Comments (3)
Sorry, I am not aware not this issue; probably it's because the train_loader would be re-initialized somehow here? Since the number of data needed for BN calibration is small, you might manually save some batches in the training epoch to avoid calling train_loader here?
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Thank you for your answer! I also have a question about learning rate. I noticed in lines 30 through 32 of the https://github.com/facebookresearch/AttentiveNAS/blob/main/solver/lr_scheduler.py, you didn't use BigNAS 'learning rate strategy, which is cosine drop and 5% initial learning rate constant ending. Why is that? If self.last_epoch > self.warmup_iters
seems never to be satisfied, what is the significance of it?
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To our observations, we found the typical SGD + cosine decay setting actually works quite well.
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Related Issues (12)
- is AlphaNet a0 ~ a6 exactly same as the a0 ~ a6 in attentative NAS? HOT 1
- How to modify the loss function to apply to multi-label classification tasks HOT 6
- there are some files missing or I can't find them HOT 1
- How were the final architectures selected? HOT 4
- Why use the training dataset in the test stage? HOT 3
- How can I preserve the search architecture? HOT 6
- evolutionary search in a single gpu HOT 4
- The AdaptiveLossSoft become NAN HOT 4
- Can Adaptive-KD use with additional attentive sampling at the same time ? HOT 3
- Re-training code is available? HOT 2
- Training accuracy suddenly approaches zero HOT 6
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