Comments (3)
Hi @izmailovpavel!
Thank you so much for taking the time to get back to me! I considered both the native Pytorch implementation and the Lightning one. But because the rest of my code uses some usefull callbacks in Torchbearer, I was trying to avoid a full change. I wrote the Torchbearer guys and one of them taught me how to pass the native Pytorch swa as a callback to my Torchbearer implementation. I'm testing it now but I'm hopeful.
P.S. I loved reading your paper on SWA.
Thank you so much again!
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Hi @AnabetsyR, sorry for the late reply!
I have never used Torchbearer, from their github page it seems like it is no longer maintained, and the successor is pytorch lightning. Lightning actually has its own implementation of SWA.
Generally, you may also want to try using the native pytorch implementation of SWA, but I don't know how easy it is to use it with Torchbearer.
from swa.
Happy to hear that you liked the paper :)
Sounds great, I hope that solution works out!
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Related Issues (20)
- About bn_update HOT 5
- About conv_init(m)
- CUDA out of memory HOT 1
- Unable to re-produce SGD numbers HOT 7
- [ Question ] Isn't the mid-training evaluation of the SWA performance corrupting the batchnorm running averages ? HOT 4
- SWA compared to Exponential Moving Average (EMA) HOT 4
- Codes for figure 1,3,4, and 5 HOT 1
- Isn't the weight learnt from each minibatchs are supposed to be averaged? HOT 5
- release code for training imagenet HOT 1
- Finetuning model with SWA HOT 1
- How many weights instances stored in memory simultaneously? HOT 1
- SWA with distributed training HOT 1
- Figure 3 plot HOT 1
- About preresnet results HOT 2
- How to reproduce result of Fig.1 in the paper? HOT 5
- Can we use Adam or other optimizer instead of SGD to train the network? HOT 3
- About calculating parameters of BN HOT 1
- performance drop due to batch norm params recalculation HOT 1
- How about sgdr? HOT 1
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