequential(*layers)
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model_kwargs = dict(
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model_kwargs = dict(
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@ -110,8 +110,10 @@ class SyntheticDEnv(data.Dataset):
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if self._seq_length is None:
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if self._seq_length is None:
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return self.__call__(timestamp)
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return self.__call__(timestamp)
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else:
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else:
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noise = random.random() * self.timestamp_interval * 0.3
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timestamps = [
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timestamps = [
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timestamp + i * self.timestamp_interval for i in range(self._seq_length)
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timestamp + i * self.timestamp_interval + noise
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for i in range(self._seq_length)
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]
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]
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xdata = [self.__call__(timestamp) for timestamp in timestamps]
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xdata = [self.__call__(timestamp) for timestamp in timestamps]
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return zip_sequence(xdata)
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return zip_sequence(xdata)
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